Introduction to Machine Learning & Deep Learning in Python

Introduction to Machine Learning & Deep Learning in Python

 


 

 


Introduction to Machine Learning & Deep Learning in Python

English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 13 Hours | 1.82 GB
eLearning | Skill level: All Levels

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

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

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

Introduction
1 Introduction
2 Introduction to machine learning

Installations
3 Installing Anaconda
4 Installing Spyder
5 Installing Keras and TensorFlow

Linear Regression
6 Linear regression introduction
7 Linear regression theory – optimization
8 Linear regression theory – gradient descent
9 Linear regression implementation I
10 Linear regression implementation II

Logistic Regression
11 Logistic regression introduction
12 Logistic regression introduction II
13 Logistic regression example I – sigmoid function
14 Logistic regression example II- credit scoring
15 Logistic regression example III – credit scoring
16 Cross validation introduction
17 Cross validation example

K-Nearest Neighbor Classifier
18 K-nearest neighbor introduction
19 K-nearest neighbor introduction – lazy learning
20 K-nearest neighbor introduction – Euclidean-distance
21 UPDATE bias and variance
22 K-nearest neighbor implementation I
23 K-nearest neighbor implementation II
24 K-nearest neighbor implementation III

Naive Bayes Classifier
25 Naive Bayes classifier introduction I
26 Naive Bayes classifier introduction II – illustration
27 Naive Bayes classifier implementation
28 TEXT CLASSIFICATION —–
29 Text clustering – basics
30 Text clustering – inverse document frequency (TF-IDF)
31 Naive Bayes example – clustering news

Support Vector Machine (SVM)
32 Support vector machine introduction I – linear case
33 Support vector machine introduction II – non-linear case
34 Support vector machine introduction III – kernels
35 Support vector machine example I – simple
36 Support vector machine example II – iris dataset
37 Support vector machine example III – digit recognition

Decision Trees
38 Decision trees introduction – basics
39 Decision trees introduction – entropy
40 Decision trees introduction – information gain
41 Decision trees introduction – pros and cons
42 Decision trees implementation
43 Decision trees implementation II
44 The Gini-index approach

Random Forest Classifier
45 Pruning introduction
46 Bagging introduction
47 Random forest classifier introduction
48 Random forests example I – iris dataset
49 Random forests example II – credit scoring
50 Random forests example III – parameter tuning

Boosting
51 Boosting introduction – basics
52 Boosting introduction – illustration
53 Boosting introduction – equations
54 Boosting introduction – final formula
55 Boosting implementation I – iris dataset
56 Boosting implementation II -tuning
57 Boosting vs. bagging

Clustering
58 Principal component anlysis introduction
59 Hierarchical clustering example
60 Principal component analysis example
61 K-means clustering introduction I
62 K-means clustering introduction II
63 K-means clustering example
64 K-means clustering – text clustering
65 DBSCAN introduction
66 DBSCAN example
67 Hierarchical clustering introduction

Neural Networks
68 NEURAL NETWORKS INTRODUCTION —-
69 BACKPROPAGATION —-
70 Feedforward neural networks
71 Optimization – cost function
72 Simplified feedforward network
73 Feedforward neural network topology
74 The learning algorithm
75 Error calculation
76 Gradient calculation I – output layer
77 Gradient calculation II – hidden layer
78 Backpropagation
79 Axons and neurons in the human brain
80 Backpropagation II
81 Applications of neural networks I – character recognition
82 Applications of neural networks II – stock market forecast
83 Deep learning
84 IMPLEMENTATION —–
85 Building networks
86 Building networks II
87 Handling datasets
88 Neural network example I – XOR problem
89 Neural network example II – iris dataset
90 Modeling human brain
91 Learning paradigms
92 Artificial neurons – the model
93 Artificial neurons – activation functions
94 Artificial neurons – an example
95 Neural networks – the big picture
96 Applications of neural networks

Machine Learning in Finance
97 Stock market basics
98 Fetching data from Yahoo Finance
99 Predicting stock prices logistic regression
100 Predicting stock prices k-nearest neighbor
101 Predicting stock prices support vector machine
102 Predicting stock prices – conclusion

Computer Vision – Face Detection
103 Computer vision introduction
104 Face detection implementation IV – tuning the parameters
105 Viola-Jones algorithm
106 Haar-features
107 Integral images
108 Boosting in computer vision
109 Cascading
110 Face detection implementation I – installing OpenCV
111 Face detection implementation II – CascadeClassifier
112 Face detection implementation III – CascadeClassifier parameters

Deep Learning
113 Types of neural networks

Deep Neural Networks
114 Deep neural networks
115 IRIS DATASET —–
116 Multiclass classification implementation I
117 Multiclass classification implementation II
118 ARTICLE Optimizers Explained (SGD, ADAGrad, ADAM…)
119 Activation functions revisited
120 Loss functions
121 Gradient descent stochastic gradient descent
122 Hyperparameters
123 XOR PROBLEM —–
124 Deep neural network implementation I
125 Deep neural network implementation II
126 Deep neural network implementation III

Convolutional Neural Networks
127 CNN THEORY —–
128 Handwritten digit classification I
129 Handwritten digit classification II
130 Handwritten digit classification III
131 ARTICLE Regularization (L1, L2 and dropout)
132 Convolutional neural networks basics
133 Feature selection
134 Convolutional neural networks – kernel
135 Convolutional neural networks – kernel II
136 Convolutional neural networks – pooling
137 Convolutional neural networks – flattening
138 Convolutional neural networks – illustration
139 HANDWRITTEN DIGITS —–

Recurrent Neural Networks
140 RNN THEORY —–
141 Stock price prediction example III
142 Stock price prediction example IV
143 Stock price prediction example V
144 Stock price prediction example VI
145 Stock price prediction example VII
146 Why do recurrent neural networks are important
147 Recurrent neural networks basics
148 Vanishing and exploding gradients problem
149 Long-short term memory (LTSM) model
150 Gated recurrent units (GRUs)
151 STOCK MAKRET —
152 Stock price prediction example I
153 Stock price prediction example II

Course Materials (DOWNLOADS)
154 Course materials
155 House prices csv file

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