Introduction to Machine Learning & Deep Learning in Python

Description :

This course is about the fundamental concepts of machine learning, focusing on regression, SVM, decision trees and neural networks. These topics are getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to investment banking. Learning algorithms can recognize patterns which can help detect cancer for example or we may construct algorithms that can have a very good guess about stock prices movement in the market. 

In each section we will talk about the theoretical background for all of these algorithms then we are going to implement these problems together. We will use Python with Sklearn, Keras and TensorFlow.

Machine Learning Algorithms: regression and classification problems with Linear Regression, Logistic Regression, Naive Bayes Classifier, kNN algorithm, Support Vector Machines (SVMs) and Decision Trees

Machine Learning approaches in finance: how to use learning algorithms to predict stock prices

Computer Vision and Face Detection with OpenCV

Neural Networks: what are feed-forward neural networks and why are they useful

Deep Learning: Recurrent Neural Networks and Convolutional Neural Networks and their applications such as sentiment analysis or stock prices forecast

Reinforcement Learning: Markov Decision processes (MDPs) and Q-learning

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