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[2020] Machine Learning and Deep Learning Bootcamp in Python, Machine Learning models, Neural Networks, Deep Learning and Reinforcement Learning Approaches in Keras and TensorFlow
Created by Holczer Balazs
Holczer Balazs
English
English [Auto]
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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: 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 sentiment 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!
