[2020] Machine Learning and Deep Learning Bootcamp in Python

Udemy [2020] Machine Learning and Deep Learning Bootcamp in Python

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

What you'll learn
  • 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
Requirements
  • Basic Python - we will use Panda and Numpy as well (we will cover the basics during implementations)
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:
    1. linear regression model
    2. logistic regression model
    3. k nearest neighbour classifier
    4. naive Bayes classifier
    5. support vector machines (SVMs)
    6. random forest classifier
    7. boosting algorithm
    8. 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?
    1. deep neural networks
    2. convolutional neural networks (CNNs)
    3. 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!
Who this course is for:
  • 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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TUTProfessor
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