Data Science And Machine Learning With Gen AI
Master the Future with Data Science, Machine Learning & Generative AI.Step into the world of data-driven innovation with our industry-focused program. From Python basics to advanced ML models and Gen AI tools, you’ll gain the expertise to analyze data, build intelligent systems, and create AI-powered solutions that transform businesses. With hands-on projects, real case studies, and expert mentorship, this course equips you to launch a high-growth career in AI, Data Science, and next-gen technologies.
6 Months
.Duration
Virtual Live
Classes
100% Job
Assurance
Languages
English | Hindi
Get Certifications From
Course Highlights
Industry Mentorship
Learn directly from top Data Scientists & AI Engineers
Job-Ready Skills
Resume building, interview prep & 100% job assurance
Capstone Project
End-to-end real-world AI/ML solution with Gen AI integration
Gen AI Mastery
Gain hands-on expertise with cutting-edge Generative AI tools like ChatGPT, GPT-4, and DALL·E.
Master these Tools
Skills Covered
Core Machine Learning
.
Deep Learning Expertise
.
Natural Language Processing
.
Cloud AI Services
.
Generative AI Applications
.
Data Analysis & Visualization
.
Course Curriculum
Learn everything from data analysis and machine learning to deep learning, NLP, and big data. Build hands-on projects, master tools like Python, TensorFlow, and Hugging Face, and gain job-ready skills to succeed in today’s AI-driven world.
Module 1: Introduction to Data Science & AI
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. What is Data Science? Scope & Applications
. Evolution of AI & Machine Learning
. AI vs ML vs DL vs Generative AI
. Data Science Project Lifecycle
. Role of a Data Scientist, AI Engineer, and ML Engineer
. AI in Real-World Industries (Healthcare, Finance, Retail, Autonomous Systems)
. Overview of Tools & Ecosystem (Python, R, TensorFlow, PyTorch, Hadoop, Spark)
. Introduction to Responsible & Ethical AI
Module 2: Python Programming for Data Science
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. Setting up Python Environment (Anaconda, Jupyter, VS Code)
. Python Syntax & Data Types
. Conditional Statements & Loops
. Functions & Lambda Functions
. Data Structures in Python (List, Tuple, Dict, Set)
. File Handling in Python
. Exception Handling
. Modules & Packages
. Numpy for Numerical Computing
. Pandas for Data Handling
. Matplotlib & Seaborn Basics
. Python OOP Concepts for ML Projects
Module 3: Data Handling & Preprocessing
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. Data Collection Methods
. Reading Data from CSV, Excel, SQL, APIs
. Handling Missing Values
. Outlier Detection & Treatment
. Data Transformation & Normalization
. Feature Scaling (Min-Max, Standardization)
. Feature Engineering & Selection
. Encoding Categorical Variables
. Data Cleaning Best Practices
. Automating Data Preprocessing Pipelines
Module 4: Data Visualization & Analytics
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. Importance of Visualization in AI
. Matplotlib Deep Dive
. Seaborn Advanced Charts
. Plotly & Interactive Dashboards
. Exploratory Data Analysis (EDA)
. Storytelling with Data
. Geospatial Data Visualization
. Building BI Dashboards with Power BI / Tableau
Module 5: Statistics & Probability for AI
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. Descriptive Statistics
. Inferential Statistics
. Probability Distributions (Normal, Binomial, Poisson)
. Hypothesis Testing
. Correlation & Covariance
. ANOVA & Chi-Square Test
. Bayesian Statistics Basics
. Random Variables & Sampling
. Markov Chains
Module 6: Machine Learning Fundamentals
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. Introduction to Machine Learning
. Supervised vs Unsupervised Learning
. Linear Regression
. Logistic Regression
. Decision Trees
. K-Nearest Neighbors (KNN)
. Naive Bayes Classifier
. Support Vector Machines (SVM)
. Clustering (K-Means, Hierarchical)
. Model Evaluation Metrics (Accuracy, Precision, Recall, F1, ROC-AUC)
. Overfitting & Underfitting
. Cross-Validation & Grid Search
Module 7: Advanced Machine Learning
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. Ensemble Learning (Bagging, Boosting, Stacking)
. Random Forests
. Gradient Boosting Machines (XGBoost, LightGBM, CatBoost)
. Advanced Feature Engineering
. Dimensionality Reduction (PCA, t-SNE, UMAP)
. Imbalanced Data Handling (SMOTE, ADASYN)
. Hyperparameter Tuning (Grid Search, Random Search, Bayesian Optimization)
. Model Interpretability (SHAP, LIME)
Module 8: Deep Learning with Neural Networks
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. Introduction to Neural Networks
. Perceptron & Multi-Layer Perceptron
. Activation Functions
. Forward & Backpropagation
. Gradient Descent & Optimizers
. Loss Functions
. Regularization (Dropout, Batch Normalization)
. Hyperparameter Tuning in DL
. Transfer Learning
. Introduction to TensorFlow & PyTorch
Module 9: Computer Vision with AI
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. Image Processing Basics (OpenCV)
. Convolutional Neural Networks (CNNs)
. Image Classification
. Object Detection (R-CNN, YOLO, SSD)
. Semantic & Instance Segmentation
. Face Recognition Systems
. Image Augmentation Techniques
. Vision Transformers (ViT)
Module 10: Natural Language Processing (NLP)
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. Text Preprocessing (Tokenization, Lemmatization, Stop Words)
. Bag of Words & TF-IDF
. Word Embeddings (Word2Vec, GloVe, FastText)
. Recurrent Neural Networks (RNN, LSTM, GRU)
. Attention Mechanisms
. Transformers (BERT, GPT, RoBERTa)
. Text Classification & Sentiment Analysis
. Named Entity Recognition
. Machine Translation
. Summarization & Chatbots
Module 11: Generative AI Fundamentals
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. What is Generative AI?
. Generative Models (GANs, VAEs)
. Diffusion Models
. Large Language Models (GPT, LLaMA, Falcon)
. Prompt Engineering Basics
. Reinforcement Learning with Human Feedback (RLHF)
. Fine-tuning & Instruction Tuning
Module 12: Applied Generative AI Projects
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. AI-Powered Chatbots
. AI Content Generation (Text, Image, Audio, Video)
. Text-to-Image Models (Stable Diffusion, DALL-E)
. AI in Gaming (Level Generation, NPC Behavior)
. Voice Cloning & Speech Synthesis
. Personalized Recommendation Systems with AI
. AI-powered Code Generation (Copilot, Code Llama)
Module 13: Big Data & Analytics
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. Introduction to Big Data
. Hadoop & HDFS
. MapReduce
. Apache Spark Basics
. Spark MLlib for Machine Learning
. Data Lakes vs Data Warehouses
. NoSQL Databases (MongoDB, Cassandra)
. Streaming Data with Kafka
. Real-Time Analytics
Module 14: Cloud Computing for AI
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. Introduction to Cloud Platforms (AWS, GCP, Azure)
. Cloud Storage & Databases
. Deploying ML Models on Cloud
. MLOps Fundamentals
. Containerization with Docker
. CI/CD Pipelines for ML
. Model Monitoring & Retraining
. Serverless AI Applications
Module 15: Responsible & Ethical AI
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. AI Bias & Fairness
. Explainable AI (XAI)
. AI & Privacy (GDPR, HIPAA)
. Adversarial Attacks on ML Models
. Model Interpretability Tools
. Sustainability in AI
. AI Governance & Compliance
Module 16: Domain-Specific AI Applications
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. AI in Healthcare (Diagnostics, Drug Discovery)
. AI in Finance (Fraud Detection, Algorithmic Trading)
. AI in Retail & E-commerce (Personalization, Inventory Management)
. AI in Manufacturing & IoT
. AI for Autonomous Vehicles
. AI in Cybersecurity
. AI in Education & EdTech
Module 17: Advanced Topics in AI & M
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. Reinforcement Learning
. Multi-Agent Systems
. Evolutionary Algorithms
. Federated Learning
. Self-Supervised Learning
. Meta-Learning (Learning to Learn)
. Quantum Machine Learning
. AI for Edge Devices (TinyML)
. Neuromorphic Computing
Module 18: Capstone Project & Portfolio Building
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. Choosing a Real-World AI Problem
. Project Planning & Documentation
. End-to-End AI Project Implementation
. Model Deployment & Scaling
. Portfolio Showcase on GitHub
. Writing AI Case Studies
. Building a Personal AI Website/Portfolio
Module 19: Career Preparation & Job Readiness
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. Resume Building for AI & Data Science
. Interview Preparation (Coding + ML + System Design)
. Top 100 AI/ML Interview Questions
. Freelancing in AI (Upwork, Fiverr)
. Building LinkedIn & GitHub Presence
. Networking & AI Communities
. Hackathons & Kaggle Competitions
. Job Roles: AI Engineer, Data Scientist, ML Engineer, MLOps Engineer
About Data Science And Machine Learning With GenAI Course
Our Data Science & Machine Learning with Generative AI Program is designed to equip you with the skills needed to analyze data, build predictive models, and create cutting-edge AI applications. You’ll master Python, statistics, machine learning, deep learning, NLP, computer vision, and Generative AI—empowering you to solve real-world problems, accelerate your career, or kickstart your freelancing journey.
✅ Practical, Project-Based Learning
Through hands-on labs, live projects, Kaggle-style challenges, and capstone assignments, you’ll gain practical experience in data preprocessing, model training, and deploying AI/ML solutions. By the end, you’ll have a strong portfolio showcasing your expertise in AI and ML, making you job-ready for high-demand roles.
✅ Expert Mentorship & Career Support
Learn directly from industry experts, receive personalized career guidance, prepare for top interviews, and get assistance for certifications in Data Science & AI. Plus, earn a recognized certification to stand out to employers and clients in the competitive AI/ML job market.
✅ For Everyone Ready to Master AI & Data Science
Whether you’re a student, IT professional, or entrepreneur, this program will help you master data-driven decision-making, machine learning, and Generative AI—preparing you for the future of intelligent systems and scalable AI solutions.
👥 Who Should Enroll?
✔ Students & graduates aiming to start a career in Data Science, ML, or AI
✔ Working professionals looking to upskill or switch into Data/AI roles
✔ Entrepreneurs & innovators wanting to integrate AI into their products & services
Earn Industry Recognised Certificates from:
Career Opportunities After the Course
List potential roles students can pursue after completing the course, such as
- Data Scientist
- Generative AI Enginee
- AI Research Scientist
- Data Analyst / Business Analyst
- Computer Vision Engineer
- NLP Engineer
- Machine Learning Engineer
With 100% job assurance, our course prepares you for a lucrative career in Data Science.
Authorised Training Partners
Live Virtual Classes
Batch Timings
📅 Weekday Batches
🕖 Monday to Friday | 7:00PM – 9:00PM
📅 Weekend Special Batches
🕖 Saturday | 4:00 PM – 9:00 PM
🕖 Sunday | 10:00 AM – 3:00 PM
Total Course Duration : 6 Months
Call to Action:
Live Virtual Classes
Course Starts At:
₹17,999
EMI Options Available
Starts At ₹3500 / month
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