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Testprep Training

Machine Learning Online Course

About the Course

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.


Course Curriculum

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)

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)

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

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

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

Model Performance Metrics

  • Performance Metrics Part1
  • Performance Metrics Part2
  • Performance Metrics Part3

Model Selection Part1

  • Model Creation Case1
  • Model Creation Case2
  • Grid Search Case Study Part1
  • Grid Search Case Study Part2

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

Logistic Regression

  • Introduction
  • Sigmoid Function
  • Log Odds
  • Case Study

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

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

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

Model Selection Part2

  • Model Selection Part1
  • Model Selection Part2
  • Model Selection Part3

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

Dimension Reduction

  • Introduction
  • PCA
  • Maths Behind PCA
  • Case Study Part1
  • Case Study Part2

Advanced Machine Learning Algorithms

  • Introduction
  • Example Part1
  • Example Part2
  • Optimal Solution
  • Case Study
  • Regularization
  • Ridge and Lasso
  • Case Study
  • Model Selection
  • Adjusted R Square

Deep Learning

  • Expectations
  • Introduction
  • History
  • Perceptron
  • Multi Layered Perceptron
  • Neural Network Playground

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

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

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

Loan Analysis Project

  • Problem Statement
  • Lending Club Default Analysis - Data Understanding and Data Cleaning
  • Data Analysis - Univariate & Bivariate Analysis
  • Segmented Univariate Analysis

Car Project

  • Problem Statement
  • Data Understanding and Exploration
  • Data Cleaning & Data Preparation
  • Model Building and Evaluation
  • Final Model Evaluation

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

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