Data Science 2020 : Complete Data Science & Machine Learning

Data Science 2020 : Complete Data Science & Machine Learning
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