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 feedforward 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 Qlearning
Thanks for joining the course, let’s get started!
 This course is meant for newbies who are not familiar with machine learning or students looking for a quick refresher
Suggest:
☞ Deep Learning Prerequisites: Logistic Regression in Python
☞ Ensemble Machine Learning in Python: Random Forest, AdaBoost
☞ Machine Learning and AI: Support Vector Machines in Python
☞ Learn Python Through Exercises
☞ Machine Learning Intro for Python Developers
☞ HandsOn Machine Learning: Learn TensorFlow, Python, & Java!