AI & Machine Learning Course in Hyderabad with Real-Time Projects
In Association with
Start Your Journey
Complete the form to get closer to your goal.
Artificial
Intelligence
Real-time insights into the fastest growing tech sector of 2026
14 LPA
Average annual salary for AI/ML professionals in India
50k+
AI & ML job openings across India
42%
Annual growth rate of the AI/ML job market globally
Industry-Relevant AI & ML Training at DIGIT innovations
Our AI & Machine Learning course at DIGIT innovations is a meticulously designed 180-day program that takes you from Python programming fundamentals all the way to building and deploying intelligent AI systems. You will gain expertise in data analysis with NumPy and Pandas, machine learning algorithms (supervised and unsupervised), deep learning with TensorFlow and Keras, and cutting-edge Generative AI with LangChain, RAG, and LLM APIs.
Our professional instructors bring a wealth of real-world industry experience, offering personalized mentorship and project-based guidance throughout the program. You will build an impressive portfolio of 30+ projects from house price prediction to AI chatbots and participate in placement training that includes resume building, aptitude training, DSA preparation, and mock interviews.
End-to-End ML & Deep Learning
Generative AI & Deployment
Your AI & Machine Learning Learning Journey
A comprehensive 180-day program designed to transform you from a beginner to a job-ready AI & ML professional. Every module is built around real-world industry projects and best practices.
Python Programming
( Days 1-20 )
Introduction & Setup
Introduction to AI, ML, Data Science & Python ecosystem
Python environment setup — Anaconda, VS Code, Jupyter Notebook
Variables and Data Types — int, float, str, bool
Operators and Expressions — arithmetic, comparison, logical
Input/Output Functions — input(), print(), format()
Conditional Statements — if, elif, else
Data Structures
Loops — for loop, while loop, break, continue, pass
Pattern Programs — nested loops practice
Strings — methods, slicing, formatting, f-strings
Lists — creation, indexing, slicing, list methods
Tuples and Sets — immutability, set operations
Functions & Modules
Dictionaries — key-value pairs, methods, comprehension
Functions — definition, parameters, return values, scope
Lambda Functions — anonymous functions, map, filter
Recursion — base case, call stack, factorial, Fibonacci
Modules and Packages — import, pip, standard libraries
Advanced Python & OOP
File Handling — read, write, append, with statement
Exception Handling — try, except, finally, custom exceptions
OOP Concepts — classes, objects, constructors (__init__)
Inheritance, Polymorphism, Encapsulation, Abstraction
Mini Project — Contact Book or Calculator
Python Assessment
Python Programming
Introduction & Setup
Introduction to AI, ML, Data Science & Python ecosystem
Python environment setup — Anaconda, VS Code, Jupyter Notebook
Variables and Data Types — int, float, str, bool
Operators and Expressions — arithmetic, comparison, logical
Input/Output Functions — input(), print(), format()
Conditional Statements — if, elif, else
Data Structures
Loops — for loop, while loop, break, continue, pass
Pattern Programs — nested loops practice
Strings — methods, slicing, formatting, f-strings
Lists — creation, indexing, slicing, list methods
Tuples and Sets — immutability, set operations
Functions & Modules
Dictionaries — key-value pairs, methods, comprehension
Functions — definition, parameters, return values, scope
Lambda Functions — anonymous functions, map, filter
Recursion — base case, call stack, factorial, Fibonacci
Modules and Packages — import, pip, standard libraries
Advanced Python & OOP
File Handling — read, write, append, with statement
Exception Handling — try, except, finally, custom exceptions
OOP Concepts — classes, objects, constructors (__init__)
Inheritance, Polymorphism, Encapsulation, Abstraction
Mini Project — Contact Book or Calculator
Python Assessment
SQL
( Days 21-35 )SQL Fundamentals
Database Concepts — DBMS vs RDBMS, tables, keys
DDL Commands — CREATE, ALTER, DROP, TRUNCATE
DML Commands — INSERT, UPDATE, DELETE, SELECT
Constraints — PRIMARY KEY, FOREIGN KEY, UNIQUE, NOT NULL, CHECK
SQL Practice Exercises
Intermediate SQL
Joins — INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN, SELF JOIN
GROUP BY and HAVING Clauses — aggregate functions
Subqueries — correlated and non-correlated
Views and Indexes — CREATE VIEW, performance optimization
SQL Assessment
Advanced SQL
Stored Procedures — parameterized procedures
Triggers — BEFORE and AFTER triggers
Common Table Expressions (CTE) — WITH clause
Window Functions — ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD, PARTITION BY
SQL Capstone Project — E-Commerce Database Analysis
SQL
SQL Fundamentals
Database Concepts — DBMS vs RDBMS, tables, keys
DDL Commands — CREATE, ALTER, DROP, TRUNCATE
DML Commands — INSERT, UPDATE, DELETE, SELECT
Constraints — PRIMARY KEY, FOREIGN KEY, UNIQUE, NOT NULL, CHECK
SQL Practice Exercises
Intermediate SQL
Joins — INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN, SELF JOIN
GROUP BY and HAVING Clauses — aggregate functions
Subqueries — correlated and non-correlated
Views and Indexes — CREATE VIEW, performance optimization
SQL Assessment
Advanced SQL
Stored Procedures — parameterized procedures
Triggers — BEFORE and AFTER triggers
Common Table Expressions (CTE) — WITH clause
Window Functions — ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD, PARTITION BY
SQL Capstone Project — E-Commerce Database Analysis
Statistics & Maths
( Days 36-45 )Descriptive Statistics
5 learning pointsInferential Statistics & Linear Algebra
7 learning pointsStatistics & Maths
Descriptive Statistics
5 learning pointsInferential Statistics & Linear Algebra
7 learning pointsNumPy & Pandas
( Days 46-60 )
NumPy Arrays & Operations
NumPy Arrays — ndarray, shape, dtype
Array Operations — arithmetic, element-wise operations
Indexing and Slicing — 1D, 2D arrays
Broadcasting — operating on arrays of different shapes
Pandas for Data Analysis
Pandas Series — creation, indexing, operations
DataFrames — creation from CSV, dict, list
Data Cleaning — removing duplicates, renaming columns
Handling Missing Values — fillna, dropna, interpolate
Data Transformation — apply, map, replace
Data Aggregation — groupby, pivot_table, agg
Merge and Join — merge(), concat(), join()
GroupBy Operations — split-apply-combine
Real Dataset Analysis — Titanic / IPL dataset
Pandas Project & Assessment
NumPy & Pandas
NumPy Arrays & Operations
NumPy Arrays — ndarray, shape, dtype
Array Operations — arithmetic, element-wise operations
Indexing and Slicing — 1D, 2D arrays
Broadcasting — operating on arrays of different shapes
Pandas for Data Analysis
Pandas Series — creation, indexing, operations
DataFrames — creation from CSV, dict, list
Data Cleaning — removing duplicates, renaming columns
Handling Missing Values — fillna, dropna, interpolate
Data Transformation — apply, map, replace
Data Aggregation — groupby, pivot_table, agg
Merge and Join — merge(), concat(), join()
GroupBy Operations — split-apply-combine
Real Dataset Analysis — Titanic / IPL dataset
Pandas Project & Assessment
Data Visualization
( Days 61-70 )Matplotlib Charts
5 TopicsMatplotlib Basics — figures, axes, subplots
Line Charts — trends over time
Bar Charts — categorical comparisons
Pie Charts — part-to-whole relationships
Histograms — frequency distribution
Advanced Visualization
5 TopicsSeaborn Basics — statistical data visualization
Heat Maps — correlation matrix visualization
Dashboard Concepts — Plotly Express, interactive charts
Visualization Project — EDA on real dataset
Assessment
Data Visualization
Matplotlib Charts
5 TopicsMatplotlib Basics — figures, axes, subplots
Line Charts — trends over time
Bar Charts — categorical comparisons
Pie Charts — part-to-whole relationships
Histograms — frequency distribution
Advanced Visualization
5 TopicsSeaborn Basics — statistical data visualization
Heat Maps — correlation matrix visualization
Dashboard Concepts — Plotly Express, interactive charts
Visualization Project — EDA on real dataset
Assessment
Machine Learning
( Days 71-110 )Regression & Classification
Introduction to ML — types, workflow, scikit-learn pipeline
Train/Test Split — cross-validation, overfitting vs underfitting
Linear Regression — cost function, gradient descent
Multiple Regression — multivariate analysis
Logistic Regression — binary and multiclass
K-Nearest Neighbors (KNN)
Decision Trees — Gini impurity, entropy, pruning
Random Forest — ensemble method, bagging
Naive Bayes — probabilistic classification
Clustering & Dimensionality Reduction
Clustering Concepts — distance measures
K-Means Clustering — elbow method, inertia
Hierarchical Clustering — dendrogram, linkage
Principal Component Analysis (PCA) — variance explained
Model Tuning & Metrics
Encoding — Label Encoding, One-Hot Encoding
Feature Scaling — StandardScaler, MinMaxScaler, RobustScaler
Feature Selection — SelectKBest, correlation analysis
Handling Imbalanced Data — SMOTE, class_weight
Accuracy, Precision, Recall, F1 Score
ROC-AUC Curve — threshold tuning
Industry-Level ML Projects
Project 1: House Price Prediction — regression pipeline
Project 2: Student Performance Prediction — classification
Project 3: Employee Attrition Prediction — HR analytics
Project 4: Loan Approval Prediction — financial ML
Machine Learning
Regression & Classification
Introduction to ML — types, workflow, scikit-learn pipeline
Train/Test Split — cross-validation, overfitting vs underfitting
Linear Regression — cost function, gradient descent
Multiple Regression — multivariate analysis
Logistic Regression — binary and multiclass
K-Nearest Neighbors (KNN)
Decision Trees — Gini impurity, entropy, pruning
Random Forest — ensemble method, bagging
Naive Bayes — probabilistic classification
Clustering & Dimensionality Reduction
Clustering Concepts — distance measures
K-Means Clustering — elbow method, inertia
Hierarchical Clustering — dendrogram, linkage
Principal Component Analysis (PCA) — variance explained
Model Tuning & Metrics
Encoding — Label Encoding, One-Hot Encoding
Feature Scaling — StandardScaler, MinMaxScaler, RobustScaler
Feature Selection — SelectKBest, correlation analysis
Handling Imbalanced Data — SMOTE, class_weight
Accuracy, Precision, Recall, F1 Score
ROC-AUC Curve — threshold tuning
Industry-Level ML Projects
Project 1: House Price Prediction — regression pipeline
Project 2: Student Performance Prediction — classification
Project 3: Employee Attrition Prediction — HR analytics
Project 4: Loan Approval Prediction — financial ML
Deep Learning
( Days 111-130 )ANN Architecture
Neural Networks Introduction — biological vs artificial neurons
Perceptron Model — single layer learning
Activation Functions — ReLU, Sigmoid, Tanh, Softmax, Leaky ReLU
Forward Propagation — layer-by-layer computation
Backpropagation — chain rule, weight updates
Building Deep Learning Models
TensorFlow Introduction — tensors, computation graphs
Keras Basics — Sequential API, Functional API
Regularization — dropout, batch normalization, L1/L2
ANN Project — Customer Churn Prediction
Computer Vision & Sequence Models
CNN Concepts — convolution, pooling, padding, filters
Image Classification Project — CIFAR-10 / custom dataset
RNN Concepts — sequential data, vanishing gradient
LSTM Basics — gates, memory cell, time series
Deep Learning Capstone Projects — 3 end-to-end projects
Deep Learning
ANN Architecture
Neural Networks Introduction — biological vs artificial neurons
Perceptron Model — single layer learning
Activation Functions — ReLU, Sigmoid, Tanh, Softmax, Leaky ReLU
Forward Propagation — layer-by-layer computation
Backpropagation — chain rule, weight updates
Building Deep Learning Models
TensorFlow Introduction — tensors, computation graphs
Keras Basics — Sequential API, Functional API
Regularization — dropout, batch normalization, L1/L2
ANN Project — Customer Churn Prediction
Computer Vision & Sequence Models
CNN Concepts — convolution, pooling, padding, filters
Image Classification Project — CIFAR-10 / custom dataset
RNN Concepts — sequential data, vanishing gradient
LSTM Basics — gates, memory cell, time series
Deep Learning Capstone Projects — 3 end-to-end projects
Generative AI
( Days 131-150 )
LLMs & Transformers
Introduction to Generative AI — GANs, VAEs, Diffusion Models
Large Language Models (LLM) Concepts — tokens, context window
Transformers Architecture — attention mechanism, BERT, GPT
Prompt Engineering Basics — zero-shot, few-shot prompting
Advanced Prompting — chain-of-thought, role prompting, output formatting
Building AI-Powered Applications
OpenAI APIs — GPT-4o, embeddings, function calling
Gemini APIs — Google AI Studio, Gemini Pro
LangChain Basics — chains, prompts, output parsers
Chains and Agents — ReAct agent, tool use
Vector Databases — ChromaDB, Pinecone, FAISS
Real-World GenAI Projects
RAG Concepts — why RAG, retrieval vs generation
RAG Implementation — document loader, chunking, embedding, retrieval
Project 1: AI Chatbot — LangChain + GPT
Project 2: Resume Analyzer AI — PDF parsing + LLM
Project 3: Document Q&A System — RAG pipeline
GenAI Capstone Project — 5-day end-to-end project
Generative AI
LLMs & Transformers
Introduction to Generative AI — GANs, VAEs, Diffusion Models
Large Language Models (LLM) Concepts — tokens, context window
Transformers Architecture — attention mechanism, BERT, GPT
Prompt Engineering Basics — zero-shot, few-shot prompting
Advanced Prompting — chain-of-thought, role prompting, output formatting
Building AI-Powered Applications
OpenAI APIs — GPT-4o, embeddings, function calling
Gemini APIs — Google AI Studio, Gemini Pro
LangChain Basics — chains, prompts, output parsers
Chains and Agents — ReAct agent, tool use
Vector Databases — ChromaDB, Pinecone, FAISS
Real-World GenAI Projects
RAG Concepts — why RAG, retrieval vs generation
RAG Implementation — document loader, chunking, embedding, retrieval
Project 1: AI Chatbot — LangChain + GPT
Project 2: Resume Analyzer AI — PDF parsing + LLM
Project 3: Document Q&A System — RAG pipeline
GenAI Capstone Project — 5-day end-to-end project
Deployment & Placement
( Days 151-180 )Deployment Tools & Frameworks
6 Learning PointsFlask Basics — REST API development, routes, JSON responses
FastAPI Basics — async APIs, automatic docs, Pydantic
Model Deployment — saving with pickle/joblib, serving predictions
Git & GitHub — version control, pull requests, portfolio repos
Streamlit — interactive ML dashboards and demos
Portfolio Building — GitHub profile, project README
Career & Interview Preparation
6 Learning PointsResume Preparation — ATS-friendly, quantifying impact
LinkedIn Profile Optimization — headline, about, projects
Aptitude Training — quantitative, logical, verbal (7 days)
DSA for Interviews — arrays, strings, sorting, recursion (7 days)
Mock Interviews — 5 rounds with feedback
Final Capstone Presentation — end-to-end AI system demo
Deployment & Placement
Deployment Tools & Frameworks
6 Learning PointsFlask Basics — REST API development, routes, JSON responses
FastAPI Basics — async APIs, automatic docs, Pydantic
Model Deployment — saving with pickle/joblib, serving predictions
Git & GitHub — version control, pull requests, portfolio repos
Streamlit — interactive ML dashboards and demos
Portfolio Building — GitHub profile, project README
Career & Interview Preparation
6 Learning PointsResume Preparation — ATS-friendly, quantifying impact
LinkedIn Profile Optimization — headline, about, projects
Aptitude Training — quantitative, logical, verbal (7 days)
DSA for Interviews — arrays, strings, sorting, recursion (7 days)
Mock Interviews — 5 rounds with feedback
Final Capstone Presentation — end-to-end AI system demo
Track Your Progress Toward Enrollment
Follow these four milestones to transform your career. From registration to your first class, we've made it seamless.
Registration
Register online, provide details, help admissions understand your career goals.
Test
Submit application, then an assessment ensures eligibility and program aptitude.
Offer Applicable
After review, eligible candidates get scholarship offers and confirmation email.
Fee Payment
Review your admission offer, promptly pay fee to confirm enrollment.
Mentor Community
Our mentor community at DIGIT Innovations believes in the power of sharing. We partner with experienced professionals from leading companies to guide you through real projects, career advice, and hiring-ready skills.
Advance Your Career with Our AI & Machine Learning Certification
Global Industry Recognition
Our certifications are well-regarded in the software industry, providing valuable opportunities for career advancement
Practical Skill Validation
Our certifications validate your skills through practical application, equipping you for the workforce and demonstrating your expertise to employers.
Career Advancement
Achieve new heights in your professional journey with our certifications.
Shared Experiences from Our Students
Companies Hiring
DIGIT innovations Alumni
Our alumni work with leading companies worldwide, delivering real-world impact.
Join Our Upcoming
Live Batches
Secure your spot in the next batch. Limited seats available. Enroll now and start your learning journey with expert-led live training.
Loading Schedules...
FAQ
Frequently Asked Questions
Everything you need to know about the course. Find answers to common queries regarding enrollment, curriculum, and career paths.
Talk to usNo prior experience is required. The course starts from Python programming basics, making it perfectly suited for beginners. A basic logical aptitude is beneficial but not mandatory.
The program is a comprehensive 180-day (approximately 6-month) course covering Python, SQL, Statistics, NumPy, Pandas, Data Visualization, Machine Learning, Deep Learning, Generative AI, and Deployment & Placement training.
You will build 30+ real-world projects including House Price Prediction, Student Performance Prediction, Employee Attrition Analysis, Loan Approval System, Image Classification using CNN, AI Chatbots using LangChain, Resume Analyzer, Document Q&A System, and a final Capstone Project.
Yes! We provide comprehensive placement support including resume building, LinkedIn profile optimization, aptitude training, DSA interview preparation, mock interviews, and direct connections to our hiring partner network.
You will learn Python, SQL, NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, TensorFlow, Keras, OpenAI APIs, Gemini APIs, LangChain, Vector Databases, Flask, FastAPI, Streamlit, Git & GitHub, and more.
Yes, upon successful completion you will receive an industry-recognized certificate from DIGIT innovations. The curriculum is aligned with NASSCOM, Skill India, and FutureSkills Prime standards.
Graduates pursue roles as Machine Learning Engineer, Data Scientist, AI Engineer, NLP Engineer, Deep Learning Researcher, Data Analyst, MLOps Engineer, and Generative AI Developer in companies ranging from startups to Fortune 500 enterprises.