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Introduction to Machine Learning & Deep Learning in Python

Regression, Naive Bayes Classifier, Support Vector Machines, Random Forest Classifier and Deep Neural Networks
Instructor:
Holczer Balazs
4,562 students enrolled
English [Auto-generated]
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

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!

Introduction

1
Introduction
2
Introduction to machine learning

Installations

1
Installing Anaconda
2
Installing Spyder
3
Installing Keras and TensorFlow

Linear Regression

1
Linear regression introduction
2
Linear regression theory - optimization
3
Linear regression theory - gradient descent
4
Linear regression implementation I
5
Linear regression implementation II

Logistic Regression

1
Logistic regression introduction
2
Logistic regression introduction II
3
Logistic regression example I - sigmoid function
4
Logistic regression example II- credit scoring
5
Logistic regression example III - credit scoring
6
Cross validation introduction
7
Cross validation example

K-Nearest Neighbor Classifier

1
K-nearest neighbor introduction
2
K-nearest neighbor introduction - lazy learning
3
K-nearest neighbor introduction - Euclidean-distance
4
UPDATE: bias and variance
5
K-nearest neighbor implementation I
6
K-nearest neighbor implementation II
7
K-nearest neighbor implementation III

Naive Bayes Classifier

1
Naive Bayes classifier introduction I
2
Naive Bayes classifier introduction II - illustration
3
Naive Bayes classifier implementation
4
----- TEXT CLASSIFICATION -----
5
Text clustering - basics
6
Text clustering - inverse document frequency (TF-IDF)
7
Naive Bayes example - clustering news

Support Vector Machine (SVM)

1
Support vector machine introduction I - linear case
2
Support vector machine introduction II - non-linear case
3
Support vector machine introduction III - kernels
4
Support vector machine example I - simple
5
Support vector machine example II - iris dataset
6
Support vector machine example III - digit recognition

Decision Trees

1
Decision trees introduction - basics
2
Decision trees introduction - entropy
3
Decision trees introduction - information gain
4
Decision trees introduction - pros and cons
5
Decision trees implementation
6
Decision trees implementation II
7
The Gini-index approach

Random Forest Classifier

1
Pruning introduction
2
Bagging introduction
3
Random forest classifier introduction
4
Random forests example I - iris dataset
5
Random forests example II - credit scoring
6
Random forests example III - parameter tuning

Boosting

1
Boosting introduction - basics
2
Boosting introduction - illustration
3
Boosting introduction - equations
4
Boosting introduction - final formula
5
Boosting implementation I - iris dataset
6
Boosting implementation II -tuning
7
Boosting vs. bagging

Clustering

1
Principal component anlysis introduction
2
Principal component analysis example
3
K-means clustering introduction I
4
K-means clustering introduction II
5
K-means clustering example
6
K-means clustering - text clustering
7
DBSCAN introduction
8
DBSCAN example
9
Hierarchical clustering introduction
10
Hierarchical clustering example

Neural Networks

1
---- NEURAL NETWORKS INTRODUCTION ----
2
Axons and neurons in the human brain
3
Modeling human brain
4
Learning paradigms
5
Artificial neurons - the model
6
Artificial neurons - activation functions
7
Artificial neurons - an example
8
Neural networks - the big picture
9
Applications of neural networks
10
---- BACKPROPAGATION ----
11
Feedforward neural networks
12
Optimization - cost function
13
Simplified feedforward network
14
Feedforward neural network topology
15
The learning algorithm
16
Error calculation
17
Gradient calculation I - output layer
18
Gradient calculation II - hidden layer
19
Backpropagation
20
Backpropagation II
21
Applications of neural networks I - character recognition
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Includes

13 hours on-demand video
16 articles
Full lifetime access
Access on mobile and TV
Certificate of Completion