  # Machine learning has become embedded in a wide variety of day-to-day business, nonprofit, and government operations. It is all about extracting knowledge from data and the application of machine learning methods has in recent years become ubiquitous in everyday life

###### MACHINE LEARNING USING PYTHON

95000 32138 Duration | 40 hours (2 hrs/day)

ELIGIBILITY:

• Pursuing any degree

• Basic programming knowledge and mathematical knowledge

HIGHLIGHTS

• End of the Course the learners can build a Machine Learning model by themselves

• Hands-on practical sessions

• Real-world case studies

• Interactive visualizations of algorithms

• Module wise Assessments enable instructor/Learner to track & judge Learner’s knowledge level

CONTENTS

1. Introduction to Machine Learning

• What is Machine Learning?

• Applications of Machine Learning

• Types of Machine Learning Algorithms

• Steps to Solve a Machine Learning Problem

2. Introduction to Python

• Installation of Python using Anaconda Navigator

• Python Overview

• Python Keywords

• Python Identifiers

• Data types

• Type Conversion

• Input and Output Statements

• Operators

• Python control structures

• Selection Statement / Decision Making Statement

• Looping Statement / Iterative Statement

• Python Modules

3. Vectors, Matrices, and Arrays

• Creating a Vector

• Creating a Matrix

• Creating a Sparse Matrix

• Selecting Elements

• Describing a Matrix

• Applying Operations to Elements

• Finding the Maximum and Minimum Values

• Calculating the Average, Variance, and Standard Deviation

• Reshaping Arrays

• Transposing a Vector or Matrix

• Flattening a Matrix

• Finding the Rank of a Matrix

• Calculating the Determinant

• Getting the Diagonal of a Matrix

• Finding Eigenvalues and Eigenvectors

• Calculating Dot Products

• Multiplying Matrices

• Inverting a Matrix

5. Data Wrangling

• Creating a Data Frame

• Describing the Data

• Navigating DataFrames

• Selecting Rows Based on Conditionals

• Replacing Values

• Renaming Columns

• Finding the Minimum, Maximum, Sum, Average, and Count

• Finding Unique Values

• Handling Missing Values

• Deleting a Column

• Deleting a Row

• Dropping Duplicate Rows

• Grouping Rows by Values

• Applying a Function

• Concatenating DataFrames

• Merging DataFrames

CONTENTS

6. Handling Numerical Data

• Standardizing a Feature

• Normalizing Observations

• Generating Polynomial and Interaction Features

• Transforming Features

• Detecting Outliers

• Handling Outliers

• Deleting Observations with Missing Values

• Imputing Missing Values

•

7. Handling Categorical Data

• Encoding Nominal Categorical Features

• Encoding Ordinal Categorical Features

• Encoding Dictionaries of Features

• Imputing Missing Class Values

• Handling Imbalanced Classes

•

8. Handling Text

• Cleaning Text

• Parsing and Cleaning HTML

• Removing Punctuation

• Tokenizing Text

• Removing Stop Words

• Stemming Words

• Tagging Parts of Speech

• Encoding Text as a Bag of Words

• Weighting Word Importance

9. Handling Dates and Times

• Introduction

• Converting Strings to Dates

• Handling Time Zones

• Selecting Dates and Times

• Breaking Up Date Data into Multiple Features

• Calculating the Difference Between Dates

• Encoding Days of the Week

• Creating a Lagged Feature

• Using Rolling Time Windows

• Handling Missing Data in Time Series

10. A Machine Learning Model using Linear Regression

11. A Machine Learning Model using Trees and Forests

12. A Machine Learning Model using K-Nearest Neighbors

13. A Machine Learning Model using Logistic Regression

14. A Machine Learning Model using Support Vector Machines

15. A Machine Learning Model using Naive Bayes

16. A Machine Learning Model using K-means Clustering

OUTCOMES

By The End Of Course The Learner Will Be Able To:

• Design Machine Learning Model

• Visualize the data

• Data Analytics

• Work with various python libraries numpy, pandas, matplotlib, seaborn and sklean