Apache Configuration Error AH00558: Could not reliably determine

This tutorial series explains how to troubleshoot and fix some of the most common errors that you may encounter when using the Apache web server.

Each tutorial in this series includes descriptions of common Apache configuration, network, filesystem, or permission errors. The series begins with an overview of the commands and log files that you can use to troubleshoot Apache. Subsequent tutorials examine specific errors in detail.

How To Troubleshoot Common Apache Errors

Series Description

This tutorial series explains how to troubleshoot and fix some of the most common errors that you may encounter when using the Apache web server.

Each tutorial in this series includes descriptions of common Apache configuration, network, filesystem, or permission errors. The series begins with an overview of the commands and log files that you can use to troubleshoot Apache. Subsequent tutorials examine specific errors in detail.

How To Troubleshoot Common Apache Errors

Introduction

There are three main commands, and a set of common log locations that you can use to get started troubleshooting Apache errors. Generally when you are troubleshooting Apache, you will use these commands in the order indicated here, and then examine log files for specific diagnostic data.

The commands that you will commonly use to troubleshoot Apache across most Linux distributions are:

• systemctl - Used to control and interact with Linux services via the systemd service manager.
• journalctl - Used to query and view the logs that are generated by systemd.
• apachectl - When troubleshooting, this command is used to check Apache’s configuration.

These commands, how to use them, and Apache’s log locations where you can find additional information about errors are described in further detail in the following sections.

Note : On Debian and Ubuntu systems, the Apache service and process name is apache2 , whereas on CentOS, Fedora, and other RedHat-derived systems, Apache’s service and process name is httpd . Apart from the differences between the service and running process names, starting, stopping, and checking Apache’s status, as well as logs with journalctl  should work the same on any Linux system that uses systemd  to manage the Apache service. Be sure to use the correct name for your Linux distribution.

#error ah00558 #apache #apache errors

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Apache Configuration Error AH00558: Could not reliably determine

This tutorial series explains how to troubleshoot and fix some of the most common errors that you may encounter when using the Apache web server.

Each tutorial in this series includes descriptions of common Apache configuration, network, filesystem, or permission errors. The series begins with an overview of the commands and log files that you can use to troubleshoot Apache. Subsequent tutorials examine specific errors in detail.

How To Troubleshoot Common Apache Errors

Series Description

This tutorial series explains how to troubleshoot and fix some of the most common errors that you may encounter when using the Apache web server.

Each tutorial in this series includes descriptions of common Apache configuration, network, filesystem, or permission errors. The series begins with an overview of the commands and log files that you can use to troubleshoot Apache. Subsequent tutorials examine specific errors in detail.

How To Troubleshoot Common Apache Errors

Introduction

There are three main commands, and a set of common log locations that you can use to get started troubleshooting Apache errors. Generally when you are troubleshooting Apache, you will use these commands in the order indicated here, and then examine log files for specific diagnostic data.

The commands that you will commonly use to troubleshoot Apache across most Linux distributions are:

• systemctl - Used to control and interact with Linux services via the systemd service manager.
• journalctl - Used to query and view the logs that are generated by systemd.
• apachectl - When troubleshooting, this command is used to check Apache’s configuration.

These commands, how to use them, and Apache’s log locations where you can find additional information about errors are described in further detail in the following sections.

Note : On Debian and Ubuntu systems, the Apache service and process name is apache2 , whereas on CentOS, Fedora, and other RedHat-derived systems, Apache’s service and process name is httpd . Apart from the differences between the service and running process names, starting, stopping, and checking Apache’s status, as well as logs with journalctl  should work the same on any Linux system that uses systemd  to manage the Apache service. Be sure to use the correct name for your Linux distribution.

#error ah00558 #apache #apache errors

Background Fetch for React Native Apps

react-native-background-fetch

Background Fetch is a very simple plugin which attempts to awaken an app in the background about every 15 minutes, providing a short period of background running-time. This plugin will execute your provided callbackFn whenever a background-fetch event occurs.

There is no way to increase the rate which a fetch-event occurs and this plugin sets the rate to the most frequent possible — you will never receive an event faster than 15 minutes. The operating-system will automatically throttle the rate the background-fetch events occur based upon usage patterns. Eg: if user hasn't turned on their phone for a long period of time, fetch events will occur less frequently or if an iOS user disables background refresh they may not happen at all.

:new: Background Fetch now provides a scheduleTask method for scheduling arbitrary "one-shot" or periodic tasks.

iOS

• There is no way to increase the rate which a fetch-event occurs and this plugin sets the rate to the most frequent possible — you will never receive an event faster than 15 minutes. The operating-system will automatically throttle the rate the background-fetch events occur based upon usage patterns. Eg: if user hasn't turned on their phone for a long period of time, fetch events will occur less frequently.
• scheduleTask seems only to fire when the device is plugged into power.
• ⚠️ When your app is terminated, iOS no longer fires events — There is no such thing as stopOnTerminate: false for iOS.
• iOS can take days before Apple's machine-learning algorithm settles in and begins regularly firing events. Do not sit staring at your logs waiting for an event to fire. If your simulated events work, that's all you need to know that everything is correctly configured.
• If the user doesn't open your iOS app for long periods of time, iOS will stop firing events.

Android

• The Android plugin provides a HeadlessJS implementation allowing you to continue handling events even after app-termination (see @config enableHeadless)

Installing the plugin

⚠️ If you have a previous version of react-native-background-fetch < 2.7.0 installed into react-native >= 0.60, you should first unlink your previous version as react-native link is no longer required.

$ react-native unlink react-native-background-fetch

With yarn

$ yarn add react-native-background-fetch

With npm

$ npm install --save react-native-background-fetch

Setup Guides

iOS Setup

react-native >= 0.60

• Auto-linking Setup

Android Setup

react-native >= 0.60

• Auto-linking Setup

Example

ℹ️ This repo contains its own Example App. See /example

import React from 'react';
import {
  SafeAreaView,
  StyleSheet,
  ScrollView,
  View,
  Text,
  FlatList,
  StatusBar,
} from 'react-native';

import {
  Header,
  Colors
} from 'react-native/Libraries/NewAppScreen';

import BackgroundFetch from "react-native-background-fetch";

class App extends React.Component {
  constructor(props) {
    super(props);
    this.state = {
      events: []
    };
  }

  componentDidMount() {
    // Initialize BackgroundFetch ONLY ONCE when component mounts.
    this.initBackgroundFetch();
  }

  async initBackgroundFetch() {
    // BackgroundFetch event handler.
    const onEvent = async (taskId) => {
      console.log('[BackgroundFetch] task: ', taskId);
      // Do your background work...
      await this.addEvent(taskId);
      // IMPORTANT:  You must signal to the OS that your task is complete.
      BackgroundFetch.finish(taskId);
    }

    // Timeout callback is executed when your Task has exceeded its allowed running-time.
    // You must stop what you're doing immediately BackgroundFetch.finish(taskId)
    const onTimeout = async (taskId) => {
      console.warn('[BackgroundFetch] TIMEOUT task: ', taskId);
      BackgroundFetch.finish(taskId);
    }

    // Initialize BackgroundFetch only once when component mounts.
    let status = await BackgroundFetch.configure({minimumFetchInterval: 15}, onEvent, onTimeout);

    console.log('[BackgroundFetch] configure status: ', status);
  }

  // Add a BackgroundFetch event to <FlatList>
  addEvent(taskId) {
    // Simulate a possibly long-running asynchronous task with a Promise.
    return new Promise((resolve, reject) => {
      this.setState(state => ({
        events: [...state.events, {
          taskId: taskId,
          timestamp: (new Date()).toString()
        }]
      }));
      resolve();
    });
  }

  render() {
    return (
      <>
        <StatusBar barStyle="dark-content" />
        <SafeAreaView>
          <ScrollView
            contentInsetAdjustmentBehavior="automatic"
            style={styles.scrollView}>
            <Header />

            <View style={styles.body}>
              <View style={styles.sectionContainer}>
                <Text style={styles.sectionTitle}>BackgroundFetch Demo</Text>
              </View>
            </View>
          </ScrollView>
          <View style={styles.sectionContainer}>
            <FlatList
              data={this.state.events}
              renderItem={({item}) => (<Text>[{item.taskId}]: {item.timestamp}</Text>)}
              keyExtractor={item => item.timestamp}
            />
          </View>
        </SafeAreaView>
      </>
    );
  }
}

const styles = StyleSheet.create({
  scrollView: {
    backgroundColor: Colors.lighter,
  },
  body: {
    backgroundColor: Colors.white,
  },
  sectionContainer: {
    marginTop: 32,
    paddingHorizontal: 24,
  },
  sectionTitle: {
    fontSize: 24,
    fontWeight: '600',
    color: Colors.black,
  },
  sectionDescription: {
    marginTop: 8,
    fontSize: 18,
    fontWeight: '400',
    color: Colors.dark,
  },
});

export default App;

Executing Custom Tasks

In addition to the default background-fetch task defined by BackgroundFetch.configure, you may also execute your own arbitrary "oneshot" or periodic tasks (iOS requires additional Setup Instructions). However, all events will be fired into the Callback provided to BackgroundFetch#configure:

⚠️ iOS:

• scheduleTask on iOS seems only to run when the device is plugged into power.
• scheduleTask on iOS are designed for low-priority tasks, such as purging cache files — they tend to be unreliable for mission-critical tasks. scheduleTask will never run as frequently as you want.
• The default fetch event is much more reliable and fires far more often.
• scheduleTask on iOS stop when the user terminates the app. There is no such thing as stopOnTerminate: false for iOS.

// Step 1:  Configure BackgroundFetch as usual.
let status = await BackgroundFetch.configure({
  minimumFetchInterval: 15
}, async (taskId) => {  // <-- Event callback
  // This is the fetch-event callback.
  console.log("[BackgroundFetch] taskId: ", taskId);

  // Use a switch statement to route task-handling.
  switch (taskId) {
    case 'com.foo.customtask':
      print("Received custom task");
      break;
    default:
      print("Default fetch task");
  }
  // Finish, providing received taskId.
  BackgroundFetch.finish(taskId);
}, async (taskId) => {  // <-- Task timeout callback
  // This task has exceeded its allowed running-time.
  // You must stop what you're doing and immediately .finish(taskId)
  BackgroundFetch.finish(taskId);
});

// Step 2:  Schedule a custom "oneshot" task "com.foo.customtask" to execute 5000ms from now.
BackgroundFetch.scheduleTask({
  taskId: "com.foo.customtask",
  forceAlarmManager: true,
  delay: 5000  // <-- milliseconds
});

API Documentation

Config

Common Options

@param {Integer} minimumFetchInterval [15]

The minimum interval in minutes to execute background fetch events. Defaults to 15 minutes. Note: Background-fetch events will never occur at a frequency higher than every 15 minutes. Apple uses a secret algorithm to adjust the frequency of fetch events, presumably based upon usage patterns of the app. Fetch events can occur less often than your configured minimumFetchInterval.

@param {Integer} delay (milliseconds)

ℹ️ Valid only for BackgroundFetch.scheduleTask. The minimum number of milliseconds in future that task should execute.

@param {Boolean} periodic [false]

ℹ️ Valid only for BackgroundFetch.scheduleTask. Defaults to false. Set true to execute the task repeatedly. When false, the task will execute just once.

Android Options

@config {Boolean} stopOnTerminate [true]

Set false to continue background-fetch events after user terminates the app. Default to true.

@config {Boolean} startOnBoot [false]

Set true to initiate background-fetch events when the device is rebooted. Defaults to false.

❗ NOTE: startOnBoot requires stopOnTerminate: false.

@config {Boolean} forceAlarmManager [false]

By default, the plugin will use Android's JobScheduler when possible. The JobScheduler API prioritizes for battery-life, throttling task-execution based upon device usage and battery level.

Configuring forceAlarmManager: true will bypass JobScheduler to use Android's older AlarmManager API, resulting in more accurate task-execution at the cost of higher battery usage.

let status = await BackgroundFetch.configure({
  minimumFetchInterval: 15,
  forceAlarmManager: true
}, async (taskId) => {  // <-- Event callback
  console.log("[BackgroundFetch] taskId: ", taskId);
  BackgroundFetch.finish(taskId);
}, async (taskId) => {  // <-- Task timeout callback
  // This task has exceeded its allowed running-time.
  // You must stop what you're doing and immediately .finish(taskId)
  BackgroundFetch.finish(taskId);
});
.
.
.
// And with with #scheduleTask
BackgroundFetch.scheduleTask({
  taskId: 'com.foo.customtask',
  delay: 5000,       // milliseconds
  forceAlarmManager: true,
  periodic: false
});

@config {Boolean} enableHeadless [false]

Set true to enable React Native's Headless JS mechanism, for handling fetch events after app termination.

• 📂 index.js (MUST BE IN index.js):

import BackgroundFetch from "react-native-background-fetch";

let MyHeadlessTask = async (event) => {
  // Get task id from event {}:
  let taskId = event.taskId;
  let isTimeout = event.timeout;  // <-- true when your background-time has expired.
  if (isTimeout) {
    // This task has exceeded its allowed running-time.
    // You must stop what you're doing immediately finish(taskId)
    console.log('[BackgroundFetch] Headless TIMEOUT:', taskId);
    BackgroundFetch.finish(taskId);
    return;
  }
  console.log('[BackgroundFetch HeadlessTask] start: ', taskId);

  // Perform an example HTTP request.
  // Important:  await asychronous tasks when using HeadlessJS.
  let response = await fetch('https://reactnative.dev/movies.json');
  let responseJson = await response.json();
  console.log('[BackgroundFetch HeadlessTask] response: ', responseJson);

  // Required:  Signal to native code that your task is complete.
  // If you don't do this, your app could be terminated and/or assigned
  // battery-blame for consuming too much time in background.
  BackgroundFetch.finish(taskId);
}

// Register your BackgroundFetch HeadlessTask
BackgroundFetch.registerHeadlessTask(MyHeadlessTask);

@config {integer} requiredNetworkType [BackgroundFetch.NETWORK_TYPE_NONE]

Set basic description of the kind of network your job requires.

If your job doesn't need a network connection, you don't need to use this option as the default value is BackgroundFetch.NETWORK_TYPE_NONE.

NetworkType	Description
BackgroundFetch.NETWORK_TYPE_NONE	This job doesn't care about network constraints, either any or none.
BackgroundFetch.NETWORK_TYPE_ANY	This job requires network connectivity.
BackgroundFetch.NETWORK_TYPE_CELLULAR	This job requires network connectivity that is a cellular network.
BackgroundFetch.NETWORK_TYPE_UNMETERED	This job requires network connectivity that is unmetered. Most WiFi networks are unmetered, as in "you can upload as much as you like".
BackgroundFetch.NETWORK_TYPE_NOT_ROAMING	This job requires network connectivity that is not roaming (being outside the country of origin)

@config {Boolean} requiresBatteryNotLow [false]

Specify that to run this job, the device's battery level must not be low.

This defaults to false. If true, the job will only run when the battery level is not low, which is generally the point where the user is given a "low battery" warning.

@config {Boolean} requiresStorageNotLow [false]

Specify that to run this job, the device's available storage must not be low.

This defaults to false. If true, the job will only run when the device is not in a low storage state, which is generally the point where the user is given a "low storage" warning.

@config {Boolean} requiresCharging [false]

Specify that to run this job, the device must be charging (or be a non-battery-powered device connected to permanent power, such as Android TV devices). This defaults to false.

@config {Boolean} requiresDeviceIdle [false]

When set true, ensure that this job will not run if the device is in active use.

The default state is false: that is, the for the job to be runnable even when someone is interacting with the device.

This state is a loose definition provided by the system. In general, it means that the device is not currently being used interactively, and has not been in use for some time. As such, it is a good time to perform resource heavy jobs. Bear in mind that battery usage will still be attributed to your application, and shown to the user in battery stats.

---

Methods

Method Name	Arguments	Returns	Notes
configure	{FetchConfig}, callbackFn, timeoutFn	Promise<BackgroundFetchStatus>	Configures the plugin's callbackFn and timeoutFn. This callback will fire each time a background-fetch event occurs in addition to events from #scheduleTask. The timeoutFn will be called when the OS reports your task is nearing the end of its allowed background-time.
scheduleTask	{TaskConfig}	Promise<boolean>	Executes a custom task. The task will be executed in the same Callback function provided to #configure.
status	callbackFn	Promise<BackgroundFetchStatus>	Your callback will be executed with the current status (Integer) 0: Restricted, 1: Denied, 2: Available. These constants are defined as BackgroundFetch.STATUS_RESTRICTED, BackgroundFetch.STATUS_DENIED, BackgroundFetch.STATUS_AVAILABLE (NOTE: Android will always return STATUS_AVAILABLE)
finish	String taskId	Void	You MUST call this method in your callbackFn provided to #configure in order to signal to the OS that your task is complete. iOS provides only 30s of background-time for a fetch-event -- if you exceed this 30s, iOS will kill your app.
start	none	Promise<BackgroundFetchStatus>	Start the background-fetch API. Your callbackFn provided to #configure will be executed each time a background-fetch event occurs. NOTE the #configure method automatically calls #start. You do not have to call this method after you #configure the plugin
stop	[taskId:String]	Promise<boolean>	Stop the background-fetch API and all #scheduleTask from firing events. Your callbackFn provided to #configure will no longer be executed. If you provide an optional taskId, only that #scheduleTask will be stopped.

Debugging

iOS

🆕 BGTaskScheduler API for iOS 13+

• ⚠️ At the time of writing, the new task simulator does not yet work in Simulator; Only real devices.
• See Apple docs Starting and Terminating Tasks During Development
• After running your app in XCode, Click the [||] button to initiate a Breakpoint.
• In the console (lldb), paste the following command (Note: use cursor up/down keys to cycle through previously run commands):

e -l objc -- (void)[[BGTaskScheduler sharedScheduler] _simulateLaunchForTaskWithIdentifier:@"com.transistorsoft.fetch"]

• Click the [ > ] button to continue. The task will execute and the Callback function provided to BackgroundFetch.configure will receive the event.

Simulating task-timeout events

• Only the new BGTaskScheduler api supports simulated task-timeout events. To simulate a task-timeout, your fetchCallback must not call BackgroundFetch.finish(taskId):

let status = await BackgroundFetch.configure({
  minimumFetchInterval: 15
}, async (taskId) => {  // <-- Event callback.
  // This is the task callback.
  console.log("[BackgroundFetch] taskId", taskId);
  //BackgroundFetch.finish(taskId); // <-- Disable .finish(taskId) when simulating an iOS task timeout
}, async (taskId) => {  // <-- Event timeout callback
  // This task has exceeded its allowed running-time.
  // You must stop what you're doing and immediately .finish(taskId)
  print("[BackgroundFetch] TIMEOUT taskId:", taskId);
  BackgroundFetch.finish(taskId);
});

• Now simulate an iOS task timeout as follows, in the same manner as simulating an event above:

e -l objc -- (void)[[BGTaskScheduler sharedScheduler] _simulateExpirationForTaskWithIdentifier:@"com.transistorsoft.fetch"]

Old BackgroundFetch API

• Simulate background fetch events in XCode using Debug->Simulate Background Fetch
• iOS can take some hours or even days to start a consistently scheduling background-fetch events since iOS schedules fetch events based upon the user's patterns of activity. If Simulate Background Fetch works, your can be sure that everything is working fine. You just need to wait.

Android

• Observe plugin logs in $ adb logcat:

$ adb logcat *:S ReactNative:V ReactNativeJS:V TSBackgroundFetch:V

• Simulate a background-fetch event on a device (insert <your.application.id>) (only works for sdk 21+:

$ adb shell cmd jobscheduler run -f <your.application.id> 999

• For devices with sdk <21, simulate a "Headless JS" event with (insert <your.application.id>)

$ adb shell am broadcast -a <your.application.id>.event.BACKGROUND_FETCH

Download Details:
Author: transistorsoft
Source Code: https://github.com/transistorsoft/react-native-background-fetch
License: MIT license

#react  #reactnative  #mobileapp  #javascript 

Apache Configuration Error AH02572: Failed to configure

Introduction

Apache generates an AH02572: Failed to configure at least one certificate and key error message when it is configured to use the ssl module, but is missing a TLS/SSL public certificate and corresponding private key. The error will prevent Apache from starting up, and the error message itself will be found in Apache’s logs.

In this tutorial you will learn how to troubleshoot an AH02572 error using the methods described in the How to Troubleshoot Common Apache Errors tutorial at the beginning of this series. You will also learn how to set the SSLCertificateFile and SSLCertificateKeyFile directives to resolve the message.

If you have already determined that your Apache server is affected by an AH02572 error and you would like to skip the troubleshooting steps, the Adding an SSL Certificate to Apache section at the end of this tutorial explains how to resolve the error.

Troubleshooting Using systemctl

When you are troubleshooting an AH02572: Failed to configure at least one certificate and key error message, Apache will not be running. Its systemctl status will show a failed message.

To examine Apache’s status with systemctl, run the following command on Ubuntu and Debian derived Linux distributions:

Ubuntu and Debian Systems

sudo systemctl status apache2.service -l --no-pager

Copy

On CentOS and Fedora systems, use this command to examine Apache’s status:

CentOS and Fedora Systems

sudo systemctl status httpd.service -l --no-pager

Copy

The -l flag will ensure that systemctl outputs the entire contents of a line, instead of substituting in ellipses (…) for long lines. The --no-pager flag will output the entire log to your screen without invoking a tool like less that only shows a screen of content at a time.

You should receive output that is similar to the following:

#ah02572 #apache #common apache errors

Machine Learning Tutorial: Step By Step for Beginners

In this Machine Learning article, we learn about Machine Learning Tutorial: step by step for beginners. This Machine Learning tutorial provides both intermediate and basics of machine learning. It is designed for students and working professionals who are complete beginners. At the end of this tutorial, you will be able to make machine learning models that can perform complex tasks such as predicting the price of a house or recognizing the species of an Iris from the dimensions of its petal and sepal lengths. If you are not a complete beginner and are a bit familiar with Machine Learning, I would suggest starting with subtopic eight i.e, Types of Machine Learning.

Before we deep dive further, if you are keen to explore a course in Artificial Intelligence & Machine Learning do check out our Artificial Intelligence Courses available at Great Learning. Anyone could expect an average Salary Hike of 48% from this course. Participate in Great Learning’s career accelerate programs and placement drives and get hired by our pool of 500+ Hiring companies through our programs.

Before jumping into the tutorial, you should be familiar with Pandas and NumPy. This is important to understand the implementation part. There are no prerequisites for understanding the theory. Here are the subtopics that we are going to discuss in this tutorial:

What is Machine Learning?

Arthur Samuel coined the term Machine Learning in the year 1959. He was a pioneer in Artificial Intelligence and computer gaming, and defined Machine Learning as “Field of study that gives computers the capability to learn without being explicitly programmed”.

In simple terms, Machine Learning is an application of Artificial Intelligence (AI) which enables a program(software) to learn from the experiences and improve their self at a task without being explicitly programmed. For example, how would you write a program that can identify fruits based on their various properties, such as colour, shape, size or any other property?

One approach is to hardcode everything, make some rules and use them to identify the fruits. This may seem the only way and work but one can never make perfect rules that apply on all cases. This problem can be easily solved using machine learning without any rules which makes it more robust and practical. You will see how we will use machine learning to do this task in the coming sections.

Thus, we can say that Machine Learning is the study of making machines more human-like in their behaviour and decision making by giving them the ability to learn with minimum human intervention, i.e., no explicit programming. Now the question arises, how can a program attain any experience and from where does it learn? The answer is data. Data is also called the fuel for Machine Learning and we can safely say that there is no machine learning without data.

You may be wondering that the term Machine Learning has been introduced in 1959 which is a long way back, then why haven’t there been any mention of it till recent years? You may want to note that Machine Learning needs a huge computational power, a lot of data and devices which are capable of storing such vast data. We have only recently reached a point where we now have all these requirements and can practice Machine Learning.

How is it different from traditional programming?

Are you wondering how is Machine Learning different from traditional programming? Well, in traditional programming, we would feed the input data and a well written and tested program into a machine to generate output. When it comes to machine learning, input data along with the output associated with the data is fed into the machine during the learning phase, and it works out a program for itself.

Why do we need Machine Learning?

Machine Learning today has all the attention it needs. Machine Learning can automate many tasks, especially the ones that only humans can perform with their innate intelligence. Replicating this intelligence to machines can be achieved only with the help of machine learning. 

With the help of Machine Learning, businesses can automate routine tasks. It also helps in automating and quickly create models for data analysis. Various industries depend on vast quantities of data to optimize their operations and make intelligent decisions. Machine Learning helps in creating models that can process and analyze large amounts of complex data to deliver accurate results. These models are precise and scalable and function with less turnaround time. By building such precise Machine Learning models, businesses can leverage profitable opportunities and avoid unknown risks.

Image recognition, text generation, and many other use-cases are finding applications in the real world. This is increasing the scope for machine learning experts to shine as a sought after professionals. 

How Does Machine Learning Work?

A machine learning model learns from the historical data fed to it and then builds prediction algorithms to predict the output for the new set of data the comes in as input to the system. The accuracy of these models would depend on the quality and amount of input data. A large amount of data will help build a better model which predicts the output more accurately.

Suppose we have a complex problem at hand that requires to perform some predictions. Now, instead of writing a code, this problem could be solved by feeding the given data to generic machine learning algorithms. With the help of these algorithms, the machine will develop logic and predict the output. Machine learning has transformed the way we approach business and social problems. Below is a diagram that briefly explains the working of a machine learning model/ algorithm. our way of thinking about the problem.

History of Machine Learning

Nowadays, we can see some amazing applications of ML such as in self-driving cars, Natural Language Processing and many more. But Machine learning has been here for over 70 years now. It all started in 1943, when neurophysiologist Warren McCulloch and mathematician Walter Pitts wrote a paper about neurons, and how they work. They decided to create a model of this using an electrical circuit, and therefore, the neural network was born.

In 1950, Alan Turing created the “Turing Test” to determine if a computer has real intelligence. To pass the test, a computer must be able to fool a human into believing it is also human. In 1952, Arthur Samuel wrote the first computer learning program. The program was the game of checkers, and the IBM computer improved at the game the more it played, studying which moves made up winning strategies and incorporating those moves into its program.

Just after a few years, in 1957, Frank Rosenblatt designed the first neural network for computers (the perceptron), which simulates the thought processes of the human brain. Later, in 1967, the “nearest neighbor” algorithm was written, allowing computers to begin using very basic pattern recognition. This could be used to map a route for travelling salesmen, starting at a random city but ensuring they visit all cities during a short tour.

But we can say that in the 1990s we saw a big change. Now work on machine learning shifted from a knowledge-driven approach to a data-driven approach.  Scientists began to create programs for computers to analyze large amounts of data and draw conclusions or “learn” from the results.

In 1997, IBM’s Deep Blue became the first computer chess-playing system to beat a reigning world chess champion. Deep Blue used the computing power in the 1990s to perform large-scale searches of potential moves and select the best move. Just a decade before this, in 2006, Geoffrey Hinton created the term “deep learning” to explain new algorithms that help computers distinguish objects and text in images and videos.

Machine Learning at Present

The year 2012 saw the publication of an influential research paper by Alex Krizhevsky, Geoffrey Hinton, and Ilya Sutskever, describing a model that can dramatically reduce the error rate in image recognition systems. Meanwhile, Google’s X Lab developed a machine learning algorithm capable of autonomously browsing YouTube videos to identify the videos that contain cats. In 2016 AlphaGo (created by researchers at Google DeepMind to play the ancient Chinese game of Go) won four out of five matches against Lee Sedol, who has been the world’s top Go player for over a decade.

And now in 2020, OpenAI released GPT-3 which is the most powerful language model ever. It can write creative fiction, generate functioning code, compose thoughtful business memos and much more. Its possible use cases are limited only by our imaginations.

Features of Machine Learning

1. Automation: Nowadays in your Gmail account, there is a spam folder that contains all the spam emails. You might be wondering how does Gmail know that all these emails are spam? This is the work of Machine Learning. It recognizes the spam emails and thus, it is easy to automate this process. The ability to automate repetitive tasks is one of the biggest characteristics of machine learning. A huge number of organizations are already using machine learning-powered paperwork and email automation. In the financial sector, for example, a huge number of repetitive, data-heavy and predictable tasks are needed to be performed. Because of this, this sector uses different types of machine learning solutions to a great extent.

2. Improved customer experience: For any business, one of the most crucial ways to drive engagement, promote brand loyalty and establish long-lasting customer relationships is by providing a customized experience and providing better services. Machine Learning helps us to achieve both of them. Have you ever noticed that whenever you open any shopping site or see any ads on the internet, they are mostly about something that you recently searched for? This is because machine learning has enabled us to make amazing recommendation systems that are accurate. They help us customize the user experience. Now coming to the service, most of the companies nowadays have a chatting bot with them that are available 24×7. An example of this is Eva from AirAsia airlines. These bots provide intelligent answers and sometimes you might even not notice that you are having a conversation with a bot. These bots use Machine Learning, which helps them to provide a good user experience.

3. Automated data visualization: In the past, we have seen a huge amount of data being generated by companies and individuals. Take an example of companies like Google, Twitter, Facebook. How much data are they generating per day? We can use this data and visualize the notable relationships, thus giving businesses the ability to make better decisions that can actually benefit both companies as well as customers. With the help of user-friendly automated data visualization platforms such as AutoViz, businesses can obtain a wealth of new insights in an effort to increase productivity in their processes.

4. Business intelligence: Machine learning characteristics, when merged with big data analytics can help companies to find solutions to the problems that can help the businesses to grow and generate more profit. From retail to financial services to healthcare, and many more, ML has already become one of the most effective technologies to boost business operations.

Python provides flexibility in choosing between object-oriented programming or scripting. There is also no need to recompile the code; developers can implement any changes and instantly see the results. You can use Python along with other languages to achieve the desired functionality and results.

Python is a versatile programming language and can run on any platform including Windows, MacOS, Linux, Unix, and others. While migrating from one platform to another, the code needs some minor adaptations and changes, and it is ready to work on the new platform. To build strong foundation and cover basic concepts you can enroll in a python machine learning course that will help you power ahead your career.

Here is a summary of the benefits of using Python for Machine Learning problems:

Types of Machine Learning

Machine learning has been broadly categorized into three categories

1. Supervised Learning
2. Unsupervised Learning
3. Reinforcement Learning

What is Supervised Learning?

Let us start with an easy example, say you are teaching a kid to differentiate dogs from cats. How would you do it? 

You may show him/her a dog and say “here is a dog” and when you encounter a cat you would point it out as a cat. When you show the kid enough dogs and cats, he may learn to differentiate between them. If he is trained well, he may be able to recognize different breeds of dogs which he hasn’t even seen. 

Similarly, in Supervised Learning, we have two sets of variables. One is called the target variable, or labels (the variable we want to predict) and features(variables that help us to predict target variables). We show the program(model) the features and the label associated with these features and then the program is able to find the underlying pattern in the data. Take this example of the dataset where we want to predict the price of the house given its size. The price which is a target variable depends upon the size which is a feature.

Number of rooms	Price
1	$100
3	$300
5	$500

In a real dataset, we will have a lot more rows and more than one features like size, location, number of floors and many more.

Thus, we can say that the supervised learning model has a set of input variables (x), and an output variable (y). An algorithm identifies the mapping function between the input and output variables. The relationship is y = f(x).

The learning is monitored or supervised in the sense that we already know the output and the algorithm are corrected each time to optimize its results. The algorithm is trained over the data set and amended until it achieves an acceptable level of performance.

We can group the supervised learning problems as:

Regression problems – Used to predict future values and the model is trained with the historical data. E.g., Predicting the future price of a house.

Classification problems – Various labels train the algorithm to identify items within a specific category. E.g., Dog or cat( as mentioned in the above example), Apple or an orange, Beer or wine or water.

What is Unsupervised Learning?

This approach is the one where we have no target variables, and we have only the input variable(features) at hand. The algorithm learns by itself and discovers an impressive structure in the data. 

The goal is to decipher the underlying distribution in the data to gain more knowledge about the data. 

We can group the unsupervised learning problems as:

Clustering: This means bundling the input variables with the same characteristics together. E.g., grouping users based on search history

Association: Here, we discover the rules that govern meaningful associations among the data set. E.g., People who watch ‘X’ will also watch ‘Y’.

What is Reinforcement Learning?

In this approach, machine learning models are trained to make a series of decisions based on the rewards and feedback they receive for their actions. The machine learns to achieve a goal in complex and uncertain situations and is rewarded each time it achieves it during the learning period. 

Reinforcement learning is different from supervised learning in the sense that there is no answer available, so the reinforcement agent decides the steps to perform a task. The machine learns from its own experiences when there is no training data set present.

In this tutorial, we are going to mainly focus on Supervised Learning and Unsupervised learning as these are quite easy to understand and implement.

Machine learning Algorithms

This may be the most time-consuming and difficult process in your journey of Machine Learning. There are many algorithms in Machine Learning and you don’t need to know them all in order to get started. But I would suggest, once you start practising Machine Learning, start learning about the most popular algorithms out there such as:

• Linear Regression
• Logistic Regression
• Decision Tree
• SVM
• Naive Bayes
• K-nearest neighbour
• K-Means
• Random Forest
• Gradient Boosting algorithms
  • GBM
  • XGBoost
  • LightGBM
  • CatBoost

Here, I am going to give a brief overview of one of the simplest algorithms in Machine learning, the K-nearest neighbor Algorithm (which is a Supervised learning algorithm) and show how we can use it for Regression as well as for classification. I would highly recommend checking the Linear Regression and Logistic Regression as we are going to implement them and compare the results with KNN(K-nearest neighbor) algorithm in the implementation part.

You may want to note that there are usually separate algorithms for regression problems and classification problems. But by modifying an algorithm, we can use it for both classifications as well as regression as you will see below

K-Nearest Neighbor Algorithm

KNN belongs to a group of lazy learners. As opposed to eager learners such as logistic regression, SVM, neural nets, lazy learners just store the training data in memory. During the training phase, KNN arranges the data (sort of indexing process) in order to find the closest neighbours efficiently during the inference phase. Otherwise, it would have to compare each new case during inference with the whole dataset making it quite inefficient.

So if you are wondering what is a training phase, eager learners and lazy learners, for now just remember that training phase is when an algorithm learns from the data provided to it. For example, if you have gone through the Linear Regression algorithm linked above, during the training phase the algorithm tries to find the best fit line which is a process that includes a lot of computations and hence takes a lot of time and this type of algorithm is called eager learners. On the other hand, lazy learners are just like KNN which do not involve many computations and hence train faster.

K-NN for Classification Problem

Now let us see how we can use K-NN for classification. Here a hypothetical dataset which tries to predict if a person is male or female (labels) on the base of the height and weight (features).

Height(cm) -feature	Weight(kg) -feature.	Gender(label)
187	80	Male
165	50	Female
199	99	Male
145	70	Female
180	87	Male
178	65	Female
187	60	Male

Now let us plot these points:

Now we have a new point that we want to classify, given that its height is 190 cm and weight is 100 Kg. Here is how K-NN will classify this point:

1. Select the value of K, which the user selects which he thinks will be best after analysing the data.
2. Measure the distance of new points from its nearest K number of points. There are various methods for calculating this distance, of which the most commonly known methods are – Euclidian, Manhattan (for continuous data points i.e regression problems) and Hamming distance (for categorical i.e for classification problems).
3. Identify the class of the points that are more closer to the new point and label the new point accordingly. So if the majority of points closer to our new point belong to a certain “a” class than our new point is predicted to be from class “a”.

Now let us apply this algorithm to our own dataset. Let us first plot the new data point.

Now let us take k=3 i.e, we will see the three closest points to the new point:

Therefore, it is classified as Male:

Now let us take the value of k=5 and see what happens:

As we can see four of the points closest to our new data point are males and just one point is female, so we go with the majority and classify it as Male again. You must always select the value of K as an odd number when doing classification.

K-NN for a Regression problem

We have seen how we can use K-NN for classification. Now, let us see what changes are made to use it for regression. The algorithm is almost the same there is just one difference. In Classification, we checked for the majority of all nearest points. Here, we are going to take the average of all the nearest points and take that as predicted value. Let us again take the same example but here we have to predict the weight(label) of a person given his height(features).

Height(cm) -feature	Weight(kg) -label
187	80
165	50
199	99
145	70
180	87
178	65
187	60

Now we have new data point with a height of 160cm, we will predict its weight by taking the values of K as 1,2 and 4.

When K=1: The closest point to 160cm in our data is 165cm which has a weight of 50, so we conclude that the predicted weight is 50 itself.

When K=2: The two closest points are 165 and 145 which have weights equal to 50 and 70 respectively. Taking average we say that the predicted weight is (50+70)/2=60.

When K=4: Repeating the same process, now we take 4 closest points instead and hence we get 70.6 as predicted weight.

You might be thinking that this is really simple and there is nothing so special about Machine learning, it is just basic Mathematics. But remember this is the simplest algorithm and you will see much more complex algorithms once you move ahead in this journey.

At this stage, you must have a vague idea of how machine learning works, don’t worry if you are still confused. Also if you want to go a bit deep now, here is an excellent article – Gradient Descent in Machine Learning, which discusses how we use an optimization technique called as gradient descent to find a best-fit line in linear regression.

How To Choose Machine Learning Algorithm?

There are plenty of machine learning algorithms and it could be a tough task to decide which algorithm to choose for a specific application. The choice of the algorithm will depend on the objective of the problem you are trying to solve.

Let us take an example of a task to predict the type of fruit among three varieties, i.e., apple, banana, and orange. The predictions are based on the colour of the fruit. The picture depicts the results of ten different algorithms. The picture on the top left is the dataset. The data is classified into three categories: red, light blue and dark blue. There are some groupings. For instance, from the second image, everything in the upper left belongs to the red category, in the middle part, there is a mixture of uncertainty and light blue while the bottom corresponds to the dark category. The other images show different algorithms and how they try to classified the data.

Steps in Machine Learning

I wish Machine learning was just applying algorithms on your data and get the predicted values but it is not that simple. There are several steps in Machine Learning which are must for each project.

1. Gathering Data: This is perhaps the most important and time-consuming process. In this step, we need to collect data that can help us to solve our problem. For example, if you want to predict the prices of the houses, we need an appropriate dataset that contains all the information about past house sales and then form a tabular structure. We are going to solve a similar problem in the implementation part.
2. Preparing that data: Once we have the data, we need to bring it in proper format and preprocess it. There are various steps involved in pre-processing such as data cleaning, for example, if your dataset has some empty values or abnormal values(e.g, a string instead of a number) how are you going to deal with it? There are various ways in which we can but one simple way is to just drop the rows that have empty values. Also sometimes in the dataset, we might have columns that have no impact on our results such as id’s, we remove those columns as well. We usually use Data Visualization to visualize our data through graphs and diagrams and after analyzing the graphs, we decide which features are important. Data preprocessing is a vast topic and I would suggest checking out this article to know more about it.
3. Choosing a model: Now our data is ready is to be fed into a Machine Learning algorithm. In case you are wondering what is a Model? Often “machine learning algorithm” is used interchangeably with “machine learning model.” A model is the output of a machine learning algorithm run on data. In simple terms when we implement the algorithm on all our data, we get an output which contains all the rules, numbers, and any other algorithm-specific data structures required to make predictions. For example, after implementing Linear Regression on our data we get an equation of the best fit line and this equation is termed as a model. The next step is usually training the model incase we don’t want to tune hyperparameters and select the default ones.
4. Hyperparameter Tuning: Hyperparameters are crucial as they control the overall behavior of a machine learning model. The ultimate goal is to find an optimal combination of hyperparameters that gives us the best results. But what are these hyper-parameters? Remember the variable K in our K-NN algorithm. We got different results when we set different values of K. The best value for K is not predefined and is different for different datasets. There is no method to know the best value for K, but you can try different values and check for which value do we get the best results. Here K is a hyperparameter and each algorithm has its own hyperparameters and we need to tune their values to get the best results. To get more information about it, check out this article – Hyperparameter Tuning Explained.
5. Evaluation: You may be wondering, how can you know if the model is performing good or bad. What better way than testing the model on some data. This data is known as testing data and it must not be a subset of the data (training data) on which we trained the algorithm. The objective of training the model is not for it to learn all the values in the training dataset but to identify the underlying pattern in data and based on that make predictions on data it has never seen before. There are various evaluation methods such as K-fold cross-validation and many more. We are going to discuss this step in detail in the coming section.
6. Prediction: Now that our model has performed well on the testing set as well, we can use it in real-world and hope it is going to perform well on real-world data.

Evaluation of Machine learning Model

For evaluating the model, we hold out a portion of data called test data and do not use this data to train the model. Later, we use test data to evaluate various metrics.

The results of predictive models can be viewed in various forms such as by using confusion matrix, root-mean-squared error(RMSE), AUC-ROC etc.

TP (True Positive) is the number of values predicted to be positive by the algorithm and was actually positive in the dataset. TN represents the number of values that are expected to not belong to the positive class and actually do not belong to it. FP depicts the number of instances misclassified as belonging to the positive class thus is actually part of the negative class. FN shows the number of instances classified as the negative class but should belong to the positive class. 

Now in Regression problem, we usually use RMSE as evaluation metrics. In this evaluation technique, we use the error term.

Let’s say you feed a model some input X and the model predicts 10, but the actual value is 5. This difference between your prediction (10) and the actual observation (5) is the error term: (f_prediction – i_actual). The formula to calculate RMSE is given by:

Where N is a total number of samples for which we are calculating RMSE.

In a good model, the RMSE should be as low as possible and there should not be much difference between RMSE calculated over training data and RMSE calculated over the testing set. 

Python for Machine Learning

Although there are many languages that can be used for machine learning, according to me, Python is hands down the best programming language for Machine Learning applications. This is due to the various benefits mentioned in the section below. Other programming languages that could to use for Machine Learning Applications are R, C++, JavaScript, Java, C#, Julia, Shell, TypeScript, and Scala. R is also a really good language to get started with machine learning.

Python is famous for its readability and relatively lower complexity as compared to other programming languages. Machine Learning applications involve complex concepts like calculus and linear algebra which take a lot of effort and time to implement. Python helps in reducing this burden with quick implementation for the Machine Learning engineer to validate an idea. You can check out the Python Tutorial to get a basic understanding of the language. Another benefit of using Python in Machine Learning is the pre-built libraries. There are different packages for a different type of applications, as mentioned below:

1. Numpy, OpenCV, and Scikit are used when working with images
2. NLTK along with Numpy and Scikit again when working with text
3. Librosa for audio applications
4. Matplotlib, Seaborn, and Scikit for data representation
5. TensorFlow and Pytorch for Deep Learning applications
6. Scipy for Scientific Computing
7. Django for integrating web applications
8. Pandas for high-level data structures and analysis

Implementation of algorithms in Machine Learning with Python

Before moving on to the implementation of machine learning with Python part, you need to download some important software and libraries. Anaconda is an open-source distribution that makes it easy to perform Python/R data science and machine learning on a single machine. It contains all most all the libraries that are needed by us. In this tutorial, we are mostly going to use the scikit-learn library which is a free software machine learning library for the Python programming language.

Now, we are going to implement all that we learnt till now. We will solve a Regression problem and then a Classification problem using the seven steps mentioned above.

Implementation of a Regression problem

We have a problem of predicting the prices of the house given some features such as size, number of rooms and many more. So let us get started:

1. Gathering data: We don’t need to manually collect the data for past sales of houses. Luckily there are some good people who do it for us and make these datasets available for us to use. Also let me mention not all datasets are free but for you to practice, you will find most of the datasets free to use on the internet.

The dataset we are using is called the Boston Housing dataset. Each record in the database describes a Boston suburb or town. The data was drawn from the Boston Standard Metropolitan Statistical Area (SMSA) in 1970. The attributes are defined as follows (taken from the UCI Machine Learning Repository).

1. CRIM: per capita crime rate by town
2. ZN: proportion of residential land zoned for lots over 25,000 sq.ft.
3. INDUS: proportion of non-retail business acres per town
4. CHAS: Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)
5. NOX: nitric oxides concentration (parts per 10 million)
6. RM: average number of rooms per dwelling
7. AGE: the proportion of owner-occupied units built prior to 1940
8. DIS: weighted distances to five Boston employment centers
9. RAD: index of accessibility to radial highways
10. TAX: full-value property-tax rate per $10,000
11. PTRATIO: pupil-teacher ratio by town 
12. B: 1000(Bk−0.63)2 where Bk is the proportion of blacks by town 
13. LSTAT: % lower status of the population
14. MEDV: Median value of owner-occupied homes in $1000s

Here is a link to download this dataset.

Now after opening the file you can see the data about House sales. This dataset is not in a proper tabular form, in fact, there are no column names and each value is separated by spaces. We are going to use Pandas to put it in proper tabular form. We will provide it with a list containing column names and also use delimiter as ‘\s+’ which means that after encounterings a single or multiple spaces, it can differentiate every single entry.

We are going to import all the necessary libraries such as Pandas and NumPy. Next, we will import the data file which is in CSV format into a pandas DataFrame.

import numpy as np
import pandas as pd
column_names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX','PTRATIO', 'B', 'LSTAT', 'MEDV']
bos1 = pd.read_csv('housing.csv', delimiter=r"\s+", names=column_names)

2. Preprocess Data: The next step is to pre-process the data. Now for this dataset, we can see that there are no NaN (missing) values and also all the data is in numbers rather than strings so we won’t face any errors when training the model. So let us just divide our data into training data and testing data such that 70% of data is training data and the rest is testing data. We could also scale our data to make the predictions much accurate but for now, let us keep it simple.

bos1.isna().sum()

from sklearn.model_selection import train_test_split
X=np.array(bos1.iloc[:,0:13])
Y=np.array(bos1["MEDV"])
#testing data size is of 30% of entire data
x_train, x_test, y_train, y_test =train_test_split(X,Y, test_size = 0.30, random_state =5)

3. Choose a Model: For this particular problem, we are going to use two algorithms of supervised learning that can solve regression problems and later compare their results. One algorithm is K-NN (K-nearest Neighbor) which is explained above and the other is Linear Regression. I would highly recommend to check it out in case you haven’t already.

from sklearn.linear_model import LinearRegression
from sklearn.neighbors import KNeighborsRegressor
#load our first model 
lr = LinearRegression()
#train the model on training data
lr.fit(x_train,y_train)
#predict the testing data so that we can later evaluate the model
pred_lr = lr.predict(x_test)
#load the second model
Nn=KNeighborsRegressor(3)
Nn.fit(x_train,y_train)
pred_Nn = Nn.predict(x_test)

4. Hyperparameter Tuning: Since this is a beginners tutorial, here, I am only going to turn the value ok K in the K-NN model. I will just use a for loop and check results of k ranging from 1 to 50. K-NN is extremely fast on small dataset like ours so it won’t take any time. There are much more advanced methods of doing this which you can find linked in the steps of Machine Learning section above.

import sklearn
for i in range(1,50):
    model=KNeighborsRegressor(i)
    model.fit(x_train,y_train)
    pred_y = model.predict(x_test)
    mse = sklearn.metrics.mean_squared_error(y_test, pred_y,squared=False)
    print("{} error for k = {}".format(mse,i))

Output:

From the output, we can see that error is least for k=3, so that should justify why I put the value of K=3 while training the model

5. Evaluating the model: For evaluating the model we are going to use the mean_squared_error() method from the scikit-learn library. Remember to set the parameter ‘squared’ as False, to get the RMSE error.

#error for linear regression
mse_lr= sklearn.metrics.mean_squared_error(y_test, pred_lr,squared=False)
print("error for Linear Regression = {}".format(mse_lr))
#error for linear regression
mse_Nn= sklearn.metrics.mean_squared_error(y_test, pred_Nn,squared=False)
print("error for K-NN = {}".format(mse_Nn))

Now from the results, we can conclude that Linear Regression performs better than K-NN for this particular dataset. But It is not necessary that Linear Regression would always perform better than K-NN as it completely depends upon the data that we are working with.

6. Prediction: Now we can use the models to predict the prices of the houses using the predict function as we did above. Make sure when predicting the prices that we are given all the features that were present when training the model.

Here is the whole script:

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.neighbors import KNeighborsRegressor
column_names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']
bos1 = pd.read_csv('housing.csv', delimiter=r"\s+", names=column_names)
X=np.array(bos1.iloc[:,0:13])
Y=np.array(bos1["MEDV"])
#testing data size is of 30% of entire data
x_train, x_test, y_train, y_test =train_test_split(X,Y, test_size = 0.30, random_state =54)
#load our first model 
lr = LinearRegression()
#train the model on training data
lr.fit(x_train,y_train)
#predict the testing data so that we can later evaluate the model
pred_lr = lr.predict(x_test)
#load the second model
Nn=KNeighborsRegressor(12)
Nn.fit(x_train,y_train)
pred_Nn = Nn.predict(x_test)
#error for linear regression
mse_lr= sklearn.metrics.mean_squared_error(y_test, pred_lr,squared=False)
print("error for Linear Regression = {}".format(mse_lr))
#error for linear regression
mse_Nn= sklearn.metrics.mean_squared_error(y_test, pred_Nn,squared=False)
print("error for K-NN = {}".format(mse_Nn))

Implementation of a Classification problem

In this section, we will solve the population classification problem known as Iris Classification problem. The Iris dataset was used in R.A. Fisher’s classic 1936 paper, The Use of Multiple Measurements in Taxonomic Problems, and can also be found on the UCI Machine Learning Repository.

It includes three iris species with 50 samples each as well as some properties about each flower. One flower species is linearly separable from the other two, but the other two are not linearly separable from each other. The columns in this dataset are:

Different species of iris

• SepalLengthCm
• SepalWidthCm
• PetalLengthCm
• PetalWidthCm
• Species

We don’t need to download this dataset as scikit-learn library already contains this dataset and we can simply import it from there. So let us start coding this up:

from sklearn.datasets import load_iris
iris = load_iris()
X=iris.data
Y=iris.target
print(X)
print(Y)

As we can see, the features are in a list containing four items which are the features and at the bottom, we got a list containing labels which have been transformed into numbers as the model cannot understand names that are strings, so we encode each name as a number. This has already done by the scikit learn developers.

from sklearn.model_selection import train_test_split
#testing data size is of 30% of entire data
x_train, x_test, y_train, y_test =train_test_split(X,Y, test_size = 0.3, random_state =5)
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
#fitting our model to train and test
Nn = KNeighborsClassifier(8)
Nn.fit(x_train,y_train)
#the score() method calculates the accuracy of model.
print("Accuracy for K-NN is ",Nn.score(x_test,y_test))
Lr = LogisticRegression()
Lr.fit(x_train,y_train)
print("Accuracy for Logistic Regression is ",Lr.score(x_test,y_test))

Advantages of Machine Learning

1. Easily identifies trends and patterns

Machine Learning can review large volumes of data and discover specific trends and patterns that would not be apparent to humans. For instance, for e-commerce websites like Amazon and Flipkart, it serves to understand the browsing behaviors and purchase histories of its users to help cater to the right products, deals, and reminders relevant to them. It uses the results to reveal relevant advertisements to them.

2. Continuous Improvement

We are continuously generating new data and when we provide this data to the Machine Learning model which helps it to upgrade with time and increase its performance and accuracy. We can say it is like gaining experience as they keep improving in accuracy and efficiency. This lets them make better decisions.

3. Handling multidimensional and multi-variety data

Machine Learning algorithms are good at handling data that are multidimensional and multi-variety, and they can do this in dynamic or uncertain environments.

4. Wide Applications

You could be an e-tailer or a healthcare provider and make Machine Learning work for you. Where it does apply, it holds the capability to help deliver a much more personal experience to customers while also targeting the right customers.

Disadvantages of Machine Learning

1. Data Acquisition

Machine Learning requires a massive amount of data sets to train on, and these should be inclusive/unbiased, and of good quality. There can also be times where we must wait for new data to be generated.

2. Time and Resources

Machine Learning needs enough time to let the algorithms learn and develop enough to fulfill their purpose with a considerable amount of accuracy and relevancy. It also needs massive resources to function. This can mean additional requirements of computer power for you.

3. Interpretation of Results

Another major challenge is the ability to accurately interpret results generated by the algorithms. You must also carefully choose the algorithms for your purpose. Sometimes, based on some analysis you might select an algorithm but it is not necessary that this model is best for the problem.

4. High error-susceptibility

Machine Learning is autonomous but highly susceptible to errors. Suppose you train an algorithm with data sets small enough to not be inclusive. You end up with biased predictions coming from a biased training set. This leads to irrelevant advertisements being displayed to customers. In the case of Machine Learning, such blunders can set off a chain of errors that can go undetected for long periods of time. And when they do get noticed, it takes quite some time to recognize the source of the issue, and even longer to correct it.

Future of Machine Learning

Machine Learning can be a competitive advantage to any company, be it a top MNC or a startup. As things that are currently being done manually will be done tomorrow by machines. With the introduction of projects such as self-driving cars, Sophia(a humanoid robot developed by Hong Kong-based company Hanson Robotics) we have already started a glimpse of what the future can be. The Machine Learning revolution will stay with us for long and so will be the future of Machine Learning.

Machine Learning Tutorial FAQs

How do I start learning Machine Learning?

You first need to start with the basics. You need to understand the prerequisites, which include learning Linear Algebra and Multivariate Calculus, Statistics, and Python. Then you need to learn several ML concepts, which include terminology of Machine Learning, types of Machine Learning, and Resources of Machine Learning. The third step is taking part in competitions. You can also take up a free online statistics for machine learning course and understand the foundational concepts.

Is Machine Learning easy for beginners? 

Machine Learning is not the easiest. The difficulty in learning Machine Learning is the debugging problem. However, if you study the right resources, you will be able to learn Machine Learning without any hassles.

What is a simple example of Machine Learning? 

Recommendation Engines (Netflix); Sorting, tagging and categorizing photos (Yelp); Customer Lifetime Value (Asos); Self-Driving Cars (Waymo); Education (Duolingo); Determining Credit Worthiness (Deserve); Patient Sickness Predictions (KenSci); and Targeted Emails (Optimail).

Can I learn Machine Learning in 3 months? 

Machine Learning is vast and consists of several things. Therefore, it will take you around six months to learn it, provided you spend at least 5-6 days every day. Also, the time taken to learn Machine Learning depends a lot on your mathematical and analytical skills.

Does Machine Learning require coding? 

If you are learning traditional Machine Learning, it would require you to know software programming as it will help you to write machine learning algorithms. However, through some online educational platforms, you do not need to know coding to learn Machine Learning.

Is Machine Learning a good career? 

Machine Learning is one of the best careers at present. Whether it is for the current demand, job, and salary growth, Machine Learning Engineer is one of the best profiles. You need to be very good at data, automation, and algorithms.

Can I learn Machine Learning without Python? 

To learn Machine Learning, you need to have some basic knowledge of Python. A version of Python that is supported by all Operating Systems such as Windows, Linux, etc., is Anaconda. It offers an overall package for machine learning, including matplotlib, scikit-learn, and NumPy.

Where can I practice Machine Learning? 

The online platforms where you can practice Machine Learning include CloudXLab, Google Colab, Kaggle, MachineHack, and OpenML.

Where can I learn Machine Learning for free?

You can learn the basics of Machine Learning from online platforms like Great Learning. You can enroll in the Beginners Machine Learning course and get the certificate for free. The course is easy and perfect for beginners to start with.

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Original article source at: https://www.mygreatlearning.com

#machine-learning 

Immediate $olution to Fix AOL Blerk Error Code 5 with easy instructions

AOL Email is one of the leading web email services. It has a number of features who access easily at any place. Through this, you can easily share messages, documents or files, etc.AOL Blerk Error is not a big issue. It is a temporary error and it occurs when there is an issue in loading messages from the AOL server. If your mind is stuck, How to Resolve or Fix AOL Blerk Error Code 5? Here, In this article, we mentioned troubleshooting steps to fix AOL Blerk Error Code 5.

What are the causes of AOL Blerk Error Code 5?

AOL mail usually presents an AOL Blerk Error 5 after the AOL connection details have been entered. meaning. Your password and your username. This error is usually found in words! Or 'BLERK! Error 5 Authentication problem, 'Your sign-in has been received.

Some of the reasons for the error are as follows:
• Internet browser configuration problem

• Saved erroneous bookmark addresses

• browser cache or cookie

• An AOL Desktop Gold technical error.
How to Fix AOL Mail Blerk Error 5 in a Simple Way

This type of error is mostly due to your browser settings or the use of outdated, obsolete software. Users should remember that the steps to solve problems vary, depending on the browser you are using. Here are the steps to fix the mistake, check your browser and follow the steps.

Internet Explorer: Make sure you use the most recent web browser version. Open a new window and follow the “Tools> Web Options> Security> Internet Zone” thread. Activate ‘Safeguard Mode’ and follow the steps to include AOL Mail in the list of assured websites. Start the browser again to save changes and run Internet Explorer without additional information.
Firefox Mozilla: Open a new Firefox window and press Menu. To start the browser in safe mode, disable the add-on and choose the option to restart Firefox. You can see two options in the dialog box. Use the “Start in Safe Mode” option to disable all themes and extensions. The browser also turns off the hardware speed and resets the toolbar. You should be able to execute AOL mail when this happens.

Google Chrome: Update to the latest version of Chrome. Open the browser and go to the Advanced Options section. Go to ‘Security and Privacy’ and close the appropriate add-ons. Once the browsing history is deleted, the password, cookies saved and the cache will be cleared. Restart your system and try to log in to your AOL account with a new window.

Safari: Some pop-up windows block AOL mail when it comes to Safari and causes authentication issues. To fix the error, use Safari Security Preferences to enable the pop-up window and disable the security warning.

If you see, even when you change the required browser settings, the black error will not disappear, you can consult a skilled professional and see all the AOL email customer support numbers.

Get Connect to Fix Blerk Error Even After Clearing Cache & Cookies?
Somehow you can contact AOL technical support directly and get immediate help if you still get the error. Call +1(888)857-5157 to receive assistance from the AOL technical support team.

Source: https://email-expert247.blogspot.com/2021/04/immediate-olution-to-fix-aol-blerk.html “How to Resolve or Fix AOL Blerk Error Code 5”)**? Here, In this article, we mentioned troubleshooting steps to fix AOL Blerk Error Code 5.

What are the causes of AOL Blerk Error Code 5?

AOL mail usually presents an AOL Blerk Error 5 after the AOL connection details have been entered. meaning. Your password and your username. This error is usually found in words! Or 'BLERK! Error 5 Authentication problem, 'Your sign-in has been received.

Some of the reasons for the error are as follows:
• Internet browser configuration problem

• Saved erroneous bookmark addresses

• browser cache or cookie

• An AOL Desktop Gold technical error.
How to Fix AOL Mail Blerk Error 5 in a Simple Way

This type of error is mostly due to your browser settings or the use of outdated, obsolete software. Users should remember that the steps to solve problems vary, depending on the browser you are using. Here are the steps to fix the mistake, check your browser and follow the steps.

1. Internet Explorer: Make sure you use the most recent web browser version. Open a new window and follow the “Tools> Web Options> Security> Internet Zone” thread. Activate ‘Safeguard Mode’ and follow the steps to include AOL Mail in the list of assured websites. Start the browser again to save changes and run Internet Explorer without additional information.

2. Firefox Mozilla: Open a new Firefox window and press Menu. To start the browser in safe mode, disable the add-on and choose the option to restart Firefox. You can see two options in the dialog box. Use the “Start in Safe Mode” option to disable all themes and extensions. The browser also turns off the hardware speed and resets the toolbar. You should be able to execute AOL mail when this happens.

3. Google Chrome: Update to the latest version of Chrome. Open the browser and go to the Advanced Options section. Go to ‘Security and Privacy’ and close the appropriate add-ons. Once the browsing history is deleted, the password, cookies saved and the cache will be cleared. Restart your system and try to log in to your AOL account with a new window.

4. Safari: Some pop-up windows block AOL mail when it comes to Safari and causes authentication issues. To fix the error, use Safari Security Preferences to enable the pop-up window and disable the security warning.

If you see, even when you change the required browser settings, the black error will not disappear, you can consult a skilled professional and see all the AOL email customer support numbers.

Get Connect to Fix Blerk Error Even After Clearing Cache & Cookies?

Somehow you can contact AOL technical support directly and get immediate help if you still get the error. Call +1(888)857-5157 to receive assistance from the AOL technical support team.

Source: https://email-expert247.blogspot.com/2021/04/immediate-olution-to-fix-aol-blerk.html

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