Artificial Intelligence & Machine Learning Course

Master Artificial Intelligence and Machine Learning with Python, TensorFlow, Scikit-learn, Deep Learning, NLP, and Computer Vision. Build real-world AI applications.

Duration 5 Months (160 Hours)

Mode Live Online / Offline

3,800+ Students

360+ Partners

91% Placement

Fresher / Entry Level

₹6 - 9 LPA

0-2 years experience

Junior ML Engineer
AI Trainee

⭐ Most Common

Mid Level

₹10 - 20 LPA

2-5 years experience

Machine Learning Engineer
AI Specialist

Senior / Expert Level

₹22 - 50+ LPA

5+ years experience

Lead AI Architect
Principal Data Scientist

Machine Learning Engineer

AI Engineer

Data Scientist

Deep Learning Engineer

NLP Engineer

Computer Vision Engineer

Python

Machine Learning

Deep Learning

TensorFlow/Keras

Scikit-learn

Computer Vision

NLP

Generative AI

Pandas/NumPy

OpenCV

Module 1: Python for AI/ML

Python Basics - Variables, Data Types, Operators
Control Flow - if-else, Loops
Functions & Lambda Expressions
Data Structures - Lists, Tuples, Dictionaries, Sets
NumPy - Arrays, Mathematical Operations
Pandas - Series, DataFrame, Data Manipulation
Matplotlib & Seaborn - Data Visualization
File Handling - CSV, JSON, Excel
Object Oriented Programming in Python

Module 2: Mathematics for Machine Learning

Linear Algebra - Vectors, Matrices, Eigenvalues
Matrix Operations - Addition, Multiplication, Transpose
Calculus - Derivatives, Partial Derivatives, Gradients
Descriptive Statistics - Mean, Median, Mode, Variance
Probability - Distributions, Bayes Theorem
Correlation & Covariance
Hypothesis Testing & p-values

Module 3: Data Preprocessing & Visualization

Data Collection & Understanding
Handling Missing Values
Handling Outliers
Data Encoding - One-Hot, Label Encoding
Feature Scaling - Standardization, Normalization
Train-Test Split
Exploratory Data Analysis (EDA)
Data Visualization with Matplotlib & Seaborn

Module 4: Machine Learning - Supervised Learning

Introduction to Machine Learning
Linear Regression - Simple & Multiple
Polynomial Regression
Logistic Regression for Classification
Decision Trees
Random Forest
Support Vector Machines (SVM)
K-Nearest Neighbors (KNN)
Naive Bayes Classifier
Gradient Boosting (XGBoost, LightGBM)

Module 5: Machine Learning - Unsupervised Learning

Clustering - K-Means Algorithm
Hierarchical Clustering
DBSCAN Clustering
Principal Component Analysis (PCA)
t-SNE for Dimensionality Reduction
Anomaly Detection

Module 6: Model Evaluation & Selection

Evaluation Metrics for Regression - MAE, MSE, RMSE, R²
Evaluation Metrics for Classification - Accuracy, Precision, Recall, F1
Confusion Matrix
ROC Curve & AUC Score
Cross-Validation - K-Fold, Stratified K-Fold
Hyperparameter Tuning - GridSearchCV, RandomizedSearchCV
Bias-Variance Tradeoff

Module 7: Ensemble Learning

What is Ensemble Learning
Bagging - Bootstrap Aggregating
Random Forest - Bagging with Decision Trees
Boosting - AdaBoost
Gradient Boosting Machines (GBM)
XGBoost - Extreme Gradient Boosting
LightGBM & CatBoost
Stacking & Voting Classifiers

Module 8: Deep Learning & Neural Networks

Introduction to Neural Networks
Perceptron & Activation Functions
Forward Propagation & Backpropagation
TensorFlow & Keras Basics
Building Neural Networks with Keras
Convolutional Neural Networks (CNN)
Recurrent Neural Networks (RNN)
LSTM & GRU
Transfer Learning & Pre-trained Models
Model Deployment with TensorFlow Serving

Module 9: Computer Vision (OpenCV)

Introduction to Computer Vision
OpenCV Basics - Reading, Writing, Displaying Images
Image Processing - Filtering, Thresholding, Edge Detection
Face Detection with Haar Cascades
Object Detection with YOLO
Image Classification with CNNs
Real-time Video Processing

Module 10: Natural Language Processing (NLP)

Introduction to Natural Language Processing
Text Preprocessing - Tokenization, Stemming, Lemmatization
Removing Stopwords & Punctuation
Bag of Words & TF-IDF
Word Embeddings - Word2Vec, GloVe
Sentiment Analysis
Named Entity Recognition (NER)
Text Classification
Transformers & BERT

Module 11: Generative AI & LLMs

Introduction to Generative AI
Large Language Models (LLMs)
OpenAI GPT & ChatGPT API
Prompt Engineering Techniques
LangChain for LLM Applications
RAG (Retrieval-Augmented Generation)
Building AI Chatbots
Text & Image Generation (DALL-E, Stable Diffusion)

Module 12: Real-Time Projects

Project 1: House Price Prediction
Project 2: Customer Churn Prediction
Project 3: Image Classification using CNN
Project 4: Sentiment Analysis on Reviews
Project 5: Face Detection System
Project 6: AI Chatbot using GPT
Project 7: Recommendation System
Capstone Project - End to End AI Solution

Expert Trainers

Learn from industry professionals with 10+ years of AI/ML experience

Hands-on Projects

Work on 8+ real-time AI/ML projects with live datasets

Industry Certification

Get globally recognized AI/ML certification

100% Placement Support

Tie-ups with 360+ companies for AI/ML roles

Resume Building

Professional resume & portfolio with AI projects

Mock Interviews

Regular mock interviews with detailed feedback

Customer Churn Prediction

Build a machine learning model to predict customer churn using classification algorithms and provide business insights.

Python Pandas Scikit-learn XGBoost Matplotlib

Image Classification with CNN

Create a deep learning model using CNN to classify images into multiple categories like cats, dogs, birds.

Python TensorFlow Keras CNN OpenCV

AI Chatbot using GPT

Develop an intelligent chatbot using OpenAI GPT API and LangChain for customer support automation.

Python OpenAI API LangChain Flask Streamlit

Placement Support Includes:

Resume & LinkedIn Profile Building
Aptitude & Technical Training
Mock Interviews with Industry Experts
Soft Skills & Communication Training

Our Hiring Partners:

500+ Hiring Partners
Unlimited Interview Opportunities
Job Portal Access
Life-long Placement Support

What are the prerequisites for AI/ML course?

Basic programming knowledge is helpful. We cover Python from scratch, so no prior ML experience required.

What is the duration of the course?

The course duration is 5 months (160 hours) with flexible batch timings.

Will I learn both ML and Deep Learning?

Yes, the course covers Machine Learning, Deep Learning, NLP, Computer Vision, and Generative AI.

What projects will I build?

You will build 8+ projects including Churn Prediction, Image Classification, and AI Chatbot.

Is placement assistance provided?

Yes, we provide 100% placement assistance with 360+ hiring partners.

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