**Start Date:**Oct. 10, 2020, 11:52 p.m.**End Date:**Oct. 25, 2020, 11:53 p.m.**Venue:**This is a virtual event. The link to join the event will be shared with you.

₹ 999

Description

Introduction:

Introduction to Data Science and Machine Learning

Examples & Techniques of Machine Learning

Real Life Industry Based ML Problem Statements

Python:

Python Installation Guidelines

How to use Jupyter Notebook

Introduction to Python

Python programs

Python Data Structures-List

Python Data Structures-Dictionary

Python Data Structures-Sets

Python Data Structures-Strings

Python Data Structures-Tuples

Python Numpy

Python Pandas-Data Frames

Python Pandas-Series

Python Pandas-Quick-tips

Statistics:

Basics of Statistics

Central Tendency

Covariance

Correlation

Standard Deviation

Z-Score

Data Processing:

Data processing techniques in Python

Matplotlib Python visualization library

Box Plots

Histograms

Label Encoding

One Hot Encoding

Training and Testing data

Machine Learning Module 1.

K-means

Introduction to Clustering

Understanding Income Group data set

Understanding of K-means Algorithm

Implementation of K-means in Python(Income Group)

Elbow Test Method

Dimensionality Reduction:

Principal Component Analysis(PCA)

PCA implementation in Python

Statistics: Feature Scaling:

Normalization

Standardization

Implementation of Feature scaling in Python

Decision Tree:

Understanding of Decision Tree

Identification of Root Node

Implementation in Python

Evaluation: Confusion Matrix:

Accuracy

Recall

Precision

F-Score

Module 2

Random Forest:

Understanding of Random Forest

Bagging Techniques

Implementation in Python

K-Nearest Neighbours:

Understanding of KNN algorithm

Understanding of iris dataset

Implementation in Python

Linear Regression:

Understanding of Linear Regression

Assumptions of Linear Regression

Implementation in Python

Problems in achieving accuracy of model

R-Square

Adjusted R-Square

Bias

Variance

Trade-Off between Bias and Variance

Polynomial Regression:

Understanding of Polynomial Regression

Implementation in Python

Visualization of output by changing parameters

Regularization Techniques

Ridge Regression

LASSO Regression

Elastic Net Regression

Module 3

Logistic Regression:

Understanding of Logistic Regression

Implementation in Python

Pros and Cons

Naive Bayes:

Understanding of Naives Bayes

Examples

Pros and Cons

Implementation in Python

Support Vector Machines:

Understanding of Support Vector Machines(SVM)

Understanding of different Scenarios

Pros and Cons

Implementation in Python