Introduction to Programming and Python Basics

• What is programming? Introduction to Python.
• Installing Python and an Integrated Development Environment (IDE).

Python Fundamentals

• Writing and running your first Python program (Hello World!).
• Understanding variables and data types. integers, floating-point numbers, strings.

Input and Output

• Basic functions. type(), str(), int(), float(), and round()
• Basic input and output using `input()` and `print()`.

Decision Making and Loops

• Conditional statements. `if`, `elif`, and `else`.
• Introduction to Boolean logic.
• Looping concepts. `for` and `while` loops.

More on Loops and Pattern Printing

• Using loops for repetitive tasks.
• Advanced looping patterns
• Pattern printing exercises

Lists and Functions

• Working with lists. creating, indexing, slicing, and modifying.
• Introduction to functions. defining and calling functions.
• Passing arguments and returning values from functions.

Advanced Data Types

• More about strings. string methods and formatting.
• Introducing tuples and dictionaries.
• Sets and their applications.

File Handling and Exceptions

• Reading and writing files in Python.
• Handling exceptions using `try` and `except` blocks.
• Using `with` statements for better file management.

Libraries and Modules

• Introduction to Python libraries and modules.
• Using built-in modules (e.g., `math`, `random`, `datetime`).
• Exploring external libraries using `pip`.

Getting Started with Data Analysis

• Introduction to Data and Its Importance
• Overview of Data Analysis and Its Applications
• Elements of Python for Data Analysis
• Understanding Data Sources and Types
• Data Cleaning and Data Preparation

Understanding Data Tools and Libraries

• Introduction to Data Analysis Tools (Excel, Google Sheets)
• Introduction to Data Visualization
• Overview of Python Libraries for Data Analysis
• Data Analysis Using Excel
• Visualizing Data with Charts and Graphs

Exploring Data with Pandas

• Introduction to Pandas and Its Importance
• Reading and Interpreting Data Tables
• Basic Data Cleaning Techniques
• Data Filtering and Sorting
• Creating Simple Data Visualizations

Introduction to Descriptive Statistics

• Overview of Descriptive Statistics
• Understanding Measures of Central Tendency
• Exploring Measures of Dispersion
• Visualizing Data Distributions
• Practical Data Analysis Tips and Tricks

Introduction to Python for Data Analysis

• Introduction to Python for Data Analysis
• Setting Up Your Python Environment
• Basic Data Structures in Python (Lists, Dictionaries)
• Reading and Writing Data Files (CSV, Excel) with Python
• Practical Data Cleaning with Python

Data Analysis with Pandas

• Introduction to Pandas and Its Role in Data Analysis
• Reading and Displaying Data with Pandas
• Data Cleaning and Preparation with Pandas
• Data Filtering and Sorting with Pandas
• Visualizing Data Using Pandas

Introduction to Data Visualization

• Understanding Data Visualization Principles
• Basic Data Visualization with Matplotlib
• Creating Charts and Graphs in Python
• Customizing Visualizations and Adding Labels
• Practical Data Visualization Projects

Exploring Descriptive and Inferential Statistics

• Descriptive Statistics with Python
• Measures of Central Tendency and Variability
• Introduction to Probability and Inferential Statistics
• Hypothesis Testing Concepts
• Real-World Data Analysis Case Studies

Understanding Machine Learning

• Introduction to Machine Learning and Its Applications
• Types of Machine Learning (Supervised, Unsupervised)
• Introduction to Scikit-Learn
• Building a Simple Machine Learning Model
• Real-World Machine Learning Use Cases

Machine Learning Fundamentals

• Introduction to Data and Data Preprocessing
• Data Exploration and Visualization
• Data Cleaning and Handling Missing Values
• Feature Engineering and Selection
• Model Evaluation Metrics

Classification

• Introduction to Classification Problems
• Logistic Regression
• Decision Trees and Random Forests
• Support Vector Machines
• k-Nearest Neighbors (k-NN)

Regression

• Introduction to Regression Problems
• Linear Regression
• Polynomial Regression
• Ridge and Lasso Regression
• Building Regression Models

Clustering

• Introduction to Clustering
• K-Means Clustering
• Hierarchical Clustering
• DBSCAN and Other Clustering Methods
• Evaluating Clustering Performance

Dimensionality Reduction

• Dimensionality Reduction Overview
• Principal Component Analysis (PCA)
• t-Distributed Stochastic Neighbor Embedding (t-SNE)
• Implementing Dimensionality Reduction

NLP Basics

• Introduction to Natural Language Processing
• Text Preprocessing
• Text Classification
• Named Entity Recognition (NER)
• Sentiment Analysis

Advanced NLP Topics

• Word Embeddings (Word2Vec, GloVe)
• Sequence-to-Sequence Models
• Chatbots and Conversational AI
• Text Generation with Recurrent Neural Networks (RNNs)
• NLP Applications and Case Studies

Image Basics

• Introduction to Computer Vision
• Image Preprocessing and Enhancement
• Image Segmentation
• Object Detection
• Image Classification

Advanced Topics

• Convolutional Neural Networks (CNNs)
• Transfer Learning in Computer Vision
• Face Recognition
• Image Generation with Generative Adversarial Networks (GANs)
• Computer Vision Applications

Reinforcement Learning Basics

• Introduction to Reinforcement Learning
• Markov Decision Processes (MDPs)
• Q-Learning
• Deep Q-Networks (DQNs)
• Policy Gradient Methods

AI Applications

• Robotics and Autonomous Systems
• Game Playing and AlphaZero
• Autonomous Vehicles
• AI in Healthcare
• Ethical Considerations in AI

Deep Learning Fundamentals

• Introduction to Deep Learning
• Neural Networks and Backpropagation
• Activation Functions and Optimization
• Regularization Techniques
• Implementing Neural Networks

Advanced Deep Learning Topics

• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Generative Models (GANs and VAEs)
• Transfer Learning and Fine-Tuning
• Deep Learning Applications

Ethical Considerations

• AI Bias and Fairness
• Privacy and Data Security
• AI and Human Labor
• AI Regulations and Policies
• Responsible AI Development

Future Trends

• Explainable AI (XAI)
• Quantum Computing and AI
• AI in Space Exploration
• AI for Climate Change and Sustainability
• Future Frontiers of AI

• Exercises: Weekly practical coding exercises to reinforce learning.
• Activities: Interactive activities and discussions to engage with course material.
• Practice Programs: Weekly practice programming tasks to apply knowledge.
• Assignments: Regular assignments to assess understanding and skills.
• Mini-Projects: 4 Mini-projects to apply concepts and demonstrate proficiency.