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AI & Machine Learning

Master the Future of Technology

Dive into the world of Artificial Intelligence and Machine Learning. Learn cutting-edge algorithms, deep learning, neural networks, and build intelligent systems that can learn and adapt.

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Duration

4 Months

Minimum Training Period

Certification

Industry

Recognized Certificate

Batch Size

20-25

Students per Batch

Placement

95%

Success Rate

Course Overview

Artificial Intelligence and Machine Learning are transforming every industry. This comprehensive program covers the fundamentals of AI/ML, deep learning, neural networks, natural language processing, and computer vision. Learn from industry experts and work on real-world projects using Python and cutting-edge frameworks.

With hands-on experience in TensorFlow, PyTorch, and other modern AI tools, you'll be equipped to build intelligent systems that can learn from data, make predictions, and solve complex problems. This course includes Generative AI and ChatGPT integration for modern AI applications.

What You'll Learn

  • Machine Learning Algorithms

    Supervised, unsupervised, and reinforcement learning techniques

  • Deep Learning & Neural Networks

    Build and train complex neural network architectures

  • Natural Language Processing

    Text analysis, sentiment analysis, and language models

  • Computer Vision

    Image classification, object detection, and facial recognition

  • Generative AI & ChatGPT

    Build AI applications with GPT models and generative AI

  • TensorFlow & PyTorch

    Master the most popular deep learning frameworks

  • Model Deployment

    Deploy ML models to production environments

  • MLOps & Best Practices

    Version control, model monitoring, and optimization

Course Highlights

  • Live interactive classes
  • 15+ AI/ML projects
  • Industry expert instructors
  • Lifetime access to materials
  • Placement assistance
  • GPU cloud access
  • Resume building support
  • 24/7 doubt resolution
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Course Curriculum

Detailed Syllabus

Comprehensive module-wise breakdown of the course content

01

Python for AI/ML

  • • Python Programming Basics
  • • NumPy for Numerical Computing
  • • Pandas for Data Analysis
  • • Matplotlib & Seaborn Visualization
  • • Scikit-learn Library
  • • Jupyter Notebooks
  • • Data Preprocessing
  • • Feature Engineering
02

Machine Learning Fundamentals

  • • Introduction to Machine Learning
  • • Supervised Learning Algorithms
  • • Linear & Logistic Regression
  • • Decision Trees & Random Forests
  • • Support Vector Machines
  • • K-Nearest Neighbors
  • • Naive Bayes Classifier
  • • Model Evaluation Metrics
03

Unsupervised Learning

  • • Clustering Algorithms
  • • K-Means Clustering
  • • Hierarchical Clustering
  • • DBSCAN Algorithm
  • • Principal Component Analysis
  • • Dimensionality Reduction
  • • Anomaly Detection
  • • Association Rules
04

Deep Learning & Neural Networks

  • • Introduction to Neural Networks
  • • Perceptrons & Activation Functions
  • • Backpropagation Algorithm
  • • Convolutional Neural Networks
  • • Recurrent Neural Networks
  • • LSTM & GRU Networks
  • • Transfer Learning
  • • Hyperparameter Tuning
05

TensorFlow & Keras

  • • TensorFlow Framework
  • • Keras High-Level API
  • • Building Neural Networks
  • • Model Training & Validation
  • • Callbacks & Checkpoints
  • • Model Optimization
  • • TensorBoard Visualization
  • • Model Saving & Loading
06

Natural Language Processing

  • • Text Preprocessing
  • • Tokenization & Lemmatization
  • • Word Embeddings (Word2Vec)
  • • Sentiment Analysis
  • • Text Classification
  • • Named Entity Recognition
  • • Transformers & BERT
  • • Chatbot Development
07

Computer Vision

  • • Image Processing Basics
  • • OpenCV Library
  • • Image Classification
  • • Object Detection (YOLO, R-CNN)
  • • Face Recognition
  • • Image Segmentation
  • • Generative Adversarial Networks
  • • Real-time Video Processing
08

Generative AI & Deployment

  • • Generative AI Concepts
  • • GPT Models & ChatGPT
  • • OpenAI API Integration
  • • Model Deployment (Flask, FastAPI)
  • • Cloud Deployment (AWS, Azure)
  • • Docker for ML Models
  • • MLOps Best Practices
  • • Capstone AI Project

Prerequisites

To get the most out of this course, we recommend:

  • Python Programming

    Basic to intermediate Python knowledge is required

  • Mathematics Basics

    Linear algebra, calculus, and statistics fundamentals

  • Programming Logic

    Good problem-solving and analytical skills

  • Commitment

    25-30 hours per week for 4 months

Career Opportunities

AI/ML professionals are among the highest paid in tech:

ML Engineer

₹6-15 LPA

AI Developer

₹7-18 LPA

Data Scientist

₹8-20 LPA

Deep Learning Engineer

₹10-25 LPA

Start Your Journey

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