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AI & ML

  • Expert-led Training
  • Real-time Projects
  • Career Transformation & Guidance
  • 24/7 Support
4.5
Advanced
20 Weeks
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AI & ML

Artificial Intelligence (AI) and Machine Learning (ML) are transforming industries worldwide. From recommendation systems and chatbots to automation and predictive analytics, AI/ML technologies are becoming essential across every business sector.
This comprehensive training program is designed to help students and professionals learn AI & ML concepts from fundamentals to advanced real-world applications with hands-on practical training and industry projects.
The course focuses on:
✅ Strong Fundamentals
✅ Practical Implementation
✅ Real-Time Projects
✅ Industry Tools
✅ Model Development
✅ Deployment Concepts
✅ Career Preparation

Who Can Join This Course?

This course is ideal for:

  • Students
  • Freshers
  • Software Professionals
  • Data Analysts
  • Python Developers
  • Career Switchers
  • Basic computer knowledge is sufficient for beginners.

Training Methodology

  • Instructor-Led Live
  • Practical Assignments
  • Real-Time Case Studies
  • Hands-on Tool Practice
  • Weekly Assessments
  • Mock Interviews

Course Completion Certificate

Students will receive:

✅ Course Completion Certificate

Career Opportunities After Course

  • Machine Learning Engineer
  • AI Engineer
  • Data Scientist
  • AI Research Assistant
  • NLP Engineer
  • Computer Vision Engineer
  • Generative AI Engineer
  • Python Developer
  • AI Application Developer
  • Business Intelligence Analyst

Course Curriculum

Fundamentals of AI

  • What is Artificial Intelligence?
  • AI vs ML vs Deep Learning
  • Types of AI
  • Applications of AI
  • AI in Real World

Machine Learning Overview

  • Introduction to Machine Learning
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning

AI Project Lifecycle

  • Data Collection
  • Data Cleaning
  • Model Training
  • Model Evaluation
  • Deployment Basics

Outcome

Students will understand the fundamentals and scope of AI & ML technologies

  • Python Fundamentals

    • Variables & Data Types
    • Operators
    • Loops & Conditions
    • Functions
    • OOP Concepts

    Python Libraries

    • NumPy
    • Pandas
    • Matplotlib
    • Seaborn Basics

    Data Handling

    • Reading CSV/Excel Files
    • DataFrames
    • Data Manipulation
    • Missing Value Handling

    Visualization

    • Bar Charts
    • Line Charts
    • Histograms
    • Scatter Plots

    Outcome

    Students will gain programming skills required for AI/ML development.

     

Mathematics Basics

  • Linear Algebra Fundamentals
  • Matrices & Vectors
  • Probability Basics
  • Calculus Introduction

Statistics for ML

  • Mean, Median, Mode
  • Standard Deviation
  • Variance
  • Correlation
  • Hypothesis Testing

Data Distribution

  • Normal Distribution
  • Sampling Techniques
  • Statistical Analysis

Outcome

Students will understand mathematical foundations used in machine learning.

Data Cleaning

  • Handling Missing Data
  • Duplicate Removal
  • Outlier Detection

Feature Engineering

  • Encoding Techniques
  • Feature Scaling
  • Feature Selection

Data Splitting

  • Training Data
  • Testing Data
  • Validation Data

Outcome

Students will learn how to prepare quality datasets for ML models.

 

Regression Algorithms

  • Linear Regression
  • Multiple Linear Regression
  • Polynomial Regression

Classification Algorithms

  • Logistic Regression
  • Decision Trees
  • Random Forest
  • K-Nearest Neighbors (KNN)
  • Support Vector Machine (SVM)
  • Naive Bayes

Model Evaluation

  • Accuracy
  • Precision
  • Recall
  • F1 Score
  • Confusion Matrix

Outcome

Students will build predictive machine learning models.

Clustering Algorithms

  • K-Means Clustering
  • Hierarchical Clustering
  • DBSCAN

Dimensionality Reduction

  • PCA (Principal Component Analysis)

Association Rule Mining

  • Apriori Algorithm
  • Market Basket Analysis

Outcome

Students will learn pattern recognition and clustering techniques.

Introduction to Deep Learning

  • Neural Networks
  • Artificial Neurons
  • Activation Functions

Deep Learning Frameworks

  • TensorFlow Basics
  • Keras Basics

ANN Models

  • Building Artificial Neural Networks
  • Training Deep Learning Models

CNN Introduction

  • Convolutional Neural Networks
  • Image Classification Basics

Outcome

Students will understand deep learning concepts and neural network implementation.

NLP Fundamentals

  • Text Processing
  • Tokenization
  • Stop Words
  • Stemming & Lemmatization

NLP Techniques

  • Bag of Words
  • TF-IDF
  • Word Embeddings

NLP Applications

  • Sentiment Analysis
  • Text Classification
  • Chatbot Basics

Outcome

Students will learn how AI processes human language

Introduction to Generative AI

  • What is Generative AI?
  • Use Cases of Gen AI

ChatGPT & LLMs

  • Large Language Models
  • Prompt Engineering
  • AI Productivity

AI Content Generation

  • Text Generation
  • AI Image Generation
  • AI Automation

AI Tools

  • ChatGPT
  • Gemini
  • AI Assistants

Outcome

Students will learn modern AI tools and Generative AI applications.

Deployment Concepts

  • Model Saving
  • API Basics
  • Deployment Overview

Flask Basics

  • Creating Simple APIs
  • Connecting ML Models

Streamlit Basics

  • Building ML Web Apps

Outcome

Students will understand how ML models are deployed into applications.

Sales Prediction System

  • Predict future sales using regression algorithms.

Loan Approval Prediction

  • Banking dataset classification project.

Spam Email Detection

  • NLP-based text classification model.

Sentiment Analysis Project

  • Analyze customer reviews and feedback.

Image Classification

  • Deep learning image recognition project.

AI Chatbot Project

  • Build a simple AI-powered chatbot.

Outcome

Students will build practical portfolio projects for job interviews.

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