Machine learning is the education to understand computer algorithms that improve automatically via exposure and experience. It is seen as a subpart of artificial intelligence. Machine learning algorithms build a mathematical model upon sample data known as the training data to make decisions without being explicitly programmed to do so. Machine learning algorithms are used in a huge spectrum of applications, such as email filtering and computer vision, where it is difficult to develop conventional algorithms to perform the needed tasks. These are by far the 16 best free & paid machine learning courses online in 2020 that you can opt for if you’re thinking of doing an online course on it.

**Also Read: **12 Best Free & Paid Online Ethical Hacking Course For Beginners

**List of Top 16 [Free & Paid] Best Machine Learning Course Online in 2020**

**1. Introduction to Machine Learning for Data Science**** **

Machine learning was termed by Arthur Samuel in 1959, who was an American pioneer in the field of computer gaming and artificial intelligence. This course is about artificial intelligence in which a computer or machine looks back to the past inputs of data and makes the future predictions. In this course, students will be taught end to end process of investigating data through a specific machine learning lens. This course also teaches you how one can extract and identify things that perfectly represent your data.

**Course Created By **

David Valentine is a decorated Enterprise Architect with over 17 years of expertise in enterprise computing ecosystems.

**Things you’ll learn in this course**

- It gives the student a knowledge of how machine learning can solve problems and what are the problems.
- It will help to understand computer technology has changed the World, with an appreciation of scale.
- They will also learn what is the impact of Machine Learning and Data Science in having a society.
- It also helps students to understand how the different domains fit together and how they are different.

**Course Includes**

- 5.5 hours on-demand videos
- 1 article
- 8 downloadable resources
- Full lifetime access
- Access on mobile and TV
- Certificate of Completion

**Duration of the course: **5 hours 23mins 40secs

**Rating of the course: **4.4 / 5

**Also Read:** 21 Free Best Online Courses With Certification

**2. Machine Learning with Python: A Practical Introduction**

This course is about Machine Learning using Python, which is known as a programming language. In this course, students learn about supervised and unsupervised learning as well as they will also look into how statistical modeling relates to machine learning. In this course, students will come across many popular algorithms which include Classification, Regression, Clustering, and Dimensional Reduction. Along with them, they will also come across many popular models which are Train/Test Split, Root Mean Squared Error (RMSE), and Random Forests. Apart from that, the students will get an opportunity to transform their theoretical knowledge into practical skills using hands-on labs.

**Course Created By **

Saeed Aghabozorgi (PHD, Sr. Data Scientist)

**Things you’ll learn in this course**

- The students will get an experience of real-life examples of different ways machine learning affects society.
- The students learn how does statistical modeling is related to machine learning and how both of them can be compared.
- The students also get to learn the difference between supervised and unsupervised methods of machine learning.
- They learn a supervised learning algorithm which is classification and regression and also the unsupervised learning algorithm which is Clustering and Dimensionality Reduction.

**Course Includes **

- Introduction to Machine Learning
- Regression
- Classification
- Unsupervised learning
- Recommender Systems

**Duration of the course: **5 weeks(4–6 hours per week)

**Rating:** 4 / 5

**3. Machine Learning by Stanford**

The term Machine Learning is known as the science in which computers are made to act without being explicitly programmed. Machine Learning has been a gift to mankind and it has given us things like self-driving cars, practical speech recognition, effective web search, and also a very improved understanding of the human genome. Machine learning has grown so much that we are using them without knowing them in our day to day life. Many researchers say that it is the best way to progress towards human-level AI and importantly people get to know and learn the most effective machine learning techniques. This course you can learn for free which gives you an opportunity to learn a bit about Silicon Valley’s best practices in innovations.

**Course Created By **

Andrew Ng (CEO/Founder Landing AI, Co-Founder, Coursera Adjunct Professor, Stanford University; formerly Chief Scientist, Baidu and founding lead of Google Brain)

**Course Contents**

- Introduction
- Linear Regression with One Variable
- Linear Algebra Review
- Linear Regression with Multiple Variables
- Octave/MATLAB Tutorial
- Logistic Regression
- Regularization
- Neural Networks: Representation
- Neural Networks: Learning
- Advice for Applying Machine Learning
- Machine Learning System Design
- Support Vector Machines
- Unsupervised Learning
- Dimensionality Reduction
- Anomaly Detection
- Recommender System
- Large Scale Machine Learning
- Application Example: Photo OCR

**Things you’ll learn in this course**

- Machine Learning
- Artificial Neural Network
- Logistics Regression
- Machine learning (ML) Algorithms

**Duration of the course: **54 hours

**Rating of this course: **4.9 / 5

**4. Machine Learning Crash Course**

Machine Learning often seen as a subset of Artificial Intelligence, is the study that provides a system with the ability to automatically learn and improve from experience without it having to be explicitly programmed. The main focus of Machine learning is on the development of computer programs that can access data, use them, and learn by themselves. This crash course helps in learning and applying all the fundamental concepts of Machine learning.

**Instructor**

This course is created by Machine Learning Experts of Google

**Prerequisites of the course**

Machine Learning Crash Course does not require any prior knowledge in the course. However, for better understanding of the concepts presented and complete the exercises, these are two prerequisites:

- One must be comfortable with variables, linear equations, graphs of functions, histograms, and other statistical means.
- One should essentially be a good programmer. One should have some experience programming in Python because the programming exercises in the course are in Python. However, experienced programmers without any Python experience can usually complete the programming exercises anyway.

**Course includes**

- ML Concepts
- Introduction to ML
- Framing
- Descending into ML
- Reducing Loss
- First Steps with TF
- Generalization
- Training and Test Sets
- Validation Set
- Representation
- Feature Crosses
- Regularization: Simplicity
- Logistic Regression
- Classification
- Regularization: Sparsity
- Neutral Networks
- Training Neutral Nets
- Multi-Class Neural Nets
- Embeddings

- ML Engineering
- Production ML Systems
- Static vs. Dynamic Training
- Static vs. Dynamic Inference
- Data Dependencies
- Fairness

- ML Systems in the Real World
- Cancer Prediction
- Literature
- Guidelines

**Duration: **15 hours

**Rating:** 4 / 5

**5. Machine Learning A-Z: Hands-on Python & R In Data Science**

This course is about the complete course of Machine Learning which includes Python and R in Data Science. In this course, students will learn about supervised and unsupervised learning as well as they will also look into how statistical modeling relates to machine learning. In this course, students will come across many popular algorithms which include Classification, Regression, Clustering, and Dimensional Reduction. Along with them, they will also come across many popular models which are Train/Test Split, Root Mean Squared Error (RMSE), and Random Forests. Apart from that, the students will get an opportunity to transform their theoretical knowledge into practical skills using hands-on labs.

**Course created by **

Kirill Eremenko (Data Science) and Hadelin de Ponteves (AI Entrepreneur)

**Things you’ll learn in this course**

- The students will get to know which Machine Learning model to choose for each type of problem.
- They will be able to make robust Machine Learning models.
- They will be able to use Machine Learning for personal purposes.
- They will have a great intuition of many Machine Learning models.
- They will get the knowledge about which Machine Learning model to choose for each type of problem.

**Course Includes**

- 44 hours on-demand video
- 70 articles
- 38 downloadable resources
- Full Lifetime access
- Access on mobile and TV
- Certificate of Completion

**Duration of the course: **44 hours 26mins

**Rating of this course: **4.5 / 5

**6. Machine learning with Python**

This course is about Machine Learning with the well-known Programming language, Python. In this course, students will first learn the purpose of Machine Learning and where it should be applied in the real world and the students get knowledge about supervised and unsupervised learning, model evaluation, and Machine Learning algorithm. In this course, you even get the opportunity to practice with real-life examples of Machine Learning.

**Course Created By **

Saeed Aghabozorgi (Ph.D. Sr. Data Scientist) and Joseph SantarSantarcangelo (PhD Data Scientist)

**Things you’ll learn in this course**

- Introduction to Machine Learning
- Regression
- Classification
- Clustering
- Recommender System
- Final Project

**Duration of this course: **Approx. 21 hours

**Rating of this course: **4.7 / 5

**7. Machine Learning Fundamentals **

In this course, students will learn part of the Data Science MicroMasters program as well as will also learn many supervised and unsupervised learning algorithms and also all the theories behind those algorithms. This course also gives you the opportunity to use real-world case studies through which students will be able to learn how they can classify images, identify salient topics in a corpus of documents.

**Course created By **

Sanjoy Dasgupta, who is a Professor of Computer Science and Engineering at the University of California, San Diego.

**Things you’ll learn from this course**

- The students will learn how to do classification, regression and conditional probability estimated.
- They will learn Generative and discriminative models.
- They will also learn Linear modes and extensions to nonlinearity using kernel methods.
- In this course, they will learn ensemble methods which are boosting, bagging, random forests.

**Duration of the course: **2 months 2 weeks

**Rating:** 4 / 5

**8. Applied Machine Learning**

In this course, you will learn a wide range of techniques for supervised and unsupervised machine learning by using Python as the programming language. In this course, the first part is about Python for Data Analytics which provides you with the knowledge for doing the assignment and application projects which will be a part of the Applied Machine Learning course. This course will be ideal for students who are looking forward to implementing or lead a machine leading project.

**Course created by **

John Paisley (Columbia University Associate Professor, Electrical Engineering Affiliated Member, Data Sciences Institute)

**Things you’ll learn in this course**

- Module 1: Introduction to Data Science
- Module 2: Working with Data Types & Operators in Python
- Module 3: Writing Functions in Python
- Module 4: Popular Data Science Packages in Python
- Module 5: Advanced Functions
- Module 6: Data Manipulation and Analysis with Pandas
- Module 7: Data Visualization with Matplotlib
- Module 8: Random Variables & Statistical Inferences
- Module 9: Statistical Distributions & Hypothesis Testing
- Module 10: Data Cleaning
- Module 11: Exploratory Data Analysis
- Module 12: Getting Started with Linear Algebra for Machine Learning

Supervised Learning

- Module 1 – Regression
- Module 2: Linear Regression
- Module 3 – Bayesian Methods
- Module 4 – Foundational Classification Algorithms – Part 1
- Module 5 – Foundational Classification Algorithms – Part 2
- Module 6 – Intermediate Classification Algorithms – Part 1
- Module 7: Intermediate Classification Algorithms – Part 2

Unsupervised Learning

- Module 8 – Clustering Methods
- Module 9 – Recommendation Systems – Part 1
- Module 10 – Recommendation Systems – Part 2
- Module 11 – Sequential Data Models
- Module 12 – Association Analysis Clustering methods

**Duration of the Course: **5 months

**Rating: **4.1 / 5

**9. Data Science: Machine Learning: **

In this course, a student will learn popular machine learning algorithms, principal component analysis, and regularization by building a movie recommendation system. In this course, students will also learn about training data and how to make the use of that data in the discover potentially predictive relationships. As the student has already built a movie recommendation system it will help them to learn how to train algorithms using training data so the prediction can be done of the future outcome of the datasets.

**Course Created By **

Rafael Irizarry (Professor of Biostatistics, T.H. Chan School of Public Health)

**Things you’ll learn in this course**

- Students will learn the basics of machine learning
- They will learn how to perform cross-validation to avoid overtraining
- They will also learn several popular machine learning algorithms
- In this course, they can learn how to build a recommendation system
- Lastly, they can learn what is regularization and what is the use of it.

**Duration of the course: **2 months

**Rating: **4 / 5

**10. Machine Learning and AI: Support Vector Machines in Python**

This course is the most powerful machine, learning model. In this course you will learn that is why support vector machines are neural networks and also, they also look the same if you were to draw a diagram.

This course also includes the critical theory behind the Support Vector Machines in Python are:

- Linear SVM derivation
- Hinge loss (and its relation to the Cross-Entropy loss)
- Quadratic programming (and Linear programming review)
- Slack variables
- Lagrangian Duality
- Kernel SVM (nonlinear SVM)
- Polynomial Kernels, Gaussian Kernels, Sigmoid Kernels, and String Kernels
- Learn how to achieve an infinite-dimensional feature expansion Projected Gradient Descent
- SMO (Sequential Minimal Optimization)
- RBF Networks (Radial Basis Function Neural Networks)
- Support Vector Regression (SVR)
- Multiclass Classification

**Course Created By**

Lazy Programmer Inc. (Artificial intelligence and machine learning engineer)

**Things you’ll learn in this course**

- The students will learn to use Lagrangian Duality to derive the Kernel SVM.
- They learn to understand the theory behind SVMs from scratch.
- They learn to understand how Quadratic Programming is applied to SVM.
- They also learn the Support Vector Regression.
- They learn Polynomial Kernel, Gaussian Kernel, and Sigmoid Kernel.

**Course Includes**

- 9 hours on-demand video
- Full lifetime access
- Access on mobile and TV
- Certificate of completion

**Duration of this course: **8 hours 53mins

**Ratings of this course: **4.6 / 5

**11. Deep Learning Specialization **

In this course you will learn the base of Deep Learning, then you will understand how to build neural networks and you will also learn how to lead a successful machine learning project. The students will also learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, Batch Norm, Xavier/He initialization.

**Course Created By**

Andrew** **Ng (CEO/Founder Landing AI; Co-founder, Coursera; Adjunct Professor, Stanford University; formerly Chief Scientist, Baidu and founding lead of Google Brain)

**Things you’ll learn in this course**

- TensorFlow
- Convolutional Neural Network
- Artificial Neural Network
- Deep Learning
- Backpropagation
- Python Programming
- Hyperparameter
- Hyperparameter Optimization
- Machine Learning
- Inductive Transfer
- Multi-Task Learning
- Facial Recognition System

**Course Includes: **

- Neural Networks and Deep Learning
- Improving Deep Neural Networks: Hyperparameter tuning, Regularization, and Optimization
- Structuring Machine Learning Projects
- Convolutional Neural Networks
- Sequence Models

**Duration of this course: **Approx. 4 months

**Rating of this course: **4.8 / 5

**12. Bayesian Machine Learning in Python: A/B Testing**

This course is mainly about A/B testing. A/B testing is such a thing that is used everywhere starting from Marketing, retail, newsfeeds, online advertising. In this course, the students will learn the traditional A/B testing in order to appreciate its complexity and then eventually it will get to Bayesian machine learning way of doing things. In this course, you will also learn about the epsilon-greedy algorithm which students may have heard in the context of reinforcement learning.

**Course Created By **

Lazy Programmer Inc. (Artificial intelligence and machine learning engineer

**Things you’ll learn in this course: **

- The students will learn to use adaptive algorithms to improve A/B testing performance
- They also learn to understand the difference between Bayesian and frequentist statistics.
- In this course, students also learn to apply Bayesian methods to A/B testing.

**Course Includes**

- 6 hours on-demand video
- Full lifetime access
- Access on mobile and TV
- Certificate of Completion

**Duration of the course: **6 hours 13mins

**Ratings of this course: **4.6 / 5

**13. Machine Learning for Analytics Master Track Certificate **

Data analytics and data science positions are growing rapidly across a variety of industries. With this online certificate program, one will study at the graduate level to gain the knowledge you need to advance in your career. By committing to this online course for five months, one can easily earn the Machine Learning for Analytics MasterTrack Certificate and also have the credentials in applied data science to land the job you want.

**Course created by – **Austin L. Wright (Assistant Professor at the Harris School of Public Policy) and Gregory Bernstein (Master of Science in Analytics and Data Scientist, Kinexon Sports & Media)

**Things you’ll learn from this course **

- The students learn how to build an analytical model and interpret the results.
- They will also learn how to perform exploratory and confirmatory analyses using Python.
- In this course, they learn to conduct exploratory analyses via single-mode and multimode cluster analyses.

**Duration of this course: **5 months

**Rating:** 4 / 5

**14. Machine Learning Certificate Course**

This course is about depth in the Machine Learning along with working with real-time data, then developing algorithms with the help of supervised and unsupervised learning, regression, classification, and time series modeling. This course also teaches how to use Python in Machine Learning certification training to draw predictions from data.

**Things you’ll learn in this course**

- Supervised and unsupervised learning
- Time series modeling
- Linear and logistic regression
- Kernel SVM
- Decision trees
- KMeans clustering
- Naive Bayes
- Decision tree
- Random forest classifiers
- Boosting and Bagging techniques
- Deep Learning fundamentals

**Course content **

- Machine Learning
- Math Refresher
- Statistics essential for Data Science

**Duration of the course: ** 44 hours

**Ratings of the course: **4.2 / 5

**15. Machine Learning Using SAS Viya**

This course is about the theoretical foundation for different techniques associated with supervised machine learning models. This course also includes a business case study defined which will guide the students through all the steps of the Analytic cycle starting from problem understanding to model deployment, through data development, feature collection, model preparation and validation, and model evaluation. This course uses a thing called Model Studio which is the pipeline flow interface of SAS Viya that helps you to prepare certain things such as develop, compare, and deploy advanced analytics models. In this course, you will also learn to train supervised machine learning models to make better decisions on big data.

**Course Created By**

Jeff Thompson (Senior Analytical Training Consultant) and Catherine Truxillo (Director, Analytical Education)

**Things you’ll learn in this course: **

- Course Overview
- Getting Started with Machine Learning using SAS Viya
- Data Preparation and Algorithm Selection
- Decision Trees and Ensemble of Trees
- Neural Networks
- Support Vector Machines
- Model Development

**Duration of the Course: **30 hours

**Rating of this course: **4.7 / 5

**16. Machine Learning by Edx**

In this course, you will learn models and methods which you will apply to the real world which are from identifying trending news topics, to building recommendation engines, ranking sports teams, and plotting the path of movie zombies. The course also consists of topics like classification and regression, clustering methods, sequential models, matrix factorization, topic modeling, and model selection as well as includes methods which are linear and logistic regression, help vector devices, tree classifiers, boosting, maximum likelihood and MAP conclusion, EM algorithm, hidden Markov models, Kalman filters, k-means, Gaussian mixture models.

**Course Created By **

John W. Paisley (Department of Electrical Engineering, Columbia University).

**Things you’ll learn in this course**

- The students will be supervised learning techniques for regression and classification.
- The students will also learn unsupervised learning techniques which are data modeling and analysis.
- They will also learn Probabilistic versus non-probabilistic viewpoints.
- In this course, students will also learn Optimization and inference algorithms for model learning.

**Course Includes**

- Maximum likelihood estimation, linear regression, least squares
- Ridge regression, bias-variance, Bayes rule, maximum a posteriori inference
- Bayesian linear regression, sparsity, subset selection for linear regression
- Nearest neighbor classification, Bayes classifiers, linear classifiers, perceptron logistic regression, Laplace approximation, kernel methods, Gaussian processes
- Maximum margin, support vector machines, trees, random forests, boosting
- Clustering, k-means, EM algorithm, missing data.
- Mixtures of Gaussians, matrix factorization
- Non-negative matrix factorization, latent factor models, PCA and variations
- Markov models, hidden Markov models.
- Continuous state-space models, association analysis
- Model selection, next steps.

**Duration of the Course: **3 months.

**Rating:** 4 / 5

**Conclusion**

If you’re looking for any Machine Learning courses then these are your go-to courses. You will not only learn the basics but also upgrade your skills via these popular courses. The certificates of most of these courses are renowned and can help you kickstart your career.