This course is part of multiple programs

This course can be applied to multiple Specializations or Professional programs. Completing this course will count towards your learning in any of the following programs

• Data Science A-Z Course
• Deep Learning A-Z Course
• Artificial Intelligence A-Z Course

Pre-skills

No Pre-skills required for joining this course, any learner can join the course.

Course Curriculum

Module -1 – Introduction to Artificial Intelligence – 4 Hours

• Artificial Intelligence & Machine Learning Introduction
• Who uses AI?
• AI for Banking & Finance, Manufacturing, Healthcare, Retail and Supply Chain
• AI v/s ML v/s DL and Data Science
• Typical applications of Machine Learning for optimizing IT Operations
• Supervised & Unsupervised Learning
• Reinforcement Learning
• Regression & Classification Problems
• Clustering and Anomaly Detection
• Recommendation System
• What makes a Machine Learning Expert?
• What to learn to become a Machine Learning Developer?

Module 2 – Python Programming Basics – 3 Hours

• Getting started with Python
• What is Python?
• Installing Anaconda
• Variables, and Data Structure
• List, tuples and dictionary
• Control Structure
• Functions in python
• Lambda functions
• Object Oriented Programming
• Modules
• Using Packages
• Os package
• time and datetime
• File Handling in Python
• Miscellaneous Functions in python 

Module 3 – Statistics for Data Science – 4 Hours

Introduction to Statistics

• Population and Sample  
• Descriptive Statistics v/s Inferential Statistics
• Types of variable
• Categorical and Continuous Data
• Ratio and Interval
• Nominal and Ordinal Data  

Descriptive Statistics

• Measure of Central Tendency – Mean, Mode and Median
• Percentile and Quartile
• Measure of Spread – IQR, Variance and Standard Deviation
• Coefficient of Variation
• Measure of Shape – Kurtosis and Skewness
• Correlation Analysis

Inferential Statistics

• Empirical Rule & Chebyshev’s Theorem
• Z Test
•  One Sample T test, independent t test
• ANOVA – f test
• Chi Square test

Module 4 – Working with numpy & Pandas – 1 – 3 Hours

Working with Numpy

• NumPy Overview
• Properties, Purpose, and Types of ndarray
• Class and Attributes of ndarray Object
• Basic Operations: Concept and Examples
• Accessing Array
• Elements: Indexing, Slicing, Iteration, Indexing with Boolean Arrays
• Shape Manipulation & Broadcasting
• Linear Algebra using numpy
• Stacking and resizing the array
• random numbers using numpy

Working with Pandas

• Data Structures
• Series, DataFrame & Panel
• DataFrame basic properties
• Importing excel sheets, csv files, executing sql queries
• Importing and exporting json files
• Data Selection and Filtering
• Selection of columns and rows
• Filtering Dataframes
• Filtering – AND operaton and OR operation

Module 5 – Working with numpy and Pandas – 2 – 4 Hours

Working with Pandas

• Data Cleaning
• Handling Duplicates
• Handling unusual values
• handling missing values
• Finding unique values
• Descriptive Analysis with pandas
• Creating new features
• Creating new categorical features from continuous variable
• combining multiple dataframes
• groupby operations
• groupby statistical Analysis
• Apply method
• String Manipulation 

Module 6 – Data Visualization – 3 Hours

Basic Visualization with matplotlib

• Matplotlib Features
• Line Properties
• Plot with (x, y)
• Controlling Line Patterns and Colors
• Set Axis, Labels, and Legend Properties
• Alpha and Annotation
• Multiple PlotsSubplots

Advance visualization using seaborn

• Types of Plots and Seaborn
• Boxplots
• Distribution Plots
• Countplots
• Heatmaps
• Voilin plots
• Swarmplots and pointplots

Module 7 – Capstone Project – 4 Hours

Project – 1

• Data Science Standard Project
• Data Science Project Life cycle
• Project Topic
• Data Capturing
• Data Cleaning 
• Data Analytics
• Working on tools
• Data Visualization tools
• Project Report Completion

Course Features

• More than 80% hands-on session
• Project-oriented learning
• Cloud-based LMS
• Experienced Trainer
• Masterclass for Expert
• Standard reading materials
• Study resources
• Graded Quizzes
• Real-time case study-based projects
• Discussion Forum

Machine Requirement

1. Windows Machine (Windows 7 or Above) /Linux Machine
2. Only 64 Bit
3. 8 GB RAM 
4. NVIDIA Graphics Card (Recommended)