**Introduction to Machine Learning & Deep Learning in Python**

English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 13 Hours | 1.82 GB

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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