Machine Learning with Real World Projects

Machine Learning with Real World Projects
Machine Learning with Real World Projects
English | MP4 | AVC 1820×1080 | AAC 48KHz 2ch | 29h 47m | 7.73 GB

Go from Beginner to Super Advance Level in Machine Learning Algorithms using Python and Mathematical Insights

Want to become a good Data Scientist? Then this is a right course for you.

This course has been designed by IIT professionals who have mastered in Mathematics and Data Science. We will be covering complex theory, algorithms and coding libraries in a very simple way which can be easily grasped by any beginner as well.

We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science from beginner to advance level.

We have solved few Kaggle problems during this course and provided complete solutions so that students can easily compete in real world competition websites.

Learn

  • Master Machine Learning in Python
  • Learn to use MatplotLib for Python Plotting
  • Learn to use Numpy and Pandas for Data Analysis
  • Learn to use Seaborn for Statistical Plots
  • Learn All the Mathematics Required to understand Machine Learning Algorithms
  • Implement Machine Learning Algorithms along with Mathematic intuitions
  • Projects of Kaggle Level are included with Complete Solutions
  • Learning End to End Data Science Solutions
  • All Advanced Level Machine Learning Algorithms and Techniques like Regularisations, Boosting, Bagging and many more included
  • Learn All Statistical concepts To Make You Ninza in Machine Learning
  • Real-World Case Studies
  • Model Performance Metrics
  • Deep Learning
  • Model Selection
Table of Contents

Simple Linear Regression
1 Installing Anaconda & using Jupyter Notebook
2 Introduction to Machine Learning
3 Types Of Machine Learning
4 Introduction to Linear Regression (LR)
5 How LR Works
6 Some Fun with Maths Behind LR
7 R Square
8 LR Case Study Part1
9 LR Case Study Part2
10 LR Case Study Part3
11 Residual Square Error (RSE)

Multiple Linear Regression
12 Introduction
13 Case study Part1
14 Case study Part2
15 Case study Part3
16 Adjusted R Square
17 Case Study Part1
18 Case Study Part2
19 Case Study Part3
20 Case Study Part4
21 Case Study Part5
22 Case study Part6 (RFE)

Hotstar, Netflix Real world Case Study for Multiple Linear Regression
23 Introduction to The Problem Statement
24 Playing with Data
25 Building Model Part1
26 Building Model Part2
27 Building Model Part3
28 Verification of Model

Gradient Descent
29 Pre-req for Gradient Descent part1
30 Pre-req for Gradient Descent part2
31 Cost Functions
32 Defining Cost Functions more formally
33 Gradient Descent
34 Optimisation
35 Closed Form Vs Gradient Descent
36 Gradient Descent Case Study

KNN
37 Introduction to Classification
38 Defining Classification Mathematically
39 Introduction To KNN
40 Accuracy of KNN
41 Effectiveness of KNN
42 Distance Metrics
43 Distance Metrics Part2
44 Finding K
45 KNN on Regression
46 Case Study
47 Classification Case1
48 Classification Case2
49 Classification Case3
50 Classification Case4

Model Performance Metrics
51 Performance Metrics Part1
52 Performance Metrics Part2
53 Performance Metrics Part3

Model Selection Part1
54 Model Creation Case1
55 Model Creation Case2
56 Grid Search Case Study Part1
57 Grid Search Case Study Part2

Naive Bayes
58 Introduction to Naive Bayes
59 Bayes Theorem
60 Practical Example from NB with One Column
61 Practical Example from NB with Multiple Column
62 Naive Bayes on Text Data Part1
63 Naive Bayes on Text Data Part2
64 Laplace Smoothing
65 Bernoulli Naive Bayes
66 Case Study 1
67 Case Study 2 Part1
68 Case Study 2 Part2

Logistic Regression
69 Introduction
70 Sigmoid Function
71 Log Odds
72 Case Study

Support Vector Machine (SVM)
73 Introduction
74 Hyperplane Part1
75 Hyperplane Part2
76 Maths Behind SVM
77 Support Vectors
78 Slack Variables
79 SVM Case Study Part1
80 SVM Case Study Part2
81 Kernel Part1
82 Kernel Part2
83 Case Study 2
84 Case Study 3 – Part1
85 Case Study 3 – Part2
86 Case Study 4

Decision Tree
87 Introduction
88 Example Of DT
89 Homogenity
90 Gini Index
91 Information Gain Part1
92 Information Gain Part2
93 Advantages and Disadvantages Of DT
94 Preventing Overlifting Issues in DT
95 DT Case Study Part1
96 DT Case Study Part2

Ensembling
97 Runtime
98 Case study
99 Introduction to Boosting
100 Weak Learners
101 Shallow Decision Tree
102 Adaboost Part1
103 Adaboost Part2
104 Adaboost Case Study
105 XGboost
106 Boosting Part1
107 Boosting Part2
108 Xgboost Algorithm
109 Case Study Part1
110 Case Study Part2
111 Case Study Part3
112 Introduction to Ensembles
113 Bagging
114 Advantages

Model Selection Part2
115 Model Selection Part1
116 Model Selection Part2
117 Model Selection Part3

Unsupervised Learning
118 Introduction to Clustering
119 Segmentation
120 Kmeans
121 Maths Behind Kmeans
122 More Maths
123 Kmeans Plus
124 Value of K
125 Hopkins Test
126 Case Study Part1
127 Case Study Part2
128 More on Segmentation
129 Heirarchical Clustering
130 Case Study

Dimension Reduction
131 Introduction
132 PCA
133 Maths Behind PCA
134 Case Study Part1
135 Case Study Part2

Advanced Machine Learning Algorithms
136 Introduction
137 Example Part1
138 Example Part2
139 Optimal Solution
140 Case Study
141 Regularization
142 Ridge and Lasso
143 Case Study
144 Model Selection
145 Adjusted R Square

Deep Learning
146 Expectations
147 Introduction
148 History
149 Perceptron
150 Multi Layered Perceptron
151 Neural Network Playground

Project – Medical Treatment
152 Introduction to Problem Statement
153 Playing with Data
154 Translating the Problem into Machine Learning World
155 Dealing with Text Data
156 Train, Test and Cross Validation Split
157 Understanding Evaluation Matrix – Log Loss
158 Building a Worst Model
159 Evaluating a Worst ML Model
160 First Categorical column Analysis
161 Response Encoding and One Hot Encoder
162 Laplace Smoothing and Calibrated classifier
163 Significance of first categorical column
164 Second Categorical column
165 Third Categorical column
166 Data pre-processing before building machine learning model
167 Building Machine Learning model Part1
168 Building Machine Learning model Part2
169 Building Machine Learning model Part3
170 Building Machine Learning model Part4
171 Building Machine Learning model Part5
172 Building Machine Learning model Part6

Project – Quora Project
173 Quora Introduction
174 Quora Data
175 Quora Understanding ML
176 Quora Data Distribution
177 Quora Datalist
178 Quora Basic Feature Engineering
179 Quora Text
180 Advanced Feature Engineering Part1
181 Advanced Feature Engineering Part2
182 Advanced Feature Engineering Part3
183 Advanced Feature Engineering Part4
184 Quora Advance Feature Analysis
185 Featuring Text Data with TF-IDF Weighted Word2Vec
186 Building Machine Learning Models – Part 1
187 Building Machine Learning Models – Part 2

Real World Problem – Investment Requirement Analysis for a Company
188 Investment Project Brief
189 Investment Project Data Cleaning Part 1
190 Investment Project Data Cleaning – II Part 2
191 Investment Project Funding Country Sector Analysis Part 1
192 Investment Project Funding Country Sector Analysis Part 2

Loan Analysis Project
193 Problem Statement
194 Lending Club Default Analysis – Data Understanding and Data Cleaning
195 Data Analysis – Univariate & Bivariate Analysis
196 Segmented Univariate Analysis

Car Project
197 Problem Statement
198 Data Understanding and Exploration
199 Data Cleaning & Data Preparation
200 Model Building and Evaluation
201 Final Model Evaluation

Stack Overflow Project – Facebook Recruitment
202 Problem Statement
203 Performance Metric
204 Hamming Loss
205 Analysis of Tags
206 Problem – Multi Label Part1
207 Problem – Multi Label Part2
208 Problem Apply Logistic Regression with OnevsRest Classifier
209 Problem Final