Machine Learning models, Neural Networks, Deep Learning and Reinforcement Learning Approaches in Keras and TensorFlow

Solving regression problems (linear regression and logistic regression)

Solving classification problems (naive Bayes classifier, Support Vector Machines - SVMs)

Using neural networks (feedforward neural networks, deep neural networks, convolutional neural networks and recurrent neural networks

The most up to date machine learning techniques used by firms such as Google or Facebook

Face detection with OpenCV

TensorFlow and Keras

Deep learning - deep neural networks, convolutional neural networks (CNNS), recurrent neural networks (RNNs)

Reinforcement learning - Q learning and deep Q learning approaches

Basic Python - we will use Panda and Numpy as well (we will cover the basics during implementations)

These topics are getting very hot nowadays because these learning algorithms can be used in several fields from software eeering 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: machine learning approaches are becoming more and more important even in 2020. In this course, you can learn about:

linear regression model

logistic regression model

k nearest neighbour classifier

naive Bayes classifier

support vector machines (SVMs)

random forest classifier

boosting algorithm

principle components analysis (PCA)

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: feedforward neural networks and deep neural networks are the state-of-the-art approaches in artificial intelligence in 2020. So what are the topics you will learn in this course?

deep neural networks

convolutional neural networks (CNNs)

recurrent neural networks (RNNs)

Recurrent Neural Networks and Convolutional Neural Networks and their applications such as sennt analysis or stock prices forecast

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

Tic Tac Toe game with Q learning approach and the deep Q learning approach

Thanks for joining the course, let's get started!

This course is meant for newbies who are not familiar with machine learning, deep learning, computer vision and reinforcement learning or students looking for a quick refresher



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