Introduction to Machine Learning & Deep Learning in Python

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$200 | BESTSELLER | Last Updated 4/2019
Duration: 13 hours | Video: h264, 1280x720 | Audio: AAC, 48 KHz, 2 Ch | 1.8 GB
Genre: eLearning | Language: English + Sub | 156 Lectures

Regression, Naive Bayes Classifier, Support Vector Machines, Random Forest Classifier and Deep Neural Networks

What you'll learn
Solving regression problems
Solving classification problems
Using neural networks
The most up to date machine learning techniques used by firms such as Google or Facebook
Face detection with OpenCV
TensorFlow

Requirements
Basic python

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: 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!

Who this course is for:
This course is meant for newbies who are not familiar with machine learning or students looking for a quick refresher
Screenshots

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