**Data Science 2020 : Complete Data Science & Machine Learning**

English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 26 Hours | 10.7 GB

Machine Learning A-Z, Data Science, Python for Machine Learning, Math for Machine Learning, Statistics for Data Science

Data Science and Machine Learning are the hottest skills in demand but challenging to learn. Did you wish that there was one course for Data Science and Machine Learning that covers everything from Math for Machine Learning, Advance Statistics for Data Science, Data Processing, Machine Learning A-Z, Deep learning and more?

Today Data Science and Machine Learning is used in almost all the industries, including automobile, banking, healthcare, media, telecom and others.

As the Data Science and Machine Learning practioner, you will have to research and look beyond normal problems, you may need to do extensive data processing. experiment with the data using advance tools and build amazing solutions for business. However, where and how are you going to learn these skills required for Data Science and Machine Learning?

Data Science and Machine Learning require in-depth knowledge of various topics. Data Science is not just about knowing certain packages/libraries and learning how to apply them. Data Science and Machine Learning require an indepth understanding of the following skills,

- Understanding of the overall landscape of Data Science and Machine Learning
- Different types of Data Analytics, Data Architecture, Deployment characteristics of Data Science and Machine Learning projects
- Python Programming skills which is the most popular language for Data Science and Machine Learning
- Mathematics for Machine Learning including Linear Algebra, Calculus and how it is applied in Machine Learning Algorithms as well as Data Science
- Statistics and Statistical Analysis for Data Science
- Data Visualization for Data Science
- Data processing and manipulation before applying Machine Learning
- Machine Learning
- Ridge (L2), Lasso (L1) and Elasticnet Regression/ Regularization for Machine Learning
- Feature Selection and Dimensionality Reduction for Machine Learning models
- Machine Learning Model Selection using Cross Validation and Hyperparameter Tuning
- Cluster Analysis for unsupervised Machine Learning
- Deep Learning using most popular tools and technologies of today.

This Data Science and Machine Learning course has been designed considering all of the above aspects, the true Data Science and Machine Learning A-Z Course. In many Data Science and Machine Learning courses, algorithms are taught without teaching Python or such programming language. However, it is very important to understand the construct of the language in order to implement any discipline including Data Science and Machine Learning.

Also, without understanding the Mathematics and Statistics it’s impossible to understand how some of the Data Science and Machine Learning algorithms and techniques work.

Data Science and Machine Learning is a complex set of topics which are interlinked. However, we firmly believe in what Einstein once said,

“If you can not explain it simply, you have not understood it enough.”

As an instructor, I always try my level best to live up to this principle. This is one comprehensive course on Data Science and Machine Learning that teaches you everything required to learn Data Science and Machine Learning using the simplest examples with great depth.

As you will see from the preview lectures, some of the most complex topics are explained in a simple language.

Some of the key skills you will learn,

Python Programming

Python has been ranked as the #1 language for Data Science and Machine Learning. It is easy to use and is rich with various libraries and functions required for performing various tasks for Data Science and Machine Learning. Moreover, it is the most preferred and default language of use for many Deep Learning frameworks including Tensorflow and Keras.

Advance Mathematics for Machine Learning

Mathematics is the very basis for Data Science in general and Machine Learning in particular. Without understanding the meanings of Vectors, Matrices, their operations as well as understanding Calculus, it is not possible to understand the foundation of the Data Science and Machine Learning. Gradient Descent which forms the very basis of Neural Network and Machine Learning is built upon the basics of Calculus and Derivatives.

Advance Statistics for Data Science

It is not enough to know only mean, median, mode etc. The advance techniques of Data Science and Machine Learning such as Feature Selection, Dimensionality Reduction using PCA are all based on advance inferential statistics of Distributions and Statistical Significance. It also helps us understanding the data behavior and then apply an appropriate machine learning technique to get the best result from various techniques of Data Science and Machine Learning.

Data Visualization

As they say, picture is worth a thousand words. Data Visualization is one of the key techniques of Data Science and Machine Learning and is used for Exploratory Data Analysis. In that, we visually analyse the data to identify the patterns and trends. We are going to learn how to create various plots and charts as well as how to analyse them for all the practical purposes. Feature Selection plays a key role in Machine Learning and Data Visualisation is key for it.

Data Processing

Data Science require extensive data processing. Data Science and Machine Learning practitioners spend more than 2/3rd of the time processing and analysing the data. Data can be noisy and is never in the best shape and form. Data Processing is one of the key disciplines of Data Science and Machine Learning to get the best results. We will be using Pandas which is the most popular library for data processing in Python and various other libraries to read, analyse, process and clean the data.

Machine Learning

The heart and soul of Data Science is the predictive ability provided by the algorithms from Machine Learning and Deep Learning. Machine Learning takes the overall discipline of Data Science ahead of others. We will combine everything we would learn from the previous sections and build various machine learning models. The key aspects of the Machine Learning is not just about the algorithms but also understanding various parameters used by Machine Learning algorithms. We will understand all the key parameters and how their values impact the outcome so that you can build the best machine learning models.

Feature Selection and Dimensionality Reduction

In case you wonder, what makes a good data scientists, then this section is the answer. A good Data Science and Machine Learning practitioner does not just use libraries and code few lines. She will analyse every feature of the data objectively and choose the most relevant ones based on statistical analysis. We will learn how to reduce the number of features as well as how we can retain the value in the data when we practice and build various machine learning models after applying the principles of Feature Selection and Dimensionality Reduction using PCA.

Deep Learning

You can not become a good Data Science and Machine Learning practitioner, if you do not know how to build powerful neural network. Deep Learning can be said to be another kind of Machine Learning with great power and flexibility. After Learning Machine Learning, we are going to learn some key fundamentals of Deep Learning and build a solid foundation first. We will then use Keras and Tensorflow which are the most popular Deep Learning frameworks in the world.

Kaggle Project

As an aspiring Data Scientists, we always wish to work on Kaggle project for Machine Learning and achieve good results. I have spent huge effort and time in making sure you understand the overall process of performing a real Data Science and Machine Learning project. This is going to be a good Machine Learning challenge for you.

What you’ll learn

- Learn Complete Data Science skillset required to be a Data Scientist with all the advance concepts
- Master Python Programming from Basics to advance as required for Data Science and Machine Learning
- Learn complete Mathematics of Linear Algebra, Calculus, Vectors, Matrices for Data Science and Machine Learning.
- Become an expert in Statistics including Descriptive and Inferential Statistics.
- Learn how to analyse the data using data visualization with all the necessary charts and plots
- Perform data Processing using Pandas and ScikitLearn
- Master Regression with all its parameters and assumptions
- Solve a Kaggle project and see how to achieve top 1 percentile
- Learn various classification algorithms such as Logistic Regression, Decision Tree, Random Forest, Support Vector Machines
- Get complete understanding of deep learning using Keras and Tensorflow
- Become the Pro by learning Feature Selection and Dimensionality Reduction

**Table of Contents**

**Introduction**

1 Course Introduction

2 How to Claim your FREE Gift

3 Download Course Material

4 Udemy Reviews – Important Message

**Part 1 Essential Python Programming —**

5 Install Anaconda Spyder

6 Hands On – Hello Python and Know the environment

7 Hands On – Variable Types and Operators

8 Hands On – Decision Making – If-Else

9 Python Loops explained

10 Hands On – While Loops

11 Hands On – For Loops

12 Python Lists Explained

13 Hands On – Lists Basic Operations

14 Hands On – Lists Operations Part 2

15 Multidimensional Lists Explained

16 Hands On – Slicing Multidimensional lists

17 Hands On – Python Tuples

18 Python Dictionary Explained

19 Hands On – Access the Dictionary Data

20 Hands On – Dictionary Methods and functions

21 File processing – Open and Read files

22 File Processing – Process Data and Write to Files

23 File Processing – Process Data using Loops

24 Project 1 – Calculate the average temperature per city

25 Solution – Project 1 calculate the average temperature per city

**Part 2 Essential Mathematics —**

26 What you will learn in this Part

27 Algebraic Equations

28 Exponents and Logs

29 Polynomial Equations

30 Factoring

31 Quadratic Equations

32 Functions

33 Calculus Foundation

34 Rate of Change

35 Limits

36 Differentiation and Derivatives

37 Derivative Rules and Operations

38 Double Derivatives and finding Maxima

39 Double Derivatives example

40 Partial Derivatives and Gradient Descent

41 Integration and Area Under the Curve

42 Vector Basics – What is a Vector and vector operations

43 Vector Arithmetic

44 Matrix Foundation

45 Matrix Arithmetic

46 Identity Inverse Determinant and Transpose Matrix

47 Matrix Transformation

48 Change of Basis and Axis using Matrix Transformation

49 Eigenvalues and Eigenvectors

50 Understanding probability in simple terms

51 Probability Terms

52 Conditional Probability

53 Random Processes and Random Variables

**What is Data Science and Machine Learning**

54 Need for Data Science and Machine Learning

55 Types of Analytics

56 Decoding Data Science and Machine Learning

57 Data Science Project Lifecycle Part 1

58 Data Science Project Lifecycle Part 2

59 Data Science Project Lifecycle Part 3

60 Data Science Project Lifecycle Part 4

61 What does a Data Scientist do and the skills required

**Part 3 Essential Statistics —**

62 What you will learn in this part

**Descriptive Statistics**

63 What is Data Understanding the Data and its elements

64 Measure of Central Tendency using Mean Median mode

65 Measure of Dispersion using Standard Deviation and variance

66 Hands on – Get Statistical Summary

67 Measure of Dispersion using Percentile Range and IQR

**Data Visualization**

68 Importance of Data Visualization

69 Data Visualization – Frequency Table Histogram and Bar Chart

70 Understanding Boxplot for Numerical Data

71 What is a Plot

72 Hands On – Create Line Plots

73 Hands On – Understand Plot Figure Menu

74 Hands On – Create your first Bar Chart

75 Hands On – Create Histogram of Data

76 Hands On – Plotting Boxplot

77 Data Visualization for Categorical Data

78 Hands On – Pie Charts Part 1

79 Hands On – Pie Charts Part 2

80 Hands On – Scatter Plots

81 Hands On – MatplotLib Figures for creating multiple plots

82 Hands On – Subplots for plotting multiple plots in one figure

83 Hands On – Customization of Plot elements Part 1

84 Hands On – Customization of Plot elements Part 2

85 Hands On – Customization of Plot elements Part 3

86 Hands On – Customization of Plot elements Part 4

87 Claim your reward now

**Inferential Statistics Distributions and Hypothesis**

88 Understand Population Vs Samples

89 What is a Sample Bias

90 What is Correlation and Causality

91 What is Covariance and Covariance Matrix

92 Probability Density Function and Distributions

93 Normal Distributions

94 Standard Normal Distributions

95 Sampling Distributions

96 Central Limit Theorem

97 Confidence Interval – Part 1

98 Confidence Interval – Part 2

99 What is Hypothesis and Null Vs Alternate Hypothesis

100 What is Statistical Significance

101 Hypothesis Testing Examples

**Part 4 Data Pre-Processing —**

102 Hands On – Import Library to Read and Slice the data

103 Hands On – Understand the data you are dealing with

104 Hands On – Handling Missing Values

105 Label-Encoding for Categorical Data

106 Hands On Label Encoding

107 Hot-Encoding for Categorical Data Explained

108 Hands On – Hot-Encoding for Categorical Data

109 Data normalization – Understand the reasons

110 Hands On – Data Normalization using Standard Scaler

111 Hands On – Data Normalization using minmax

112 Train and Test Data Split explained

113 Hands On – Train and Test Data Split

**Part 5 Regression ——–**

114 What you will learn in this section

**Simple Linear Regression**

115 What is Simple Linear Regression

116 Ordinary Least Square and Regression Errors

117 Project 2 – Data Processing

118 Project 2 – Train and Test Model

119 Test the model and Predict Y Values

120 Project 2 – R-Squared and its Importance

121 Project 2 – Score and Get coefficients

122 Project 2 – Calculate RMSE (Root Mean Squared Error)

123 Project 2 – Plot the predictions

**Multiple Linear Regression**

124 Understanding the Multiple Linear Regression

125 Project 3 – Multiple Linear Regression Predictions

126 Issues to deal with for Multiple Linear Regression

127 Degrees of Freedom

128 Adjusted R-Squared

129 Assumptions of Multiple Linear Regression

130 Linearity and Multicollinearity Assumption

131 Assumption of Autocorrelation

132 Hands on – Plot Autocorrelation

133 Hands on – Create shifted or TimeLag Data

134 Endogeneity Assumption

135 Normality of Residuals

136 Assumption of Homoscadasticity

137 Dummy Variable trap

**Project 4 – Kaggle Bike Demand Predictions**

138 Lets understand the problem

139 Steps required to solve the problem

140 Read and Prepare Data

141 Basic Analysis of Data

142 Data Visualization of the Continuous Variables

143 Data Visualization of the Categorical Variables

144 Summarize Data Visualization Findings

145 Check for Outliers

146 Test the Multicollinearity Assumption

147 Test Auto-correlation in Demand

148 Solving the problem of Normality

149 Solving the problem of Autocorrelation

150 Create Dummy Variables

151 Train-Test Split for the Time-Series Data

152 Create the Model and measure RMSE

153 Calculate and measure RMSLE for Kaggle

**Part 6 Classification ———**

154 What you will learn in this section

**Logistic Regression**

155 What is Logistic Regression

156 Project 5 – Predict Loan Approval Problem Understanding

157 Project 5 – Predict Loan Approval Part 1

158 Project 5 – Predict Loan Approval Part 2

159 Project 5 – Predict Loan Approval Part 3

160 Project 5 – Predict Loan Approval – Stratification

161 Project 5 – Predict Loan Approval – Build Logistic Regressor

162 Project 5 – Predict Loan Approval – Confusion Martix

163 Create and Analyse Confusion Matrix

**Support Vector Machines (SVM)**

164 Common Sensical Intuition of SVM

165 Mathematical Intuition of SVM Part 1

166 Mathematical Intuition of SVM Part 2

167 Hands on – Simple Implementation of SVM

168 SVM Kernel Functions Part 1

169 SVM Kernel Functions Part 2

170 SVM Kernel Function Types

171 Project 6 – IRIS Classification Problem

172 Project 6 – Data Processing

173 Project 6 – Train and create Model

174 Project 6 – Multiple Model Creation and comparison

**Decision Trees**

175 Intuition Behind Decision Trees

176 Project 7 – Adult Income Prediction Problem Understanding

177 Project 7 – Data Processing

178 Project 7 – Split data and Import Classifier

179 Project 7 – Decision Trees – Parameters Part 1

180 Project 7 – Decision Trees – Parameters Part 2

181 Project 7 – Run and Evaluate Model

**Random Forest**

182 Ensemble Learning and Random Forests

183 Bagging and Boosting

184 Hands on – Implement Random Forest

**Evaluate Classification Models**

185 Need for Evaluation and Accuracy Paradox

186 Classification Evaluation Measures

187 Hands on – Evaluation Metrics for Loan Prediction projects

188 What is Threshold and Adjusting Thresholds

189 Hands on – Adjusting Thresholds

190 Hands On – AUC ROC Curve using Python

191 Drawing the AUC ROC Curve

**Part 7 Feature Selection ——**

192 What You will learn in this Part

**Univariate Feature Selection**

193 Feature Selection Importance

194 What is Univariate Feature Selection

195 F-Test for Regression and Classification

196 Hands on F-test – Problem Statement

197 Hands On F-test – Regression without feature selection

198 Hands on F-test – Print and analyse Pvalues

199 Hands on F-test – Compare Results with and without Feature Selection

200 Chi-Squared Intuition

201 Scikitlearn – What are Feature Selection Transforms

202 Hands on – SelectKBest Part 1

203 Hands on – SelectKBest Part 2

204 Hands on – SelectPercentile

205 Hands on – Generic Univariate Select

**Recursive Feature Elimination**

206 What is Recursive Feature Elimination (RFE)

207 Project 8 – Bank Telemarketing Predictions Problem Understanding

208 Project 8 – Build Prediction model without RFE

209 Project 8 – Configure RFE and Compare results

210 Project 8 – Get Feature Importance Score

**Part 8 Dimensionality Reduction —**

211 Why to reduce dimensions and Importance of PCA

212 Mathematical Intuition of PCA and Steps to calculate PCA

213 Project 9 – Model Implementation without PCA

214 Project 9 – Convert the Dimensions to PCA

215 Project 9 – Compare results after PCA Implementation

**Part 9 – Regularization —-**

216 Regularization Introduction

217 What is Bias Variance Trade-off

218 Ridge Regression or L2 Penalty

219 Hands on – Implement Ridge Regression

220 Hands on – Plot Ridge Regression Line

221 Hands On – Effect of LambdaAlpha

222 Note about attached code

223 Lasso Regression or L1 Penalty – Hands on

224 Part 1 – L1 and L2 for Multicollinearity and Feature Selection

225 Part 2 – L1 and L2 for Multicollinearity and Feature Selection

226 Part 3 – L1 and L2 for Multicollinearity and Feature Selection

227 Elasticnet Regularization

**Part 10 – Model Selection —–**

228 Model Selection Introduction

**Cross Validation for Model Selection**

229 What is Cross Validation

230 How Cross Validation Works

231 Hands On – Prepare for Cross Validation

232 Hands On – Parameter and implementation of Cross Validation

233 Hands On – Understand the results of Cross Validation

234 Hands On – Analyse the Result

**Hyperparameter Tuning for Model Selection**

235 What is Hyperparameter Tuning

236 Grid Search and Randomized Search Approach

237 Part 1 – GridSearchCV Parameters Explained

238 Part 2 – Create GirdSearchCV Object

239 Part 3 – Fit data to GridSearchCV

240 Part 4 – Understand GridSearchCV Results

241 Part 5 – GridSearchCV using Logistic Regression

242 Part 6 – GridSearchCV using Support Vector

243 Part 7 – Select Best Model

244 Part 8 – Randomized Search

245 Model Selection Summary

**Part 11 Deep Learning —-**

246 What is Neuron and Artificial Neural Network

247 How Artificial Neural Network works

248 What is Keras and Tensorflow

249 What is a Tensor in Tensorflow

250 Installing Keras backend and Tensorflow

251 Keras Model Building and Steps

252 Layers – Overview and Parameters

253 Activation Functions

254 Layers – Softmax Activation Function

255 What is a Loss Function

256 Cross Entropy Loss Functions

257 Optimization – What is it

258 Optimization – Gradient Descent

259 Optimization – Stochastic Gradient Descent

260 Optimization – SGD with Momentum

261 Optimization – SGD with Exponential Moving Average

262 Optimization – Adagrad and RMSProp for Learning rate decay

263 Optimization – Adam

264 Initializers – Vanishing and Exploding Gradient Problem

265 Layers – Initializers explained

266 Project 10 – Understand the Problem

267 Project 10 – Read and process the data

268 Project 10 – Define the Keras Neural Network Model

269 Project 10 – Compile the Keras Neural Network Model

270 Project 10 – Evaluate the result

**Part 12 – Clustering or Cluster Analysis —-**

271 What is Clustering

272 How the clusters are formed

273 Project 11 – Problem Understanding

274 Project 11 – Get Visualize and Normalize the data

275 Project 11 – Import KMeans and Understand Parameters

276 Project 11 – Understanding KMeans Initialization Method

277 Project 11 – Create Clusters

278 Project 11 -Visualize and create different number of clusters

279 Understand Elbow Method to Decide number of Cluster

280 Project 11 – Implement Elbow Method

281 How to use clustering for business

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