**Data Science and Machine Learning Mathematics and Statistics**

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 16.5 Hours | 5.93 GB

Learn the Mathematics, Statistics and Probability behind Data Science, Machine Learning, Artificial Intelligence!

Do you want to become a Data Scientist? Are you willing to learn Machine Learning? Well you’re at the right place!!

The average salary for a Machine Learning Engineer is $138,920 per year in the United States by Indeed.

Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to “learn” (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed ~ by Wikipedia.

Machine learning can easily consume unlimited amounts of data with timely analysis and assessment. This method helps review and adjusts your message based on recent customer interactions and behaviors. Once a model is forged from multiple data sources, it has the ability to pinpoint relevant variables. This prevents complicated integrations, while focusing only on precise and concise data feeds.

Machine learning algorithms tend to operate at expedited levels. In fact, the speed at which machine learning consumes data allows it to tap into burgeoning trends and produce real-time data and predictions

1. Churn analysis – it is imperative to detect which customers will soon abandon your brand or business. Not only should you know them in depth – but you must have the answers for questions like “Who are they? How do they behave? Why are They Leaving and What Can I do to keep them with us?”

2. Customer leads and conversion – you must understand the potential loss or gain of any and all customers. In fact, redirect your priorities and distribute business efforts and resources to prevent losses and refortify gains. A great way to do this is by reiterating the value of customers in direct correspondence or via web and mail-based campaigns.

3. Customer defections – make sure to have personalized retention plans in place to reduce or avoid customer migration. This helps increase reaction times, along with anticipating any non-related defections or leaves.

Many hospitals use this data analysis technique to predict admissions rates. Physicians are also able to predict how long patients with fatal diseases can live.

Insurance agencies across the world are also able to do the following:

Predict the types of insurance and coverage plans new customers will purchase.

Predict existing policy updates, coverage changes and the forms of insurance (such as health, life, property, flooding) that will most likely be dominant.

Predict fraudulent insurance claim volumes while establishing new solutions based on actual and artificial intelligence.

Machine learning is proactive and specifically designed for “action and reaction” industries. In fact, systems are able to quickly act upon the outputs of machine learning – making your marketing message more effective across the board.

So in this course Machine Learning, Data Science and Neural Networks + AI we will discover topics:

- Introduction
- Supervised Learning
- Bayesian Decision Theory
- Parametric Methods
- Multivariate Methods
- Dimensionality Reduction
- Clustering
- Nonparametric Methods
- Decision Trees
- McNemar’s Test
- Hypothesis Testing
- Bootstrapping
- Temporal Difference Learning
- Reinforcement Learning
- Stacked Generalization
- Combining Multiple Learners
- d-Separation
- Undirected Graphs: Markov Random Fields
- Hidden Markov Models
- Regression
- Kernel Machines
- Multiple Kernel Learning
- Normalized Basis Functions
- The Perceptron
- and much more!!

What you’ll learn

- Students will learn Introduction to Machine Learning
- They will learn what is Supervised and Unsupervised Learning
- They will learn Regression
- They will learn Bayesian Decision Theory
- They will learn Parametric Methods
- They will learn The Bayes’ Estimator
- They will learn Clustering
- They will learn Expectation-Maximization Algorithm and much more!

**Table of Contents**

**Introduction**

1 Introduction

**Introduction to the Course**

2 Introduction

3 What is Machine Learning

4 Examples of Machine Learning Applications

5 Learning Association

6 Classification

7 Regression

8 Unsupervised Learning

9 Reinforcement Learning

**Supervised Learning**

10 Supervised Learning

11 Learning a Class from Examples

12 Vapnik-Chervonenkis (VC) Dimension

13 Probably Approximately Correct (PAC) Learning

14 Noise

15 Learning Multiple Classes

16 Regression

17 Model Selection and Generalization

18 Dimensions of a Supervised Machine Learning Algorithm

**Bayesian Decision Theory**

19 Bayesian Decision Theory

20 Introduction

21 Classification

22 Losses and Risks

23 Discriminant Functions

24 Utility Theory

25 Association Rules

**Parametric Methods**

26 Parametric Methods

27 Introduction

28 Maximum Likelihood Estimation

29 Bernoulli Density

30 Multinomial Density

31 Gaussian (Normal) Density

32 Evaluating an Estimator

33 The Bayes Estimator

34 Parametric Classification

35 Regression

36 Tuning Model Complexity

37 Model Selection Procedures

**Multivariate Methods**

38 Multivariate Methods

39 Multivariate Data

40 Parameter Estimation

41 Estimation of Missing Values

42 Multivariate Normal Distribution

43 Multivariate Classification

44 Tuning Complexity

45 Discrete Features

46 Multivariate Regression

**Dimensionality Reduction**

47 Dimensionality Reduction

48 Introduction

49 Subset Selection

50 Principal Components Analysis

51 Factor Analysis

52 Multidimensional Scaling

53 Linear Discriminant Analysis

54 Locally Linear Embedding

**Clustering**

55 Clustering

56 Introduction

57 Mixture Densities

58 k-Means Clustering

59 Expectation-Maximization Algorithm

60 Mixtures of Latent Variable Models

61 Supervised Learning after Clustering

62 Hierarchical Clustering

63 Choosing the Number of Clusters

**Nonparametric Methods**

64 Nonparametric Methods

65 Introduction

66 Nonparametric Density Estimation

67 Histogram Estimator

68 Kernel Estimator

69 k-Nearest Neighbor Estimator

70 Generalization to Multivariate Data

71 Condensed Nearest Neighbor

72 Nonparametric Regression

73 Running Mean Smoother

74 Kernel Smoother

75 Running Line Smoother

76 How to Choose the Smoothing Parameter

**Decision Trees**

77 Decision Trees

78 Introduction

79 Univariate Trees

80 Classification Trees

81 Regression Trees

82 Pruning

83 Rule Extraction from Trees

84 Learning Rules from Data

85 Multivariate Trees

**Linear Discrimination**

86 Linear Discrimination

87 Introduction

88 Generalizing the Linear Model

89 Geometry of the Linear Discriminant

90 Two Classes

91 Multiple Classes

92 Pairwise Separation

93 Parametric Discrimination Revisited

94 Gradient Descent

95 Logistic Discrimination

96 Two Classes

97 Multiple Classes

98 Discrimination by Regression

**Multilayer Perceptrons**

99 Multilayer Perceptrons

100 Introduction

101 Understanding the Brain

102 Neural Networks as a Paradigm for Parallel Processing

103 The Perceptron

104 Training a Perceptron

105 Learning Boolean Functions

106 Multilayer Perceptrons

107 MLP as a Universal Approximator

108 Backpropagation Algorithm

109 Nonlinear Regression

110 Two-Class Discrimination

111 Multiclass Discrimination

112 Multiple Hidden Layers

113 Training Procedures

114 Improving Convergence

115 Over training

116 Structuring the Network

117 Hints

118 Tuning the Network Size

119 Bayesian View of Learning

120 Dimensionality Reduction

121 Learning Time

122 Time Delay Neural Networks

123 Recurrent Networks

**Local Models**

124 Local Models

125 Introduction

126 Competitive Learning

127 Online k-Means

128 Adaptive Resonance Theory

129 Self-Organizing Maps

130 Radial Basis Functions

131 Incorporating Rule-Based Knowledge

132 Normalized Basis Functions

133 Competitive Basis Functions

134 Learning Vector Quantization

135 Competitive Functions

136 Cooperative Experts

**Kernel Machines**

137 Kernel Machines

138 Introduction

139 Optimal Separating Hyperplane

140 The Nonseparable Case Soft Margin Hyperplane

141 v-SVM

142 Kernel Trick

143 Vectorial Kernels

144 Defining Kernels

145 Multiple Kernel Learning

146 Multiclass Kernel Machines

147 Kernel Machines for Regression

**Hidden Markov Models**

148 Hidden Markov Models

149 Introduction

150 Discrete Markov Processes

151 Three Basic Problems of HMMs

152 Hidden Markov Models

153 Evaluation Problem

154 Finding the State Sequence

155 Learning Model Parameters

156 Continuous Observations

157 The HMM with Input

158 Model Selection in HMM

**Combining Multiple Learners**

159 Combining Multiple Learners

160 Rationale

161 Generating Diverse Learners

162 Model Combination Schemes

163 Voting

164 Error-Correcting Output Codes

165 Bagging

166 Boosting

167 Mixture of Experts Revisited

168 Stacked Generalization

169 Fine-Tuning an Ensemble

170 Cascading

**Bayesian Estimation**

171 Bayesian Estimation

172 Introduction

173 Estimating the Parameter of a Distribution

174 Encoding Dictionaries of Features

175 Encoding Ordinal Categorical Features

176 Discrete Variables

177 Continuous Variables

178 Bayesian Estimation of the Parameters of a Function

179 Regression

180 The Use of Basis

181 Bayesian Classification

182 Gaussian Processes

**Reinforcement Learning**

183 Reinforcement Learning

184 Introduction

185 Single State Case K – Armed Bandit

186 Elements of Reinforcement Learning

187 Model-Based Learning

188 Temporal Difference Learning

189 Value Iteration

190 Exploration Strategies

191 Policy Iteration

192 Deterministic Rewards and Actions

193 Nondeterministic Rewards and Actions

194 Eligibility Traces

195 Generalization

196 Partially Observable States

197 The Setting

198 Example

**Design and Analysis of Machine**

199 Design and Analysis of Machine

200 Introduction

201 Factors Response and Strategy of Experimentation

202 Response Surface Design

203 Randomization Replication and Blocking

204 Guidelines for Machine Learning Experiments

205 Cross-Validation and Re sampling Methods

206 K-Fold Cross-Validation

207 Cross-Validation

208 Measuring Classifier Performance

209 Interval Estimation

210 Hypothesis Testing

211 Binomial Test

212 t Test

213 Comparing Two Classification Algorithms

214 K-Fold Cross-Validated Paired t Test

215 Comparing Multiple Algorithms

216 Comparison over Multiple Datasets

217 Comparing Two Algorithms

218 Multiple Algorithms

219 Bootstrapping

220 x2 cv Paired F Test

221 x2 cv Paired t Test

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