12 Best Machine Learning Courses For Free Which Will Make You Expert

In this article, we will discuss some of the best Machine Learning course available for free that will help you to learn the basics and fundamentals of Machine Learning, and also how to implement the knowledge that you gain.

The main object behind this article is to save your valuable money, I many time saw people wasting their money on the unnecessary paid course, which you don’t need to do. Other than any prerequisite, the main prerequisite that is essential for this best Machine Learning course is your passion and love toward the field.

Around 96% of companies expect to see an explosion of machine learning projects in production by 2020, as per a report by Univa, AI-driven services are expected to grow from $1.07 billion in 2016 to $19.9 billion by end of 2025. If you are interested in ML, then it is the best time to dive into this field.

With below mentions best Machine Learning courses you will learn various concepts like parameter learning, logistic regression model, neural networks, application of neural networks, cost function and backpropagation etc. If you already learn this, then you can use this best Machine Learning courses to brush-up your mind.

The Machine Learning courses that we are going to talk here are primarily selected on the quality of content, language simplicity and on public reviews so that you can learn Machine Learning in a correct manner.

The Machine Learning courses listed in this list are not set in a particular order, but they are arranged mostly in such an order that will help you to level up your knowledge.

Note that while you are learning machine learning, don’t forget to take handwritten notes as it helps in memory retention.

1) Programming Language

If you have already master or learning a programming language that heavily helps in creating Machine Learning based stuff, then you can directly move to the next point, but if you are new here then wait I have a surprise for you.

A new mind is always good as it is free from confusion if you haven’t learned any language yet, then at this right moment start with Python or R or JavaScrip, I’ll suggest you Python.

I mostly suggest beginners go with Python for Machine Learning, because Python is quick, fast and easy to understand. Medium has a great article on Why Python is the most popular language used for Machine Learning, I can talk about Python all day, but to keep the article short you can head over this link and can clear doubts.

And if we talk about R, then R is good for ad-hoc analysis and exploring data sets, R has a steep learning curve, but people without programming experience most beginner find it overwhelming. R also don’t have lots of libraries that Python actually offers, also the community of Python way big, so you can easily find someone who will help you in your errors.

Now the surprise that I talk at earlier is here, If you are thinking to learn R or Python, then below are links from where you can download the most popular and useful books based on Python and R for free, this book will help to build the core on which you can learn advanced concepts at a great speed.

Automate the Boring Stuff with Python

Programming with R

2) Udacity’s Intro to Machine Learning

Clarity in thought in must when you are in process of learning. In this second point of best Machine Learning courses, we talk about Udacity which one of the best online learning platform. Udacity offers a free Machine Learning course called Intro to Machine Learning that will wash down all your pre-assumptions, confusion and doubts.

This course is a part of Udacity’s Data Analyst Nanodegree. There are many best Machine Learning courses available on Udacity which are mostly paid but this one is for free. This Machine Learning course is about 10-weeks long and it will teach you the end-to-end process of investigating data through a machine learning lens.

It will teach you how to extract and identify useful features that best represent your data, a few of the most important machine learning algorithms, and how to evaluate the performance of your machine learning algorithms.

I also recommend you to take the foundational Intro to Data Science course which deals with Data Manipulation, Data Analysis, Data Communication with Information Visualization, and Data at Scale, this helps you understand machine learning concepts more easily.

Intro to Machine Learning will be taught by instructors Sebastian Thrun and Katie Malone, the instructors expect the beginners to know basic statistical concepts and Python.

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3) Machine Learning from Stanford

This course is a very special course on this list of best Machine Learning courses, as it is taught by Andrew Y. Ng, Andrew is a prestige name in the field of machine learning. He is a is a co-Founder of Coursera, associate professor in Stanford University’s Computer Science and EE departments, Baidu’s Chief Scientist and a former head of Google Brain.

Andrew Y. Ng believes in free educations and that why he created many different free best Machine Learning courses, this course “Machine Learning from Stanford” is specially designed for beginners by Andrew.

In this Machine Learning course, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you’ll learn about not only the theoretical underpinnings of learning but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems.

Recommended: Get Machine Learning Yearning An Amazing Book By Andrew Ng For Free

Finally, you’ll learn about some of Silicon Valley’s best practices in innovation as it pertains to machine learning and AI. This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition.

Topics that these free Machine Learning Course covers include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).

The course will also draw from numerous case studies and applications so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

After doing this Machine Learning Course, you will gain skills like Logistic Regression,Artificial Neural Network, Machine Learning (ML),  Algorithms used for Machine Learning.

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4) Google course on Machine Learning

Google is a leading company in the field of ML, and Google wants that people must master this skill. Google has launched many best Machine Learning course for free that will teach you many valuable concepts related to this field.

In this list of best Machine Learning courses, we are taking the course that name “Machine Learning Crash Course with TensorFlow APIs”.

This free Machine Learning Course will teach you to recognize the relative impact of data quality and size to algorithms, set informed and realistic expectations for the time to transform the data, explain a typical process for data collection and transformation within the overall ML workflow. collect raw data and construct a data set, example and split your dataset with considerations for imbalanced data. transform numerical and categorical data.

This Course will have 25 lessons, 40+ exercises, interactive visualizations of algorithms in action, real-world case studies, lectures from Google researchers.

This course will answer you all questions like :

1) How does machine learning differ from traditional programming?

2) What is a loss, and how do I measure it?

3) How does gradient descent work?

4) How do I determine whether my model is effective?

5) How do I represent my data so that a program can learn from it?

6) How do I build a deep neural network?

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5) Mathematics for Machine Learning Specialization

Maths is the core of Machine Learning, the one who doesn’t know about ML math, he finds Machine Learning as a complex and hard field. That’s why for higher level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in mathematics.

Mathematics for Machine Learning Specialization is one of must recommend a course in this list of best Machine Learning courses. This ML course will help in getting speed in the underlying mathematics, building an intuitive understanding, and relating it to Machine Learning and Data Science.

If you think about what this course will teach me then, this ML course will teach you Linear Algebra, you will understand what is linear algebra is and how it relates to data. Then it will take you through what vectors and matrices are and how to work with them.

In this course, there is the second section called Multivariate Calculus. the Multivariate Calculus will teach you how to optimize fitting functions to get good fits to data. It starts from introductory calculus and then uses the matrices and vectors from the first course to look at data fitting.

The third section, Dimensionality Reduction with Principal Component Analysis, uses the mathematics from the first two sections to compress high-dimensional data. This course is of intermediate difficulty and will require basic Python and numpy knowledge.

At the end of this specialization, you will have gained the prerequisite mathematical knowledge to continue your journey and take more advanced courses in machine learning.

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6) Machine Learning Fundamentals

Machine Learning Fundamentals is a course offered by the University of California, San Diego. In this 6th course of best Machine Learning courses, you will learn classification, regression, and conditional probability estimation, generative and discriminative models, linear models and extensions to nonlinearity using kernel methods, ensemble methods: boosting, bagging, random forests, representation learning: clustering, dimensionality reduction, autoencoders, deep nets.

Using real-world case studies, in this Machine Learning course, you will learn how to classify images, identify salient topics in a corpus of documents, partition people according to personality profiles, and automatically capture the semantic structure of words and use it to categorize documents.

This course also offers an additional section called Data Science MicroMasters program, where you will learn a variety of supervised and unsupervised learning algorithms and the theory behind those algorithms.

Armed with the knowledge from this course, you will be able to analyze many different types of data and to build descriptive and predictive models. All programming examples and assignments will be in Python, using Jupyter notebooks. The course will be taught by Sanjoy Dasgupta, Professor of Computer Science and Engineering, UC San Diego.

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7) Machine Learning: Classification

Machine Learning: Classification is one of dippest and knowledge riched course of this list of best Machine Learning Course. Its first section is based study on analyzing sentiment, where you will create models that predict a class (positive/negative sentiment) from input features (text of the reviews, user profile information,…).

The second section will have loan default prediction, where you will tackle financial data, and predict when a loan is likely to be risky or safe for the bank. These tasks are examples of classification, one of the most widely used areas of machine learning, with a broad array of applications, including ad targeting, spam detection, medical diagnosis, and image classification.

This Machine Learning course will teach you how to create classifiers that provide state-of-the-art performance on a variety of tasks. You will become familiar with the most successful techniques, which are most widely used in practice, including logistic regression, decision trees and boosting.

In addition, you will be able to design and implement the underlying algorithms that can learn these models at scale, using stochastic gradient ascent. You will implement these technologies on real-world, large-scale machine learning tasks.

You will also address significant tasks you will face in real-world applications of ML, including handling missing data and measuring precision and recall to evaluate a classifier.

This course is hands-on, action-packed, and full of visualizations and illustrations of how these techniques will behave on real data. It also included optional content in every module, covering advanced topics for those who want to go even deeper.

The concepts in this course will be implemented with Python. The major things this Machine Learning will cover will describe the input and output of a classification model, tackle both binary and multiclass classification problems, implement a logistic regression model for large-scale classification, create a non-linear model using decision trees, improve the performance of any model using boosting. scale your methods with stochastic gradient ascent.

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8) Machine Learning: Regression

In this course, you will learn Regression in ML that is a measure of the relation between the mean value of one variable (e.g. output) and corresponding values of other variables. This course will also be dived into a number of sections in order to build an easy understanding.

The first section will be of predicting house prices, where you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,…). This is just one of the many places where regression can be applied.

Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high-performance computing, to analyzing which regulators are important for gene expression.

In this course, you will explore regularized linear regression models for the task of prediction and feature selection. You will be able to handle very large sets of features and select between models of various complexity. You will also analyze the impact of aspects of your data — such as outliers — on your selected models and predictions. To fit these models, you will implement optimization algorithms that scale to large datasets.

The concepts in this course will be implemented with Python. The major things this Machine Learning will cover will describe the input and output of a regression model, compare and contrast bias and variance when modeling data, estimate model parameters using optimization algorithms, tuning parameters with cross-validation, analyze the performance of the model. describe the notion of sparsity and how LASSO leads to sparse solutions. build a regression model to predict prices using a housing dataset.

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9) Machine Learning for Data Science and Analytics

Though Machine Learning is sub-branch of AI, I find it mostly close to Data Science. When you learn Machine Learning and play with it you will find that you are mostly interacting with data and at you some point you will also think like me.

Machine Learning is now mostly using for searching the web, placing ads, credit scoring, stock trading and for many other applications and this course will help you so you can also be able to do this thing.

This data science course is an introduction to machine learning and algorithms. You will develop a basic understanding of the principles of machine learning and derive practical solutions using predictive analytics. This course will also help you understand why algorithms play an essential role in Big Data analysis.

In this course, you will learn what machine learning is and how it is related to statistics and data analysis, how machine learning uses computer algorithms to search for patterns in data, how to use data patterns to make decisions and predictions with real-world examples from healthcare involving genomics and preterm birth, how to uncover hidden themes in large collections of documents using topic modeling, how to prepare data, deal with missing data and create custom data analysis solutions for different industries, basic and frequently used algorithmic techniques including sorting, searching, greedy algorithms and dynamic programming

The course will be taught by Ansaf Salleb-Aouissi, Cliff Stein, David Blei, Itsik Peer, Mihalis Yannakakis and Peter Orbanz.

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10) Foundations of Data Science: Prediction and Machine Learning

I know you might be getting sucked or bore with predictions and regression, but believe me, Foundations of Data Science: Prediction and Machine Learning, will help you level up your skills that you will learn from all others best Machine Learning courses.

In this course you will learn how to use machine learning, with a focus on regression and classification, to automatically identify patterns in your data and make better predictions

One of the principal responsibilities of a data scientist is to make reliable predictions based on data. When the amount of data available is enormous, it helps if some of the analysis can be automated. Machine learning is a way of identifying patterns in data and using them to automatically make predictions or decisions. In this data science course, you will learn the basic concepts and elements of machine learning.

The two main methods of machine learning you will focus on are regression and classification. Regression is used when you seek to predict a numerical quantity. Classification is used when you try to predict a category (e.g., given information about a financial transaction, predict whether it is fraudulent or legitimate).

For regression, you will learn how to measure the correlation between two variables and compute a best-fit line for making predictions when the underlying relationship is linear. The course will also teach you how to quantify the uncertainty in your prediction using the bootstrap method. These techniques will be motivated by a wide range of examples.

For classification, you will learn the k-nearest neighbor classification algorithm, learn how to measure the effectiveness of your classifier, and apply it to real-world tasks including medical diagnoses and predicting genres of movies.

The course will highlight the assumptions underlying the techniques and will provide ways to assess whether those assumptions are good. It will also point out pitfalls that lead to overly optimistic or inaccurate predictions.

The major concepts that this course covers are fundamental concepts of machine learning. linear regression, correlation, and the phenomenon of regression to the mean, classification using the k-nearest neighbors’ algorithm, how to compare and evaluate the accuracy of machine learning models, basic probability and Bayes’ theorem.

This course will be taught by Ani Adhikari Teaching Professor of Statistics UC Berkeley,  John DeNero Giancarlo Teaching Fellow in the EECS Department UC  Berkeley, David Wagner Professor of Computer Science UC Berkeley.

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11) Dynamic Programming: Applications In Machine Learning and Genomics

If you look at two genes that serve the same purpose in two different species, how can you rigorously compare these genes in order to see how they have evolved away from each other? And the answer of this question will be in this 11th course of best Machine Learning courses list.

The first part of this course will be of Algorithms and Data Structures MicroMasters program, where you will see how the dynamic programming paradigm can be used to solve a variety of different questions related to pairwise and multiple string comparison in order to discover evolutionary histories.

In the second part of the course, you will see how a powerful machine learning approach, using a Hidden Markov Model, can dig deeper and find relationships between less obviously related sequences, such as areas of the rapidly mutating HIV genome.

This course will teach you dynamic programming and how it applies to basic string comparison algorithms, sequence alignment, including how to generalize dynamic programming algorithms to handle different cases, hidden Markov models, how to find the most likely sequence of events given a collection of outcomes and limited information, Machine learning in the sequence alignment.

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