Image by Author
 

There are many low-quality free courses and YouTube courses that provide no help in building strong machine learning fundamentals. You will end up even more confused and quit pursuing the career. 

I am a big advocate of paid courses, but you can also learn a lot from interactive free courses by Udacty, Coursera, and FastAI. These courses cover fundamentals and introduce you to supervised, unsupervised, and deep learning algorithms. 

 

1. Supervised Machine Learning by Stanford

 

The Supervised Machine Learning course fundamentals of supervised machine learning: Regression and Classification with popular Python libraries. 

You will be introduced to machine learning applications, examples, and building your first linear and logistic regression model on Jupyter Notebook. Furthermore, you will learn about feature engineering, gradient descent, cost function, decision boundary, and regularization.

 

Image from Coursera
 

Prerequisite: familiarity with Probability, Statistics, and Python programming language. 

Timeline: 33 hours (Self-Paced)

Skill level: Beginner

Perks: It is taught by industry professionals and Andrew NG. It comes with interactive exercises and hands-on learning projects.  

Topics covered: building regression and classification models using popular machine learning libraries NumPy & scikit-learn.

 

2. Machine Learning from Georgia Tech

 

The Machine Learning course from Georgia Tech introduces supervised, unsupervised, and reinforcement learning. You will be learning from video lessons and interactive quizzes and exercises. 

 

Image from Udacity
 

Prerequisite: strong familiarity with Probability Theory, Linear Algebra, and Statistics. Students also require some experience with the Python programming language. 

Timeline: 4 months (Self-Paced)

Skill level: Intermediate

Perks: Taught by industry professionals. It comes with an interactive exercise.  

Topics covered: supervised, unsupervised, and reinforcement learning with code examples. 

 

3. Practical Deep Learning for Coders by fast.ai

 

The Practical Deep Learning for Coders is designed for students with some knowledge of programming who want to learn and apply deep learning to solve practical problems.  

It is my favorite course, and I love the community, quizzes, and projects. They all are designed to help you learn the concepts and come up with your state-of-the-art solution. 

The perk of the course is that it is taught by a teacher and deep learning practitioner Jeremy Howard who has dedicated his life to making machine learning accessible to all for free.  

 

Image from fast.ai
 

Prerequisite: Students require some experience with the Python programming language. 

Timeline: ~ 3 months (Self-Paced)

Skill level: Beginners - Intermediate

Perks: It is taught by industrial professional Jeremy Howard. It comes with quizzes, coding examples, community-driven learning, and projects.  

Topics covered: model deployment, neural networks, NLP, creating a model from scratch, random forest, CNNs, and data ethics. 

 

Conclusion

 

The internet is crowded with hundreds of free courses, and sometimes it is hard for you to find quality courses that will help you progress in your career. The courses I have mentioned are enough to build the machine learning fundamentals. And after that, you can start working on the projects or participate in Kaggle competitions to get the experience of handling the data and building the models.  

You will learn more if you are working on unguided projects. I hope you like my small list of courses. Comment below if you have questions regarding the machine learning career. 

 
 
Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master's degree in Technology Management and a bachelor's degree in Telecommunication Engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.