Artificial Intelligence Learning Path – Python Programming & SQL

Core Python Programming

• Introduction to Data Science using Python
• Python Basic Constructs

Assignment 1: Basic Python Programs

• Data Types in Python

Assignment 2: Data Types and Functions

• Conditional Statements

Assignment 3: Conditional Logic

• Iterative Statements

Assignment 4: Iterative Logic

• Functions in Python

Assignment 5: Miscellaneous

• Object-Oriented Programming (OOP) in Python
• File Handling & Exception Handling

Assignment 6: Classes and Objects

Python Scripting

• Basic Python Scripts
• GUI Applications
• PyGames Scripts
• API Scripts
• Image Processing Scripts
• Automation Scripts

Assignment 7: Python Scripts Programming

SQL & Database Integration

• Databases and SQL Fundamentals

Project 1: Creating Databases and Operations

Project 2: Handling Databases and CRUD Operations using Python

Data Structures & Algorithms

• Lists, Tuples, Sets, Frozen Sets, Strings, Dictionaries, Matrices, Byte Arrays
• Linked Lists, Stack, Queue, Priority Queue, Heap
• Binary Trees, Tree Traversal, Binary Search Trees
• Graphs and Graph Traversal
• Recursion, Dynamic Programming
• Searching and Sorting Algorithms

Assignment 1: Applying Data Structures & Algorithms

Advanced Python Concepts

• Recursive Functions, Iterators and Iterables, Generators
• Lambda Functions, Filter, Reduce, Map, Zip
• Decorators, Memoization
• List Comprehensions, Currying Functions
• Regular Expressions (Basic and Advanced)
• Extended Keyword Arguments
• Testing with DocTest, UnitTest, and PyTest
• Closures and Iterator Protocol
• Static and Class Methods
• Inheritance, Encapsulation, Operator Overloading
• Anonymous Functions, Python Packages

Assignment 2: Advanced Python Programming

Project: Flexible Project

• Probability

Assignment 1: Probability Questions

• Descriptive Statistics
• Inferential Statistics

Assignment 2: Applying Statistics using SciPy

• Numerical Computing with NumPy
• Data Manipulation using Pandas
• Data Visualization with Matplotlib and Seaborn
• Data Preprocessing

Assignment 3: Data Visualization using Automobile or Titanic Dataset

Assignment 4: Data Analysis using Pandas, Matplotlib, and Seaborn (2

• Datasets)
• Introduction to Excel and Tableau

Assignment 5: Data Analysis and Visualization using Tableau Tool

• Web Scraping Using BeautifulSoup

Assignment 6: Create Dataset Using Web Scraping and Perform Data Analysis

• Project: End-to-End Project (Web Scraping, Data Preprocessing, Cleaning,
• EDA, and Visualization)

Supervised Learning

• Regression: Linear, Multiple, Polynomial, Regularization (Ridge and Lasso)

Assignment 1: Regression Analysis

• Classification: Logistic Regression, K-Nearest Neighbours, Decision Trees,
• Naïve Bayes, Support Vector Machines
• Ensemble Techniques: Random Forests, Gradient Boosting Machine

Assignment 2: Classification Analysis

Assignment 3: Image Classification

Unsupervised Learning

• Clustering
• Principal Component Analysis (PCA)

Assignment 4: Clustering

• Assignments: Regression, Classification (Binary and Multiclass), Image
• Classification, Clustering
• Project: Credit Card Fraud Detection

• Introduction to Deep Learning and Neural Networks
• Introduction to Linear Algebra
• Artificial Neural Networks (ANN)

Assignment 1: Image Classification using ANN and Regression using ANN

• Neurons, Layers, Activation Functions
• Convolutional Neural Networks (CNN): Convolution Operation, Pooling,
• Padding, Strides

Assignment 2: Image Classification (CIFAR-10)

Assignment 3: Image Classification (Cats & Dogs)

• Transfer Learning: LeNet-5, AlexNet, VGG16/19, ResNet, Inception V3

Assignment 4: Neural Style Transfer

• Recurrent Neural Networks (RNN), LSTM, GRU

Assignment 5: Text Preprocessing

Assignment 6: Movie Review Analysis, Sentiment Analysis

• Computer Vision and Natural Language Processing using CNN & RNN
• Assignments: Image Classification, Transfer Learning, Neural Style Transfer,
• Sentiment Analysis
• Projects:
• 1. COVID Detection using X-Ray Images
• 2. Flexible Project

• Introduction to Data Science using R
• Installation and Introduction to R Studio
• Variables and Data Types in R
• Operators, Statements, and Expressions
• Conditional and Iterative Statements
• Functions
• Data Manipulation using dplyr
• Data Visualization using ggplot
• Time Series Forecasting
• Assignment: R Programming
• Project: Wine Quality Prediction

Tableau

• Introduction and Architecture
• Connecting to Different Data Sources
• Data Modeling
• Tableau Calculations
• Various Charts
• Reports and Dashboards

Power BI

• Introduction and Components
• Power BI Architecture
• Connecting to Different Data Sources
• Data Modeling
• Reports and Charts
• DAX (Data Analysis Expressions)
• Dashboards
• Projects: Two Flexible Problem Statements (Tableau & Power BI)

Capstone Projects (Industry Specific)

• 1. COVID-19 Detection (Healthcare)
• 2. Real-Time Face Detection & Recognition System (Transportation)
• 3. Sentiment Analysis (E-Commerce)
• 4. Flexible Project

Generative AI

• Content to be shared soon (aligned with ongoing industry research and
• hiring trends)
• Final Doubts and Revision Session

Additional Topics

• Linear Algebra
• Web Scraping
• Advanced Excel
• Frameworks
• Deployment Interfaces

Complementary Perks

• AI Customized Career Guidance
• Unique and Social Good Projects
• AI Feedback Mock Sessions
• Interview Preparation Tips
• Resume Review and Optimization
• LinkedIn and GitHub Profile Optimization