# Machine Learning Online Course

Machine Learning Course is designed in such way that it covers all the interview specific questions. Focus is on enabling candidates to clear the machine learning interviews. After each topic doubt clearing as well as Mock Interviews are organized to tell students what are the areas of improvements and where he is strong. So that he can focus more on those areas and easily clears the interview.

Below is the details of the course. If you have any query, please scroll down and you will find the contact information. Please feel free to contact us and would like to discuss further.

Course Duration: 20-25 Days based on students interaction during the class

Fee: 2000 INR

Medium: Online classes using gotomeeting

Following topics will be covered in this module.

### Introduction to the Course

• Introduction to the Machine Learning
• Types of Problems solved using Machine Learning
• Machine Learning Overview
• Supervised Learning
• Regression
• Classification
• Un-supervised Learning
• Clustering and its Techniques
• Association Rule Mining
• Dimensionality Reduction
• Re-inforcement Learning
• Machine Setup
• Python, Anaconda Jupyter Notebook and VS Code Installation

### Statistical Modeling

• Statistics for Data Science
• Measures of Central Tendencies
• Probability and Data Distributions
• Central Limit Theorem
• Hypothesis Testing
• Analysis Of Variance
• Confusion Matrix

### Exploratory Data Analysis

• Understanding variables and data types
• Separating Numerical and Categorical variables
• Understanding distribution of features
• Outliers identification and treatment
• Handling missing values
• For Numerical Variables
• For Categorical Variables

### Data Visualization

• Uni-variate Analysis
• Bi-variate Analysis
• Multi-variate Analysis

### Data Pre-processing

• Handling Missing Values
• Outlier Treatment
• Handling categorical Variables
• Feature Scaling
• Sampling Techniques
• Feature Engineering
• Splitting criteria

### Predictive Modeling

• Regression Analysis (Simple and Multiple Linear Regression)
• What is Regression Analysis?
• Covariance and Correlation
• Multi-collinearity
• Auto-correlation
• Heteroscedasticity
• Coefficient of Determination R-squared (R2_score)
• Implementing Simple & Multiple Linear Regression
• Analyzing output of regression models
• Assumptions in Linear Regression
• Capstone Project on Regression Analysis
• Logistic Regression
• Linear Regression to Logistic Regression Transformation
• Understanding mathematics behind the logistic regression
• Sigmoid function
• Understanding Maximizing the Log-likelihood
• Understanding output of Logistic Regression classifier
• Capstone Project on Logistic Regression
• Model validation: Cross Validation, ROC Curve, Confusion Matrix
• K-Nearest Neighbor Algorithm
• Understanding KNN
• How to select optimal k value
• Distance Metrics – Euclideam, Manhattan, Chebyshev
• Case Study
• Regularization
• Lasso
• Ridge
• Elastic Net
• Decision Tree
• How to interpret decision tree
• Entropy & Information Gain
• Gini Index
• Overfitting Problem and techniques to overcome the overfitting
• Cross Validation for Overfitting Problem
• Capstone Project
• Ensemble Learning
• Bagging
• Boosting
• Projects using bagging and boosting algorithms like Ada-Boost, xgBoost etc
• Random Forest
• Random Forest Algorithm
• Majority voting concept
• Hyper parameter tuning
• Capstone Project on Random forest
• Model Fine-Tuning
• Different approaches to fine tune the model
• Hyper parameter tuning

### Un-Supervised Learning

• Clustering
• What is clustering
• k-means
• Hierarchical
• db-Scan
• Implementing k-means clustering technique using python
• Set Association Rule Mining
• Dimensionality Reduction
• Principal Component Analysis – PCA

## Bonuses:

• Mock Interviews
• Real life industry use cases
• Interaction with Industry experts

Please feel free to contact for further details using the below contact form.