Go from Beginner to Super Advance Level in Machine Learning Algorithms using Python and Mathematical Insights
About This Video
In Detail
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 …
Go from Beginner to Super Advance Level in Machine Learning Algorithms using Python and Mathematical Insights
About This Video
In Detail
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.
Who this book is for
Anyone who wants to build his career in Data Science / Machine Learning.
Chapter 1 : Simple Linear Regression
Installing Anaconda using Jupyter Notebook
Introduction to Machine Learning
Types Of Machine Learning
Introduction to Linear Regression (LR)
How LR Works
Some Fun with Maths Behind LR
R Square
LR Case Study Part1
LR Case Study Part2
LR Case Study Part3
Residual Square Error (RSE)
Chapter 2 : Multiple Linear Regression
Introduction
Case study Part1
Case study Part2
Case study Part3
Adjusted R Square
Case Study Part1
Case Study Part2
Case Study Part3
Case Study Part4
Case Study Part5
Case study Part6 (RFE)
Chapter 3 : Hotstar, Netflix Real world Case Study for Multiple Linear Regression
Introduction to The Problem Statement
Playing with Data
Building Model Part1
Building Model Part2
Building Model Part3
Verification of Model
Chapter 4 : Gradient Descent
Pre-req for Gradient Descent part1
Pre-req for Gradient Descent part2
Cost Functions
Defining Cost Functions more formally
Gradient Descent
Optimisation
Closed Form Vs Gradient Descent
Gradient Descent Case Study
Chapter 5 : KNN
Introduction to Classification
Defining Classification Mathematically
Introduction To KNN
Accuracy of KNN
Effectiveness of KNN
Distance Metrics
Distance Metrics Part2
Finding K
KNN on Regression
Case Study
Classification Case1
Classification Case2
Classification Case3
Classification Case4
Chapter 6 : Model Performance Metrics
Performance Metrics Part1
Performance Metrics Part2
Performance Metrics Part3
Chapter 7 : Model Selection Part1
Model Creation Case1
Model Creation Case2
Grid Search Case Study Part1
Grid Search Case Study Part2
Chapter 8 : Naive Bayes
Introduction to Naive Bayes
Bayes Theorem
Practical Example from NB with One Column
Practical Example from NB with Multiple Column
Naive Bayes on Text Data Part1
Naive Bayes on Text Data Part2
Laplace Smoothing
Bernoulli Naive Bayes
Case Study 1
Case Study 2 Part1
Case Study 2 Part2
Chapter 9 : Logistic Regression
Introduction
Sigmoid Function
Log Odds
Case Study
Chapter 10 : Support Vector Machine (SVM)
Introduction
Hyperplane Part1
Hyperplane Part2
Maths Behind SVM
Support Vectors
Slack Variables
SVM Case Study Part1
SVM Case Study Part2
Kernel Part1
Kernel Part2
Case Study 2
Case Study 3: Part1
Case Study 3: Part2
Case Study 4
Chapter 11 : Decision Tree
Introduction
Example Of DT
Homogenity
Gini Index
Information Gain Part1
Information Gain Part2
Advantages and Disadvantages Of DT
Preventing Overlifting Issues in DT
DT Case Study Part1
DT Case Study Part2
Chapter 12 : Ensembling
Introduction to Ensembles
Bagging
Advantages
Runtime
Case study
Introduction to Boosting
Weak Learners
Shallow Decision Tree
Adaboost Part1
Adaboost Part2
Adaboost Case Study
XGboost
Boosting Part1
Boosting Part2
Xgboost Algorithm
Case Study Part1
Case Study Part2
Case Study Part3
Chapter 13 : Model Selection Part2
Model Selection Part1
Model Selection Part2
Model Selection Part3
Chapter 14 : Unsupervised Learning
Introduction to Clustering
Segmentation
Kmeans
Maths Behind Kmeans
More Maths
Kmeans Plus
Value of K
Hopkins Test
Case Study Part1
Case Study Part2
More on Segmentation
Heirarchical Clustering
Case Study
Chapter 15 : Dimension Reduction
Introduction
PCA
Maths Behind PCA
Case Study Part1
Case Study Part2
Chapter 16 : Advanced Machine Learning Algorithms
Introduction
Example Part1
Example Part2
Optimal Solution
Case Study
Regularization
Ridge and Lasso
Case Study
Model Selection
Adjusted R Square
Chapter 17 : Deep Learning
Expectations
Introduction
History
Perceptron
Multi Layered Perceptron
Neural Network Playground
Chapter 18 : Project - Medical Treatment
Introduction to Problem Statement
Playing with Data
Translating the Problem into Machine Learning World
Dealing with Text Data
Train, Test and Cross Validation Split
Understanding Evaluation Matrix: Log Loss
Building a Worst Model
Evaluating a Worst ML Model
First Categorical column Analysis
Response Encoding and One Hot Encoder
Laplace Smoothing and Calibrated classifier
Significance of first categorical column
Second Categorical column
Third Categorical column
Data pre-processing before building machine learning model
Building Machine Learning model Part1
Building Machine Learning model Part2
Building Machine Learning model Part3
Building Machine Learning model Part4
Building Machine Learning model Part5
Building Machine Learning model Part6
Chapter 19 : Project - Quora Project
Quora Introduction
Quora Data
Quora Understanding ML
Quora Data Distribution
Quora Datalist
Quora Basic Feature Engineering
Quora Text
Advanced Feature Engineering Part1
Advanced Feature Engineering Part2
Advanced Feature Engineering Part3
Advanced Feature Engineering Part4
Quora Advance Feature Analysis
Featuring Text Data with TF-IDF Weighted Word2Vec
Building Machine Learning Models - Part 1
Building Machine Learning Models - Part 2
Chapter 20 : Real World Problem - Investment Requirement Analysis for a Company
Investment Project Brief
Investment Project_Data Cleaning Part 1
Investment Project_Data Cleaning - II Part 2
Investment Project_Funding_Country_Sector Analysis Part 1
Investment Project_Funding_Country_Sector Analysis Part 2
Chapter 21 : Loan Analysis Project
Problem Statement
Lending Club Default Analysis - Data Understanding and Data Cleaning
Data Analysis - Univariate Bivariate Analysis
Segmented Univariate Analysis
Chapter 22 : Car Project
Problem Statement
Data Understanding and Exploration
Data Cleaning Data Preparation
Model Building and Evaluation
Final Model Evaluation
Chapter 23 : Stack Overflow Project - Facebook Recruitment
Problem Statement
Performance Metric
Hamming Loss
Analysis of Tags
Problem - Multi Label Part1
Problem - Multi Label Part2
Problem_Apply Logistic Regression with OnevsRest Classifier
Problem_Final