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
Thanks for joining the course, let’s get started!
☞ 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
☞ Hands-On Machine Learning: Learn TensorFlow, Python, & Java!