Tyshawn  Braun

Tyshawn Braun

1602788400

Basics of Supervised Learning (Classification)

In this post, we are going to dive into the concepts of Supervised Learning or rather known as Classification in the domain of Machine Learning. We will discuss the definitions, components, examples of classification.

Classification can be defined as the task of learning a target function **f**that maps each attribute set **x**to one of the predefined labels y.

**Example: **Assigning a piece of news to one of the predefined categories.

In the community of Data Science or Machine Learning, anything done on data is called **modelling. **In context of classification, there are two types of modelling:

  1. Descriptive Modelling: A classification model can serve as an explanatory tool to distinguish between objects of different classes. **Example: **A model that defines the type of vertebrae based on its features.
  2. _Predictive Modelling: _A classification model can also be used to predict the class label of unknown records.

Classification techniques are most suited for predicting or describing data sets with binary or nominal categories. They are less effective for ordinal categories (e.g.,to classify a person as a member of high-, medium-, or low- income group) because they do not consider the implicit order among the categories.

#data-science #supervised-learning #computer-science #classification #machine-learning

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Basics of Supervised Learning (Classification)
Tyshawn  Braun

Tyshawn Braun

1602788400

Basics of Supervised Learning (Classification)

In this post, we are going to dive into the concepts of Supervised Learning or rather known as Classification in the domain of Machine Learning. We will discuss the definitions, components, examples of classification.

Classification can be defined as the task of learning a target function **f**that maps each attribute set **x**to one of the predefined labels y.

**Example: **Assigning a piece of news to one of the predefined categories.

In the community of Data Science or Machine Learning, anything done on data is called **modelling. **In context of classification, there are two types of modelling:

  1. Descriptive Modelling: A classification model can serve as an explanatory tool to distinguish between objects of different classes. **Example: **A model that defines the type of vertebrae based on its features.
  2. _Predictive Modelling: _A classification model can also be used to predict the class label of unknown records.

Classification techniques are most suited for predicting or describing data sets with binary or nominal categories. They are less effective for ordinal categories (e.g.,to classify a person as a member of high-, medium-, or low- income group) because they do not consider the implicit order among the categories.

#data-science #supervised-learning #computer-science #classification #machine-learning

Vern  Greenholt

Vern Greenholt

1595019420

Supervised Learning With Scikit-Learn

This is a tutorial to share what I have learnt in Supervised Learning with scikit-learn, capturing the learning objectives as well as my personal notes. The course is taught by Hugo Bowne-Anderson from DataCamp and includes 4 chapters:

Chapter 1. Classification

Chapter 2. Regression

Chapter 3. Fine-tuning your model

Chapter 4. Preprocessing and pipelines

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Is a particular email spam?

Will a tumor be benign or malignant?

Which of your customers will take their business elsewhere?

These questions can be answered by Machine learning algorithms, where computers learn from existing data to make predictions on new data.

In this course, you’ll learn how to use Python to perform supervised learning, an essential component of machine learning. You’ll learn how to build predictive models, tune their parameters, and determine how well they will perform with unseen data — all while using real world datasets. You’ll be using scikit-learn, one of the most popular and user-friendly machine learning libraries for Python.


Chapter 1. Classification

In this chapter, you will be introduced to classification problems and learn how to solve them using supervised learning techniques. And you’ll apply what you learn to a political dataset, where you classify the party affiliation of United States congressmen based on their voting records.

Supervised learning

Supervised learning is giving computers the ability to learn to make decisions from _labelled _data without being explicitly programmed. Example, to predict whether an email is spam or not (classification), or to predict life expectancy (regression).

Features = predictor variables = independent variables

Target variable = dependent variable = response variable

Unsupervised learning is used to uncover hidden patterns by clustering, using _unlabeled _data. Example, to cluster wikipedia entries into different categories, or to group customers into distinct categories based on purchasing behavior.

Reinforcement learning is when machines or software agents interact with an environment. Reinforcement agents optimise their behaviour given a system of rewards and punishments.

Which of these is a classification problem?

Once you decide to leverage supervised machine learning to solve a new problem, you need to identify whether your problem is better suited to classification or regression.

Provided below are 4 example applications of machine learning. Which of them is a supervised classification problem?

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Answer: Using labeled financial data to predict whether the value of a stock will go up or go down next week. There are two discrete, qualitative outcomes: the stock market going up, and the stock market going down. This can be represented using a binary variable, and is an application perfectly suited for classification.

Exploratory data analysis (EDA)

Explore the Iris dataset, explore flower characteristics (4 features) in columns and the 3 target species types. EDA can be visually displayed using pd.plotting.scatter_matrix() function.

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Numerical EDA

In this chapter, you’ll be working with a dataset obtained from the UCI Machine Learning Repository consisting of votes made by US House of Representatives Congressmen. Your goal will be to predict their party affiliation (‘Democrat’ or ‘Republican’) based on how they voted on certain key issues. Here, it’s worth noting that we have preprocessed this dataset to deal with missing values. This is so that your focus can be directed towards understanding how to train and evaluate supervised learning models. Once you have mastered these fundamentals, you will be introduced to preprocessing techniques in Chapter 4 and have the chance to apply them there yourself — including on this very same dataset!

Before thinking about what supervised learning models you can apply to this, however, you need to perform Exploratory data analysis (EDA) in order to understand the structure of the data.

Get started with your EDA now by exploring this voting records dataset numerically. Use pandas’ .head().info(), and .describe() methods on DataFrame df. Select the statement below that is not true.

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Answer: There are 17 predictor variables, or features, in this DataFrame.

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Observation: The number of columns in the DataFrame is not equal to the number of features. One of the columns 'party' is the target variable.

#pipeline #machine-learning #supervised-learning #regression #classification #deep learning

Biju Augustian

Biju Augustian

1574339995

Learn Python Tutorial from Basic to Advance

Description
Become a Python Programmer and learn one of employer’s most requested skills of 21st century!

This is the most comprehensive, yet straight-forward, course for the Python programming language on Simpliv! Whether you have never programmed before, already know basic syntax, or want to learn about the advanced features of Python, this course is for you! In this course we will teach you Python 3. (Note, we also provide older Python 2 notes in case you need them)

With over 40 lectures and more than 3 hours of video this comprehensive course leaves no stone unturned! This course includes tests, and homework assignments as well as 3 major projects to create a Python project portfolio!

This course will teach you Python in a practical manner, with every lecture comes a full coding screencast and a corresponding code notebook! Learn in whatever manner is best for you!

We will start by helping you get Python installed on your computer, regardless of your operating system, whether its Linux, MacOS, or Windows, we’ve got you covered!

We cover a wide variety of topics, including:

Command Line Basics
Installing Python
Running Python Code
Strings
Lists
Dictionaries
Tuples
Sets
Number Data Types
Print Formatting
Functions
Scope
Built-in Functions
Debugging and Error Handling
Modules
External Modules
Object Oriented Programming
Inheritance
Polymorphism
File I/O
Web scrapping
Database Connection
Email sending
and much more!
Project that we will complete:

Guess the number
Guess the word using speech recognition
Love Calculator
google search in python
Image download from a link
Click and save image using openCV
Ludo game dice simulator
open wikipedia on command prompt
Password generator
QR code reader and generator
You will get lifetime access to over 40 lectures.

So what are you waiting for? Learn Python in a way that will advance your career and increase your knowledge, all in a fun and practical way!

Basic knowledge
Basic programming concept in any language will help but not require to attend this tutorial
What will you learn
Learn to use Python professionally, learning both Python 2 and Python 3!
Create games with Python, like Tic Tac Toe and Blackjack!
Learn advanced Python features, like the collections module and how to work with timestamps!
Learn to use Object Oriented Programming with classes!
Understand complex topics, like decorators.
Understand how to use both the pycharm and create .py files
Get an understanding of how to create GUIs in the pycharm!
Build a complete understanding of Python from the ground up!

#Learn Python #Learn Python from Basic #Python from Basic to Advance #Python from Basic to Advance with Projects #Learn Python from Basic to Advance with Projects in a day

Dejah  Reinger

Dejah Reinger

1601344800

Machine Learning | Everything you need to know

Machine learning is enabling computers to tackle tasks that have, until now, only been carried out by people.

From driving cars to translating speech, machine learning is driving an explosion in the capabilities of artificial intelligence— helping software make sense of the messy and unpredictable real world.

But what exactly is machine learning and what is making the current boom in machine learning possible?

#supervised-learning #machine-learning #reinforcement-learning #semi-supervised-learning #unsupervised-learning

Elton  Bogan

Elton Bogan

1604091840

Supervised Learning vs Unsupervised Learning

Note from Towards Data Science’s editors:_ While we allow independent authors to publish articles in accordance with our rules and guidelines, we do not endorse each author’s contribution. You should not rely on an author’s works without seeking professional advice. See our Reader Terms for details._

Nowadays, nearly everything in our lives can be quantified by data. Whether it involves search engine results, social media usage, weather trackers, cars, or sports, data is always being collected to enhance our quality of life. How do we get from all this raw data to improve the level of performance? This article will introduce us to the tools and techniques developed to make sense of unstructured data and discover hidden patterns. Specifically, the main topics that are covered are:

1. Supervised & Unsupervised Learning and the main techniques corresponding to each one (Classification and Clustering, respectively).

2. An in-depth look at the K-Means algorithm

Goals

1. Understanding the many different techniques used to discover patterns in a set of data

2. In-depth understanding of the K-Means algorithm

1.1 Unsupervised and supervised learning

In unsupervised learning, we are trying to discover hidden patterns in data, when we don’t have any labels. We will go through what hidden patterns are and what labels are, and we will go through real data examples.

What is unsupervised learning?

First, let’s step back to what learning even means. In machine learning in statistics, we are typically trying to find hidden patterns in data. Ideally, we want these hidden patterns to help us in some way. For instance, to help us understand some scientific results, to improve our user experience, or to help us maximize profit in some investment. Supervised learning is when we learn from data, but we have labels for all the data we have seen so far. Unsupervised learning is when we learn from data, but we don’t have any labels.

Let’s use an example of an email. In general, it can be hard to keep our inbox in check. We get many e-mails every day and a big problem is spam. In fact, it would be an even bigger problem if e-mail providers, like Gmail, were not so effective at keeping spam out of our inboxes. But how do they know whether a particular e-mail is a spam or not? This is our first example of a machine learning problem.

Every machine learning problem has a data set, which is a collection of data points that help us learn. Your data set will be all the e-mails that are sent over a month. Each data point will be a single e-mail. Whenever you get an e-mail, you can quickly tell whether it’s spam. You might hit a button to label any particular e-mail as spam or not spam. Now you can imagine that each of your data points has one of two labels, spam or not spam. In the future, you will keep getting emails, but you won’t know in advance which label it should have, spam or not spam. The machine learning problem is to predict whether a new label for a new email is spam or not spam. This means that we want to predict the label of the next email. If our machine learning algorithm works, it can put all the spam in a separate folder. This spam problem is an example of supervised learning. You can imagine a teacher, or supervisor, telling you the label of each data point, which is whether each e-mail is spam or not spam. The supervisor might be able to tell us whether the labels we predicted were correct.

So what is unsupervised learning? Let’s try another example of a machine learning problem. Imagine you are looking at your emails, and realize you got too many emails. It would be helpful if you could read all the emails that are on the same topic at the same time. So, you might run a machine learning algorithm that groups together similar emails. After you have run your machine learning algorithm, you find that there are natural groups of emails in your inbox. This is an example of an unsupervised learning problem. You did not have any labels because no labels were made for each email, which means there is no supervisor.

#reinforcement-learning #supervised-learning #unsupervised-learning #k-means-clustering #machine-learning