Course Overview

This comprehensive AI bootcamp is designed to take students from beginner to advanced levels in Artificial Intelligence, with a strong foundation in Python programming. The course includes live training sessions, hands-on projects, and real-world applications, ensuring a thorough understanding of AI concepts and practices.

Course Duration

(Fees – Rs 42,000)

Total Duration: 7-8 months

  • Duration: 7-8 Months
  • Total Fee: Rs 42,000
  • Special Offer: 20% discount for the first 20 students
  • Payment Option: Rs 7,000 per month
  • Hours: Approximately 265 hours

Module 1: Introduction to Python Programming

introduction to python

  • Objectives
  • Setting up the Python Environment
  • Basic Syntax and Data Types
    • Variables and Data Types
    • Basic Operators
    • Conditional Statements

Control Flow:

  • Loops (For, While)
  • List Comprehensions

Functions and Modules:

  • Defining Functions
  • Importing Modules and Packages

Working with Data Structures:

  • Lists, Tuples, Dictionaries, and Sets

File Handling:

  • Reading and Writing Files

Error Handling and Debugging:

  • Try-Except Blocks

Module 2: Data Analysis and Visualization

Introduction to Pandas:

  • DataFrames and Series
  • Data Manipulation and Cleaning

Introduction to Data Analysis:

  • Objectives
  • Introduction to NumPy
    • Arrays and Matrix Operations
    • Mathematical Functions

Exploratory Data Analysis (EDA):

  • Descriptive Statistics
  • Data Visualization Techniques

Data Visualization:

  • Introduction to Matplotlib
  • Plotting Graphs and Charts
  • Advanced Visualization with Seaborn

Exploratory Data Analysis (EDA):

  • Descriptive Statistics
  • Data Visualization Techniques

Module 3: Introduction to Machine Learning

Introduction to Machine Learning:

  • Objectives
  • Understanding ML Concepts
    • Supervised vs Unsupervised Learning
    • Types of Algorithms

Data Preprocessing

  • Data Cleanings
  • Feature Engineering
  • Data Splitting

Supervised Learning Algorithms:

  • Linear Regression
  • Logistic Regression
  • Decision Trees and Random Forests
  • Support Vector Machines (SVM)

Model Evaluation:

  • Metrics for Regression and Classification
  • Cross-Validation

Supervised Learning Algorithms:

  • Clustering (K-Means, Hierarchical)
  • Dimensionality Reduction (PCA)

Module 4: Advanced Machine Learning

Advanced Topics in ML:

  • Objectives
  • Ensemble Methods
    • Bagging and Boosting
    • AdaBoost and Gradient Boosting

Neural Networks and Deep Learning:

  • Introduction to Neural Networks
  • Building Neural Networks with TensorFlow/Keras
  • Convolutional Neural Networks (CNNs) for Image Recognition
  • Recurrent Neural Networks (RNNs) for Sequence Data

Natural Language Processing (NLP):

  • Text Processing Techniques
  • Sentiment Analysis
  • Word Embeddings (Word2Vec, GloVe)

Time Series Analysis:

  • Time Series Forecasting Models
  • ARIMA, LSTM Networks

Module 5: AI in Practice

Practical AI Applications:

  • Objectivese
  • AI in Healthcare
  • AI in Finance
  • AI in Marketing

Building AI Projects:

  • Project Planning
  • Data Collection and Preparation
  • Model Development and Deployment

Ethics and Future of AI:

  • Ethical Considerations in AI
  • Future Trends in AI

Capstone Project:

  • End-to-End AI Project
  • Presentation and Review

Additional Features:

  • Weekly Live Sessions: Interactive live classes for theoretical and practical learning.
  • Hands-on Projects: Real-world projects to apply learned concepts.
  • Mentorship: One-on-one mentorship and guidance from industry experts.
  • Discussion Forums: Comprehensive study materials and resources.
  • Resources: 265 hours total
  • Certification: Completion certificate upon successful completion of the bootcamp.
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