Introduction to Machine Learning for Coders — Fast.ai

This course ranks 7th in the top best online Machine Learning courses. This fantastic, free machine learning course was created by Fast.ai for folks with around a year of Python programming expertise. The founders of Fast.ai have spent a lot of time and attention into this course — and other courses on their site. The lectures are done in a classroom with students, similar to the MIT OpenCourseware model, and the subject is based on the University of San Diego's Data Science program.

There are several videos, homework assignments, thorough notes, and a discussion board in this course. You won't find graded assignments and quizzes, nor will you receive certification upon completion, thus Coursera/Edx might be a better option if you like such elements.Because so much of the course information is applied, you'll learn a lot.

Much of the material in the course is applied, so you'll learn not just how to utilize machine learning models, but also how to deploy them on cloud platforms like AWS.

Course Structure:

• Introduction to Random Forests
• Random Forest Deep Dive
• Performance, Validation, and Model Interpretation
• Feature Importance. Tree Interpreter
• Extrapolation and RF from Scratch
• Data Products and Live Coding
• RF From Scratch and Gradient Descent
• Gradient Descent and Logistic Regression
• Regularization, Learning Rates, and NLP
• More NLP and Columnar Data
• Embeddings
• Complete Rossmann. Ethical Issues

This course is great if you are a programmer who wants to learn and use machine learning concepts, but there is one flaw: they teach machine learning using an open source library (named fastai), which is a class on other machine learning libraries like PyTorch. If you only want to use machine learning for your project and don't want to learn PyTorch, the fastai library provides useful abstractions.

Provider: Fast.ai
Cost: Free

Rate: N/A
Enroll here :https://course18.fast.ai/ml