The 10 Algorithms every Machine Learning Engineer should know

The 10 Algorithms every Machine Learning Engineer should know

This article introduces you to the top 10 algorithms that every machine learning engineer must know. ...

This article introduces you to the top 10 algorithms that every machine learning engineer must know. ...

Computers are able to see, hear and learn. Welcome to the future.

And Machine Learning is the future. According to Forbes, Machine learning patents grew at a 34% Rate between 2013 and 2017 and this is only set to increase in coming times. Moreover, a Harvard Business review article called a Data Scientist as the “Sexiest Job of the 21st Century” (And that’s incentive right there!!!).

In these highly dynamic times, there are various machine learning algorithms developed to solve complex real-world problems. These algorithms are highly automated and self-modifying as they continue to improve over time with the addition of an increased amount of data and with minimum human intervention required. So this article deals with the Top 10 Machine Learning algorithms.

But to understand these algorithms, first, the different types they can belong to are explained briefly.

Types of Machine Learning Algorithms –

Machine Learning algorithms can be classified into 3 different types, namely:

Supervised Machine Learning Algorithms:

Imagine a teacher supervising a class. The teacher already knows the correct answers but the learning process doesn’t stop until the students learn the answers as well (poor kids!). This is the essence of Supervised Machine Learning Algorithms. Here, the algorithm is the student that learns from a training dataset and makes predictions that are corrected by the teacher. This learning process continues until the algorithm achieves the required level of performance.

Unsupervised Machine Learning Algorithms:

In this case, there is no teacher for the class and the poor students are left to learn for themselves! This means that for Unsupervised Machine Learning Algorithms, there is no specific answer to be learned and there is no teacher. The algorithm is left unsupervised to find the underlying structure in the data in order to learn more and more about the data itself.

**Reinforcement Machine Learning Algorithms: **

Well, here are hypothetical students learn from their own mistakes over time (that’s like life!). So the Reinforcement Machine Learning Algorithms learn optimal actions through trial and error. This means that the algorithm decides the next action by learning behaviors that are based on its current state and that will maximize the reward in the future.

Top Machine Learning Algorithms

There are specific machine learning algorithms that were developed to handle complex real-world data problems. So, now that we have seen the types of machine learning algorithms, let’s study the top machine learning algorithms that exist and are actually used by data scientists.

1. Naïve Bayes Classifier Algorithm –

What would happen if you had to classify data texts such as a web page, a document or an email manually? Well, you would go mad! But thankfully this task is performed by the Naïve Bayes Classifier Algorithm. This algorithm is based on the Bayes Theorem of Probability(you probably read that in maths) and it allocates the element value to a population from one of the categories that are available.

where, y is class variable and X is a dependent feature vector (of size n) where:

An example of the Naïve Bayes Classifier Algorithm usage is for Email Spam Filtering. Gmail uses this algorithm to classify an email as Spam or Not Spam.

2. K Means Clustering Algorithm –

Let’s imagine that you want to search the term “date” on Wikipedia. Now, “date” can refer to a fruit, a particular day or even a romantic evening with your love!!! So Wikipedia groups the web pages that talk about the same ideas using the K Means Clustering Algorithm (since it is a popular algorithm for cluster analysis).

K Means Clustering Algorithm in general uses K number of clusters to operate on a given data set. In this manner, the output contains K clusters with the input data partitioned among the clusters(As pages with different “date” meanings were partitioned).

3. Support Vector Machine Algorithm –

The Support Vector Machine Algorithm is used for classification or regression problems. In this, the data is divided into different classes by finding a particular line (hyperplane) which separates the data set into multiple classes. The Support Vector Machine Algorithm tries to find the hyperplane that maximizes the distance between the classes (known as margin maximization) as this increases the probability of classifying the data more accurately.

An example of the Support Vector Machine Algorithm usage is for comparison of stock performance for stocks in the same sector. This helps in managing investment making decisions by the financial institutions.

4. Apriori Algorithm –

The Apriori Algorithm generates association rules using the IF_THEN format. This means that IF event A occurs, then event B also occurs with a certain probability. For example: IF a person buys a car, THEN they also buy car insurance. The Apriori Algorithm generates this association rule by observing the number of people who bought car insurance after buying a car.

An example of the Apriori Algorithm usage is for Google auto-complete. When a word is typed in Google, the Apriori Algorithm looks for the associated words that are usually typed after that word and displays the possibilities.

5. Linear Regression Algorithm –

The Linear Regression Algorithm shows the relationship between an independent and a dependent variable. It demonstrates the impact on the dependent variable when the independent variable is changed in any way. So the independent variable is called the explanatory variable and the dependent variable is called the factor of interest.

An example of the Linear Regression Algorithm usage is for risk assessment in the insurance domain. Linear Regression analysis can be used to find the number of claims for customers of multiple ages and then deduce the increased risk as to the age of the customer increases.

6. Logistic Regression Algorithm –

The Logistic Regression Algorithm deals in discrete values whereas the Linear Regression Algorithm handles predictions in continuous values. So, Logistic Regression is suited for binary classification wherein if an event occurs, it is classified as 1 and if not, it is classified as 0. Hence, the probability of a particular event occurrence is predicted based on the given predictor variables.

An example of the Logistic Regression Algorithm usage is in politics to predict if a particular candidate will win or lose a political election.

7. Decision Trees Algorithm –

Suppose that you want to decide the venue for your birthday. So there are many questions that factor in your decision such as “Is the restaurant Italian?”, “Does the restaurant have live music?”, “Is the restaurant close to your house?” etc. Each of these questions has a YES or NO answer that contributes to your decision.

This is what basically happens in the Decision Trees Algorithm. Here all possible outcomes of a decision are shown using a tree branching methodology. The internal nodes are tests on various attributes, the branches of the tree are the outcomes of the tests and the leaf nodes are the decision made after computing all of the attributes.

An example of the Decision Trees Algorithm usage is in the banking industry to classify loan applicants by their probability of defaulting said loan payments.

8. Random Forests Algorithm –

The Random Forests Algorithm handles some of the limitations of Decision Trees Algorithm, namely that the accuracy of the outcome decreases when the number of decisions in the tree increases.

So, in the Random Forests Algorithm, there are multiple decision trees that represent various statistical probabilities. All of these trees are mapped to a single tree known as the CART model. (Classification and Regression Trees). In the end, the final prediction for the Random Forests Algorithm is obtained by polling the results of all the decision trees.

An example of the Random Forests Algorithm usage is in the automobile industry to predict the future breakdown of any particular automobile part.

9. K Nearest Neighbours Algorithm –

The K Nearest Neighbours Algorithm divides the data points into different classes based on a similar measure such as the distance function. Then a prediction is made for a new data point by searching through the entire data set for the K most similar instances (the neighbors) and summarizing the output variable for these K instances. For regression problems, this might be the mean of the outcomes and for classification problems, this might be the mode (most frequent class).

The K Nearest Neighbours Algorithm can require a lot of memory or space to store all of the data, but only performs a calculation (or learns) when a prediction is needed, just in time.

10. Artificial Neural Networks Algorithm –

The human brain contains neurons that are the basis of our retentive power and sharp wit(At least for some of us!) So the Artificial Neural Networks try to replicate the neurons in the human brain by creating nodes that are interconnected to each other. These neurons take in information through another neuron, perform various actions as required and then transfer the information to another neuron as output.

An example of Artificial Neural Networks is Human facial recognition. Images with human faces can be identified and differentiated from “non-facial” images. However, this could take multiple hours depending on the number of images in the database whereas the human mind can do this instantly.

Machine Learning Full Course - Learn Machine Learning

Machine Learning Full Course - Learn Machine Learning

This complete Machine Learning full course video covers all the topics that you need to know to become a master in the field of Machine Learning.

Machine Learning Full Course | Learn Machine Learning | Machine Learning Tutorial

It covers all the basics of Machine Learning (01:46), the different types of Machine Learning (18:32), and the various applications of Machine Learning used in different industries (04:54:48).This video will help you learn different Machine Learning algorithms in Python. Linear Regression, Logistic Regression (23:38), K Means Clustering (01:26:20), Decision Tree (02:15:15), and Support Vector Machines (03:48:31) are some of the important algorithms you will understand with a hands-on demo. Finally, you will see the essential skills required to become a Machine Learning Engineer (04:59:46) and come across a few important Machine Learning interview questions (05:09:03). Now, let's get started with Machine Learning.

Below topics are explained in this Machine Learning course for beginners:

  1. Basics of Machine Learning - 01:46

  2. Why Machine Learning - 09:18

  3. What is Machine Learning - 13:25

  4. Types of Machine Learning - 18:32

  5. Supervised Learning - 18:44

  6. Reinforcement Learning - 21:06

  7. Supervised VS Unsupervised - 22:26

  8. Linear Regression - 23:38

  9. Introduction to Machine Learning - 25:08

  10. Application of Linear Regression - 26:40

  11. Understanding Linear Regression - 27:19

  12. Regression Equation - 28:00

  13. Multiple Linear Regression - 35:57

  14. Logistic Regression - 55:45

  15. What is Logistic Regression - 56:04

  16. What is Linear Regression - 59:35

  17. Comparing Linear & Logistic Regression - 01:05:28

  18. What is K-Means Clustering - 01:26:20

  19. How does K-Means Clustering work - 01:38:00

  20. What is Decision Tree - 02:15:15

  21. How does Decision Tree work - 02:25:15 

  22. Random Forest Tutorial - 02:39:56

  23. Why Random Forest - 02:41:52

  24. What is Random Forest - 02:43:21

  25. How does Decision Tree work- 02:52:02

  26. K-Nearest Neighbors Algorithm Tutorial - 03:22:02

  27. Why KNN - 03:24:11

  28. What is KNN - 03:24:24

  29. How do we choose 'K' - 03:25:38

  30. When do we use KNN - 03:27:37

  31. Applications of Support Vector Machine - 03:48:31

  32. Why Support Vector Machine - 03:48:55

  33. What Support Vector Machine - 03:50:34

  34. Advantages of Support Vector Machine - 03:54:54

  35. What is Naive Bayes - 04:13:06

  36. Where is Naive Bayes used - 04:17:45

  37. Top 10 Application of Machine Learning - 04:54:48

  38. How to become a Machine Learning Engineer - 04:59:46

  39. Machine Learning Interview Questions - 05:09:03

Machine Learning Tutorial - Learn Machine Learning - Intellipaat

Machine Learning Tutorial - Learn Machine Learning - Intellipaat

This Machine Learning tutorial for beginners will enable you to learn Machine Learning algorithms with python examples. Become a pro in Machine Learning.

Mastering the Machine Learning Course would easily develop one's career. This is the reason why studying Machine Learning Tutorial becomes so important in the career of a particular student.
Making a part of the machine learning course would enact and studying the Machine Learning Tutorial would make one carve out a new niche.

Top Machine Learning Framework: 5 Machine Learning Frameworks of 2019

Top Machine Learning Framework: 5 Machine Learning Frameworks of 2019

Machine Learning (ML) is one of the fastest-growing technologies today. ML has a lot of frameworks to build a successful app, and so as a developer, you might be getting confused about using the right framework. Herein we have curated top 5...

Machine Learning (ML) is one of the fastest-growing technologies today. ML has a lot of frameworks to build a successful app, and so as a developer, you might be getting confused about using the right framework. Herein we have curated top 5 machine learning frameworks that are cutting edge technology in your hands.

Through the machine learning frameworks, mobile phones and tablets are getting powerful enough to run the software that can learn and react in real-time. It is a complex discipline. But the implementation of ML models is far less daunting and difficult than it used to be. Now, it automatically improves the performance with the pace of time, interactions, and experiences, and the most important acquisition of useful data pertaining to the tasks allocated.

As we know that ML is considered as a subset of Artificial Intelligence (AI). The scientific study of statistical models and algorithms help a computing system to accomplish designated tasks efficiently. Now, as a mobile app developer, when you are planning to choose machine learning frameworks you must keep the following things in mind.

The framework should be performance-oriented
The grasping and coding should be quick
It allows to distribute the computational process, the framework must have parallelization
It should consist of a facility to create models and provide a developer-friendly tool
Let’s learn about the top five machine learning frameworks to make the right choice for your next ML application development project. Before we dive deeper into these mentioned frameworks, know the different types of ML frameworks that are available on the web. Here are some ML frameworks:

Mathematical oriented
Neural networks-based
Linear algebra tools
Statistical tools
Now, let’s have an insight into ML frameworks that will help you in selecting the right framework for your ML application.

Don’t Miss Out on These 5 Machine Learning Frameworks of 2019
#1 TensorFlow
TensorFlow is an open-source software library for data-based programming across multiple tasks. The framework is based on computational graphs which is essentially a network of codes. Each node represents a mathematical operation that runs some function as simple or as complex as multivariate analysis. This framework is said to be best among all the ML libraries as it supports regressions, classifications, and neural networks like complicated tasks and algorithms.

machine learning frameworks
This machine learning library demands additional efforts while learning TensorFlow Python framework. Your job becomes easy in the n-dimensional array of the framework when you have grasped the Python frameworks and libraries.

The benefits of this framework are flexibility. TensorFlow allows non-automatic migration to newer versions. It runs on the GPU, CPU, servers, desktops, and mobile devices. It provides auto differentiation and performance. There are a few goliaths like Airbus, Twitter, IBM, who have innovatively used the TensorFlow frameworks.

#2 FireBase ML Kit
Firebase machine learning framework is a library that allows effortless, minimal code, with highly accurate, pre-trained deep models. We at Space-O Technologies use this machine learning technology for image classification and object detection. The Firebase framework offers models both locally and on the Google Cloud.

machine learning frameworks
This is one of our ML tutorials to make you understand the Firebase frameworks. First of all, we collected photos of empty glass, half watered glass, full watered glass, and targeted into the machine learning algorithms. This helped the machine to search and analyze according to the nature, behavior, and patterns of the object placed in front of it.

The first photo that we targeted through machine learning algorithms was to recognize an empty glass. Thus, the app did its analysis and search for the correct answer, we provided it with certain empty glass images prior to the experiment.
The other photo that we targeted was a half water glass. The core of the machine learning app is to assemble data and to manage it as per its analysis. It was able to recognize the image accurately because of the little bits and pieces of the glass given to it beforehand.
The last one is a full glass recognition image.
Note: For correct recognition, there has to be 1 label that carries at least 100 images of a particular object.

#3 CAFFE (Convolutional Architecture for Fast Feature Embedding)
CAFFE framework is the fastest way to apply deep neural networks. It is the best machine learning framework known for its model-Zoo a pre-trained ML model that is capable of performing a great variety of tasks. Image classification, machine vision, recommender system are some of the tasks performed easily through this ML library.

machine learning frameworks
This framework is majorly written in CPP. It can run on multiple hardware and can switch between CPU and GPU with the use of a single flag. It has systematically organized the structure of Mat lab and python interface.

Now, if you have to make a machine learning app development, then it is mainly used in academic research projects and to design startups prototypes. It is the aptest machine learning technology for research experiments and industry deployment. At a time this framework can manage 60 million pictures every day with a solitary Nvidia K40 GPU.

#4 Apache Spark
The Apache Spark machine learning is a cluster-computing framework written in different languages like Java, Scala, R, and Python. Spark’s machine learning library, MLlib is considered as foundational for the Spark’s success. Building MLlib on top of Spark makes it possible to tackle the distinct needs of a single tool instead of many disjointed ones.

machine learning frameworks
The advantages of such ML library lower learning curves, less complex development and production environments, which ultimately results in a shorter time to deliver high-performing models. The key benefit of MLlib is that it allows data scientists to solve multiple data problems in addition to their machine learning problems.

It can easily solve graph computations (via GraphX), streaming (real-time calculations), and real-time interactive query processing with Spark SQL and DataFrames. The data professionals can focus on solving the data problems instead of learning and maintaining a different tool for each scenario.

#5 Scikit-Learn
Scikit-learn is said to be one of the greatest feats of Python community. This machine learning framework efficiently handles data mining and supports multiple practical tasks. It is built on foundations like SciPy, Numpy, and matplotlib. This framework is known for supervised & unsupervised learning algorithms as well as cross-validation. The Scikit learn is largely written in Python with some core algorithms in Cython to achieve performance.

machine learning frameworks
The machine learning framework can work on multiple tasks without compromising on speed. There are some remarkable machine learning apps using this framework like Spotify, Evernote, AWeber, Inria.

With the help of machine learning to build iOS apps, Android apps powered by ML have become quite an easy process. With this emerging technology trend varieties of available data, computational processing has become cheaper and more powerful, and affordable data storage. So being an app developer or having an idea for machine learning apps should definitely dive into the niche.

Conclusion
Still have any query or confusion regarding ML frameworks, machine learning app development guide, the difference between Artificial Intelligence and machine learning, ML algorithms from scratch, how this technology is helpful for your business? Just fill our contact us form. Our sales representatives will get back to you shortly and resolve your queries. The consultation is absolutely free of cost.

Author Bio: This blog is written with the help of Jigar Mistry, who has over 13 years of experience in the web and mobile app development industry. He has guided to develop over 200 mobile apps and has special expertise in different mobile app categories like Uber like apps, Health and Fitness apps, On-Demand apps and Machine Learning apps. So, we took his help to write this complete guide on machine learning technology and machine app development areas.