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
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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
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:
Alumni's Testimonies
I loved that it wasn’t just theory. From data cleaning to model deployment, every module included real datasets and projects. By the end, I felt ready to tackle real-world data challenges
Shivaansh Ravi Kandali
SDE 1
I had no prior experience in programming or statistics, but the course made everything so approachable. Step-by-step explanations and interactive exercises helped me build my first predictive models within weeks.
Zainab Solanki
Data Scientist II
Frequently Asked Questions
This course provides a complete learning experience in Data Science and Machine Learning, covering everything from data collection and cleaning to building predictive and intelligent models.
It is ideal for students, professionals, and tech enthusiasts who want to gain expertise in handling data, developing algorithms, and applying AI-driven techniques to solve real-world problems.
The course runs for 6 months, combining interactive classes, hands-on assignments, and real-world projects for complete practical exposure
The total course fee is ₹18,000, which includes access to study materials, live sessions, and a recognized certification.
You’ll learn Python for data science, data preprocessing, exploratory data analysis (EDA), machine learning algorithms, model evaluation, and deployment using tools like Pandas, NumPy, and Scikit-learn.
No prior coding experience is required. The course begins with basic concepts and gradually moves into advanced machine learning and model optimization techniques.
You’ll work on real-world projects, including predictive analytics, customer segmentation, and model-based automation for business insights.
After successful completion, you’ll receive an industry-recognized Data Science and Machine Learning Certificate from HITMI, validating your technical and analytical skills.
This course provides in-depth knowledge of Cybersecurity and Ethical Hacking, helping learners understand how to identify, prevent, and respond to digital threats and security breaches.After completing the course, you’ll receive an industry-recognized Cloud Computing with AI Certification from HITMI, validating your technical expertise.
You can enroll directly through our official website or visit our nearest HITMI training center. Our counselors will guide you through the registration and payment process.