Data Science and Machine Learning Mathematics and Statistics

Data Science and Machine Learning Mathematics and Statistics
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