What you'll learn

Artificial Intelligence, Machine Learning and Deep Learning, Data Science, Data Scientist

Coding, Code 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)

Data Augmentation, KNN, Collaborative Filtering, GloVe, Word2Vec, Resnet, VGG19, Adam, RMSprop, Adaboost, Momentum, hyperparameter

Description

Mastering the subjects of this course will give you access to a variety high paying jobs. This is the only course on the market that explains every little detail in a logical sequence. This course is absolutely no walk in the park and will require discipline.

No prior knowledge is required and the course offers math refreshers in linear algebra, calculus and statistics. The course assumes that you have no prior coding experience. All the software that we use is open source and free. We will explain every block of code.

The course is delivered through simple whiteboard and screen recording sessions. The code is made available via .ipynb files attached to the lecture itself. 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. 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

Anyone that wants to understand the math behind AI




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