Finally, It’s here.
I was getting so many messages and emails requesting to have a list of free machine learning courses. I was just waiting for the fact that all basic skills that are needed to move into Machine Learning should be covered first. So, firstly I created the list for Python, Statistics, and Data Science for having enough resources to learn this skill.
Machine Learning is one of the most trending tech skills in the present work scenario. Companies such as Google, Amazon, Baidu are investing so much money in machine learning and AI technologies. Even smaller companies are also coming forward to take help from algorithms built on Machine Learning models.
Being a field of research there is so much to uncover yet researchers are creating new models every now and then. Also, there is a huge demand for skilled Machine Learning developers in the corporate world. Due to high demand and work that needs perfection in multiple skills ML developers are paid higher than other computer science profiles.
Recently Univa had a survey in which they interviewed 344 technology and IT professionals across 17 industries. According to that survey, more than 90% of companies expect a massive growth towards Machine Learning projects in the coming 2 years. The only thing that is keeping those companies back is the lack of proper resources and professionals to migrate to machine learning technologies.
This might have cleared your confusion that if you should choose this field for your career or not. Now we should move to the topic of this article. Below are some free resources for you to learn Machine Learning to the fullest. Bookmark this article for using the details later as it is going to be a huge list of resources.
💡 Important: You can get free and paid datasets for machine learning and AI from Bounding.ai
Free Machine Learning Guides for Beginners
Guides are always a good way to know about the in and out of a particular skill. You don’t need to rush into things without knowing properly what they would need from you. Machine Learning is not an easy field to just take one or two courses and excel it. So prepare yourself for the challenges with these guides and create a learning path that you can go through step by step.
I myself wrote this guide for beginners who want to understand a bit about this field. The guide will help you in knowing about the fundamental topics and how you can create a learning path. I tried to mention the exact steps that I used for getting into Machine Learning.
So if you are someone who is new to this field then you can read this guide for an overview. The guide also covers some major profiles that you can get a job in after gaining this skill.
Python is considered one of the best languages to start Machine Learning with. So, this guide is good for those who are willing to learn it with the Python programming language. And if you are here reading this guide then many of you might have already chosen your preferred language.
This guide is from Towards Data Science and is written by Oleksii Kharkovyna. It has got 5 steps that you can follow to learn this skill in the best way possible. As this field has so much to learn and hundreds of resources that can confuse students. Thus it is good to move with a guide in hand to clear your path side by side. The author has shared some informative and practical tips on starting your learning journey.
This is I think one of the most comprehensive overviews of Machine Learning for beginners. You can learn a lot through this even if you have not heard of these terms before. the guide starts by explaining the evolution of Machine Learning and then shares some key insights on its importance. There are some additional reports provided which you can download and learn. Those reports include:
- Opportunities and Challenges for Machine Learning in Business
- Expand Your Skill Set
- Will ML Change Your Organization
- Applying Machine Learning to IoT
After that, you can learn about all the industries that are using this technology. There is more to learn as you will scroll down the page.
This is especially for people who want to learn these skills on their own by finding the best possible resources. Self-learning can be daunting but if you got a handy guide then it becomes an easy task. This guide from Elite Data Science covers all the points that a beginner needs to know about the learning path.
Firstly there is a basic introduction to Machine Learning and some questions that you might be having. Then it shares a step-by-step process for beginners to start learning it right away. At the end of the guide, there are some bonus goodies for those who had the patience to read it till the end.
You should always have a pen and paper to note down important points from such informative guides.
Free Machine Learning Courses
If you have gone through the guides mentioned above then you might have got enough knowledge about this field. Now it is your time to use those basics and move further with these free courses.
I created this list recently which contains some free Python courses for beginners. As Python is one of the popular languages for working on ML algorithms so it is better to learn Python from scratch. The list has also got some guides that you can read to get a beginner-level overview of the Python programming language.
After learning to program with Python you could move on to learning the next skills.
Mathematics and Statistics are important parts of your Machine Learning journey. You may skip at the beginning but you will need them at later stages while doing complex programming. I just shared this list of free statistics courses this week which you can go through and enroll in some courses. I have also added some beginner-level guides if you just want to get an overview of statistics.
You will need data analysis skills while working on machine learning algorithms. So I just created a list of some data science courses that you can take before going into machine learning courses. I just delayed this article because I thought to complete other basic necessary skill courses before writing this one. You can enroll in those basic courses and then can move directly to learn ML.
This is a self-study guide from Google for helping Machine Learning practitioners. It has got a series of video lectures, case studies, and exercises to test your skills side by side. After completing the course students will be able to apply fundamentals concepts in a real-world environment. The course contains 25 lessons, 40+ exercises, 15 hours of content, and interactive visualization of real-world ML algorithms.
Concepts covered in this course are:
- Difference between Machine Learning and Traditional Programming
- Knowing about Loss and Measuring it
- Gradient Descent and Its Working
- Testing Different Models
- Representing Data
- Building Deep Neural Networks
Check the prerequisites before starting the course for a better learning experience.
This is a free curriculum created by Springboard and contains some concise yet informative hands-on tutorials. More than 20,000 students have already enrolled in the curriculum. It contains 14 resources segmented in different steps which will help you in learning Machine Learning from Scratch.
The main modules of this curriculum are:
- Getting Started
- Learn Python
- Data Wrangling
- Supervised Machine Learning
- Unsupervised Machine Learning
It is just a career path that links to further resources arranged in steps for you to learn.
This is a free course from Coursera provided by Stanford University. More than 2 lac students have already taken the course and it has got more than 1 lac 4.9-star ratings. It is taught by Andrew Ng the co-founder of Coursera. This is one of the most popular Machine Learning courses on the Internet.
The course will take around 56 hours to complete and is divided into 9 weeks of curriculum. It will help students in getting a broad introduction to machine learning, data mining, and statistical pattern recognition. Other sub-topics include parametric/nonparametric algorithms, support vector machines, kernels, neural networks, and more.
Some important topics from this course are:
- Linear Regression With One Variable
- Linear Algebra Review
- Linear Regression With Multiple Variables
- Logistic Regression
- Neural Networks
- Learning Process of Neural Network and more
You can audit the course for free but that will not have a certificate.
DataCamp is one of the most popular online learning platforms, especially for Data Science. Their Machine learning courses are also good for beginners to learn from scratch. Although they only offer free access to some parts of the courses through Microsoft you can get 2 months of free DataCamp account.
And for anyone 2 months would be enough to learn new skills from scratch if you give 2-3 hours daily as the Machine Learning scientist is of 61 hours duration. I am linking to our guide which will help you in getting free access to DataCamp for 2 months.
The course is taught by Jeremy Howard and was recorded at the University of San Fransisco. You will be learning about some popular Ml models and then building them from scratch. But before diving into the lessons you should have basic coding knowledge either with Python or R language. Also, you will be needing Math skills that you learned up to high school.
There are around 24 hours of content in this series for which you should provide adequate time to learn and practice. They have also provided some additional resources including Forum, Wiki, and a Blog from Fast.ai for taking in student queries.
In this Machine Learning course, you will learn about the fundamental tools and technologies. You will also be learning about some real-world examples of its use and how it affects society. Some of the popular algorithms explained in the course are Classification, Regression, Clustering, and Dimensional Reduction. Some models that have been covered are Train/Test Split, Root Mean Squared Error, and Random Forests.
The syllabus of this course is as follows:
- Introduction to Machine Learning
- Unsupervised Learning
- Recommender System
You will need to have enough Python coding experience before getting into the course modules. For that, you can take the courses I mentioned above.
TensorFlow is a popular framework and open-source framework for Machine Learning. This course from Coursera takes you on a journey to learn it for Machine Learning, Deep Learning, and Artificial Intelligence. For getting the best out of your skills you always need hands-on experience with working on tools and libraries.
You will be building a neural network in TensorFlow, Training it for a computer vision application. After that, you will learn to use convolutions to improve your neural network. The course content has been divided into 4 weeks of curriculum.
Some Important Topics from the course are:
- A New Programming Paradigm
- Introduction to Computer Vision
- Enhancing Vision with Convolutions
- Using Real-World Images
This is an intermediate-level course so you will need to have basic Python knowledge. Also, be prepared with high school math before starting the course.
The list of free Machine Learning courses ends here and I hope that you would love it. Do share your feedback in the comment and share it with someone in need. ♥