10 Free Courses for Machine Learning and Data Science
These are some of the courses which will enable you to learn from introductory level to higher level of machine learning to deep learning to natural language processing and beyond.
Machine Learning Crash Course
Google
This course is taken by Google, a company everyone wishes to work in. This crash course is a real life experience with the companion Kaggle competition.
Introduction to Deep Learning
MIT
This course will enable students to gain foundation knowledge of deep learning algorithms and also to gain pratical experience with building neural networks in Tensorflow. Prerequisites assume Calculus (i.e Taking a derivative), linear algebra (i.e Matrix Multiplication). It will also include experience of Computer Vision, Natural Language Processing, Biology and more!!
Introduction to Computational Thinking and Data Science
MIT
This course, taught by MIT educators, 2 sessions/ week having a duration of 1 hour, includes problem solving skills which helps students irrespective of major, skills and even no programming skills. It teaches to feel confident about their skills and ability to write small codes and achieve goals. Experiments in this course uses the Python 3.5 version.
Machine Learning
Stanford
This course, taught by Anand Avati, mainly focuses on Machine Learning and Statistical Pattern Recognition. Topics include: Supervised Learning, Unsupervised Learning, Learning theory, Reinforcement Learning and Adaptive Control.This course also focus on applications of Machine Learning such as Robotic Control, Data Mining, bioinformatics, speech recognition and text and web data processing.
Introduction to Machine Learning for Coders!
fast.ai
This course is taught by Kaggle’s no 1 Competitor since 2 years and lessons of 24 hours so you can spend 12 weeks 8 hours per day on each lesson. This will teach you most important machine learning models,how to create them, also skills in data cleaning and model validation.
Practical Deep Learning For Coders, Part 1
fast.ai
This 7 week course will teach you everything about deep learning if you are totally new to it step by step. It is assumed that you are doing since 1 year and know Python (if not familiar with) and give extra time to learn about as it is suggested to know about it as you are going to need it the most in this course.
Natural Language Processing
Yandex Data School
This course is actually a Github repository and it will teach you all about Natural Language Processing and techniques like word embeddings, text classification, language models, structured learning.
From Languages to Information
Stanford
This 3 month course will introduce you to how the large unstructured data is converted to useful data and also it will teach you how to interact with humans via language from answering questions to giving advice.
Practical Reinforcement Learning
Yandex Data School
This is is Github repository which will teach you reinforcement learning in deep and also provide assignment for more deep understanding. As it is known that everything is essential in solving reinforcement learning , it is worth mentioning. This course will give you a “feel” of practical problem.
Computational Linear Algebra for Coders
fast.ai
This is a Github repository mainly focusing on question: ” How do we do matrix computations with acceptable speed and acceptable accuracy?”. This course is taught in Python using Jupyter notebook. This course uses the scikit and many more libraries and also teaches major lessons on Pytorch.
Introduction to Tensorflow for Deep Learning
Tensorflow
You will be taught practical approach to deep learning by Tensorflow. You will also get hands on experience for building your own image classifiers and other models. This course will teach you to use your models on your mobile devices and let you experience it yourself in cloud and browsers. At the end of this course, you will have all the applications and skills regarding AI so as to start creating your own device applications.
You can also check out our this post on Which languages should you learn for Data Science.
