Machine Learning: Become Expert | Full Course | 31hrs

Description
Imagine being frustrated because you do not understand what AI, Machine Learning and Deep Learning are all about. Accept the reality that not understanding the details puts your career at risk. There is no real course out there that explains every subject from start to finish including all the math.

This course is made for people that have no prior knowledge and that are committed to become credible data scientists. The course offers math refreshers in linear algebra, calculus and statistics equipping you to better understand the mathematical details behind the algorithms. The coding sessions will explain every block of code, so that no prior coding expertise is necessary.

The course is delivered through whiteboard sessions and screen recording sessions for the coding exercises. The code is made available via .ipynb files attached to the lecture itself or at the end of a section. Notes are available for the majority of the lectures, except for lectures 1 to 12 as these lectures are more descriptive. Reference is made to my Github account (mfavaits) where some of the notes can be found as well. The majority of the notes are handwritten. It would have been great if they were typed out but you understand that this is a massive amount of work. The notes by itself are a tough read but having them in front of you when looking at the videos will help you.

Who this course is for:
Anyone with a deep interest in AI, Machine Learning and Deep Learning

Genre: eLearning | MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 31.6 GB | Duration: 31h 22m

What you’ll learn
Artificial Intelligence, Machine Learning and Deep Learning
Coding: python, keras, colab, pandas
Machine Learning Fundamentals and Math refresher for Machine Learning: linear algebra, calculus, statistics
Computer Vision, NLP, Naive Bayes, XGBoost, Logistic Regression, Bagging, Boosting, Radom Forest, Transformers, LSTM, GRU, Anomaly Detection, Clustering
Dropout, Backpropagation, Gradient Descent, Variational auto-encoders, Covnets, Recurrent Neural Nets, Recommender Systems, LOF, Support Vector Machines (SVM)