Machine Learning and Data Science with PYTHON

Course Content (Duration 5 Days – Machine Learning Data Science with Python)

DURATION: 5 DAYS Data Science with Python


Day1 | Session1 | 2 Hours

Overview of Analytics
• What is Analytics?
• Types of Business Analytics
• Descriptive Analytics
• Predictive Analytics
• Prescriptive Analytics
• Application Of Business Analytics
• Used Cases from Banking
• Popular Tools
• Role of Data Scientist
• Analytics Methodology
• Problem Definition
Installation of Anaconda Python Distribution (contd.)
Data Types with Python
Basic Operators and Functions
Key Takeaways

Day1 | Session2 | 2 Hours

Getting Started with Python
Python Basics
Demonstrating Conditional Statements
Demonstrating Loops
Python Structures
Tuple – properties, related operations, compared with a list
List – properties, related operations
Dictionary – properties, related operations
Set – properties, related operations

Day1 | Session3 | 2 Hours

Python files I/O Functions
Strings and related operations
Tuples and related operations
Lists and related operations
Dictionaries and related operations
Sets and related operations

Day1 | Session4 | 2 Hours

Function Parameters
Global Variables
Variable Scope and Returning Values
Lambda Functions
Object-Oriented Concepts
Standard Libraries
Modules Used in Python
The Import Statements
Module Search Path
Package Installation Ways
Errors and Exception Handling
Handling Multiple Exceptions

Day2| Session1 | 2 Hours

Introduction to Numpy
Activity- Sequence it Right
Demo 01-Creating and Printing an ndarray
Knowledge Check
Class and Attributes of ndarray
Basic Operations
Activity-Slice It!
Copy and Views
Mathematical Functions of Numpy

Day2| Session2 | 2 Hours

Case Study 1
Case Study 2
Key Takeaways
Introduction to SciPy
SciPy Sub Package – Integration and Optimization
Knowledge Check
SciPy sub package
Demo – Calculate Eigenvalues and Eigenvector
Knowledge Check
SciPy Sub Package – Statistics, Weave and IO
Case Study 1
Case Study 2
Key Takeaways
Introduction to Pandas
Knowledge Check

Day2| Session3 | 2 Hours

Understanding DataFrame
View and Select Data Demo
Missing Values
Data Operations
Knowledge Check
File Read and Write Support
Activity- Sequence it Right
Pandas Sql Operation
Case Study 1
Case Study 2

Day2 | Session4 | 2

Hours Key Takeaways
Case Study

Day3| Session1 | 1 Hours

Introduction to Machine Learning
• Supervised Learning
• Unsupervised Learning
Application Area
Basic Ststistics
Types of Data
Summarization Techniques
Different types of Probability Distribution
Introduction to Regression Analysis
Use of Regression Analysis—Examples
Use of Regression Analysis—Examples (contd.)
Types Regression Analysis
Simple Regression Analysis
Multiple Regression Models
Simple Linear Regression Model
Simple Linear Regression Model Explained

Correlation Between X and Y
Correlation Between X and Y (contd.)

Day3 | Session2 | 2 Hours

Method of Least Squares Regression Model
Coefficient of Multiple Determination Regression Model
Standard Error of the Estimate Regression Model
Dummy Variable Regression Model
Interaction Regression Model
Non-Linear Regression
Non-Linear Regression Models
Non-Linear Regression Models (contd.)
Non-Linear Regression Models (contd.)
Non-Linear Models to Linear Models
Algorithms for Complex Non-Linear Models
Summary and quizzes

Day3 | Session3 | 2 Hours

Introduction to Classification
Examples of Classification
Classification vs. Prediction
Classification System
Classification Process
Classification Process—Model Construction
Classification Process—Model Usage in Prediction
Issues Regarding Classification and Prediction

Day3 | Session4 | 2 Hours

Data Preparation Issues
Evaluating Classification Methods Issues
Decision Tree
Decision Tree—Dataset
Decision Tree—Dataset (contd.)
Classification Rules of Trees
Overfitting in Classification

Day4 | Session1 | 2 Hours

Tips to Find the Final Tree Size
Basic Algorithm for a Decision Tree
Statistical Measure—Information Gain
Calculating Information Gain—Example
Calculating Information Gain—Example (contd.)
Calculating Information Gain for Continuous-Value Attributes
Enhancing a Basic Tree
Decision Trees in Data Mining

Day4 | Session2 | 2 Hours

Case Study

Day4 | Session3 | 2 Hours

Nearest Neighbor Classifiers
Nearest Neighbor Classifiers (contd.)
Nearest Neighbor Classifiers (contd.)
Computing Distance and Determining Class
Choosing the Value of K
Scaling Issues in Nearest Neighbor Classification
Support Vector Machines
Advantages of Support Vector Machines
Geometric Margin in SVMs
Linear SVMs
Non-Linear SVMs

Day4 | Session4 | 2 Hours

Summary and quizzes
Introduction to Clustering
Clustering vs. Classification
Use Cases of Clustering
Clustering Models
K-means Clustering
K-means Clustering Algorithm
Pseudo Code of K-means
K-means Clustering Using R
K-means Clustering—Case Study
K-means Clustering—Case Study(contd.)
K-means Clustering—Case Study (contd.)

Day5 | Session1 | 2 Hours

Hierarchical Clustering
Hierarchical Clustering Algorithms
Requirements of Hierarchical Clustering Algorithms
Agglomerative Clustering Process
Hierarchical Clustering—Case Study
Hierarchical Clustering—Case Study (contd.)
Hierarchical Clustering—Case Study (contd.)
Hierarchical Clustering—Case Study (contd.)

Day5 | Session2 | 2 Hours

Summary and quizzes
Association Rule Mining
Application Areas of Association Rule Mining
Parameters of Interesting Relationships
Association Rules
Association Rule Strength Measures
Limitations of Support and Confidence
Apriori Algorithm
Apriori Algorithm—Example
Applying Apriori Algorithm
Step 1—Mine All Frequent Item Sets
Algorithm to Find Frequent Item Set
Finding Frequent Item Set—Example
Ordering Items
Ordering Items (contd.)

Day5 | Session3 | 2 Hours

Candidate Generation
Candidate Generation (contd.)
Candidate Generation—Example
Step 2—Generate Rules from Frequent Item Sets
Generate Rules from Frequent Item Sets—Example

Problems with Association Mining
Summary and quizzes

Day5 | Session4 | 2 Hours

Factor Analysis
a. Definition and examples
b. Factor Analysis
c. Communality
d. Rotation Of Factors
e. Implementation
f. Evaluation
Web Scrapping
Getting Started
Package: Beautiful Soup