Master in AI/ML ,Deep Learning, Generative AI & Agentic AI in just 4 Months ...

✅ Live Online Classes
✅ Hands-on Projects & Real Case Studies
✅ Training by Industry Experts
✅ Generative AI & Agentic AI Specialization
✅ LMS Access with Recordings
✅ Live Industry Mentorship
✅ Profile Building & Placement Assistance

Complete Generative AI & Agentic AI Master Program

Master AI, Machine Learning, Deep Learning, Generative AI, and Agentic AI in just 4 months with our industry-oriented program. Learn through live online classes, hands-on projects, expert mentorship, and a complete job-ready curriculum covering Python, Statistics, ML, DL, GenAI, and Agents. Gain end-to-end practical skills and real industry experience, designed to fast-track your career growth and make you fully placement-ready.

Program Objectives

Build strong foundations in Python, Statistics, Machine Learning, Deep Learning, Generative AI, and Agentic AI.
Develop hands-on expertise through real-world projects and industry case studies.
Master modern AI tools, frameworks, and production-ready workflows.
Learn to design, train, evaluate, and deploy ML and AI models effectively.
Gain practical skills in LLMs, Prompt Engineering, RAG, Agents, and Multimodal AI.
Strengthen problem-solving, analytical thinking, and data-driven decision-making.
Prepare students for AI/ML job roles with profile building and placement assistance.
Enable learners to confidently work on end-to-end AI/ML pipelines and real business scenarios.

Course Curriculum

What you will learn in Program?

PART 1: PYTHON FOR DATA SCIENCE

SECTION 1: PYTHON FUNDAMENTALS

1.1 Introduction & Setup
Installing Python, IDE setup, virtual environments
1.2 Variables & Data Types
Numbers, Strings, Boolean, Type Casting
1.3 Operators
Arithmetic, Comparison, Logical, Assignment, Identity, Membership
1.4 Control Flow
If-else, Loops (for, while), Break, Continue, Pass

SECTION 2: DATA STRUCTURES

2.1 Lists
Indexing, slicing, methods, list comprehension
2.2 Tuples
Immutable sequences, unpacking
2.3 Dictionaries
Key-value storage, dictionary methods, nested dictionaries
2.4 Sets
Unique elements, set operations
2.5 Strings
String methods, formatting, slicing

SECTION 3: FUNCTIONS

3.1 Functions
Defining functions, arguments, return values, lambda functions, scope, recursion

SECTION 4: FILE HANDLING

4.1 File Operations
Read, Write, Append, File modes
4.2 Working with File Types
TXT, CSV, JSON
4.3 Error Handling
Try-Except blocks, finally, raise

SECTION 5: NUMPY

5.1 NumPy Basics
Arrays, array creation, shapes, dtypes
5.2 Array Operations
Indexing, slicing, broadcasting
5.3 Mathematical Operations
Vectorized operations, aggregation functions
5.4 Random Module
Random numbers, distributions, seed

SECTION 6: PANDAS

6.1 Pandas Fundamentals
Series, DataFrames, basic operations
6.2 Data Cleaning
Handling missing data, duplicates, type conversions
6.3 Data Transformation
Apply, map, replace, renaming, sorting
6.4 Data Aggregation & Grouping
GroupBy, aggregation functions
6.5 Merging & Joining
Merge, join, concatenate
6.6 Data Import/Export
CSV, Excel, JSON, SQL

SECTION 7: DATA VISUALIZATION

7.1 Matplotlib
Basic plots, labels, titles, styling
7.2 Seaborn
Statistical plots, styling, heatmaps, pairplots

PART 2: STATISTICS FOR MACHINE LEARNING & DATA SCIENCE

SECTION 1: BASIC STATISTICS

What is Statistics?
Types of Data
Descriptive vs Inferential Statistics
Measures of Central Tendency (Mean, Median, Mode)
Measures of Dispersion (Range, Variance, SD)
Quartiles, Percentiles & IQR
Outlier Detection
Sampling Methods
Data Visualization Basics

SECTION 2: PROBABILITY

Probability Basics & Rules
Conditional Probability
Bayes’ Theorem
Independence vs Dependence
Joint, Marginal & Conditional Distributions
Expected Value & Variance
Discrete Distributions (Binomial, Poisson, etc.)
Continuous Distributions (Normal, Exponential, etc.)
Cumulative Distribution Function (CDF)
Law of Large Numbers

SECTION 3: STATISTICAL INFERENCE

Central Limit Theorem
Standard Error vs Standard Deviation
Confidence Intervals
Hypothesis Testing Framework
Type I and Type II Errors
Statistical Power
Effect Size
P-values & Significance Levels
One-tailed vs Two-tailed Tests
Practical vs Statistical Significance

SECTION 4: PARAMETRIC TESTS

Z-Test
One-Sample T-Test
Two-Sample T-Test
Paired T-Test
One-Way ANOVA
Two-Way ANOVA
Post-Hoc Tests
Testing Assumptions (Normality, Homoscedasticity, Independence)

SECTION 5: NON-PARAMETRIC TESTS

When to Use Non-Parametric Tests
Mann-Whitney U Test
Wilcoxon Signed-Rank Test
Kruskal-Wallis Test
Sign Test

SECTION 6: CATEGORICAL DATA TESTS

Chi-Square Test of Independence
Chi-Square Goodness of Fit
Fisher’s Exact Test
McNemar’s Test

SECTION 7: MULTIPLE TESTING

Multiple Testing Problem
Bonferroni Correction
False Discovery Rate (FDR)

SECTION 8: REGRESSION & CORRELATION

Types of Correlation (Pearson, Spearman, Kendall)
Simple Linear Regression
Multiple Linear Regression
Regression Assumptions
R-Squared & Adjusted R-Squared
Residual Analysis
Multicollinearity & VIF
Polynomial Regression
Logistic Regression Basics
Regression Diagnostics

SECTION 9: DISTRIBUTION PROPERTIES

Skewness
Kurtosis
Q-Q Plots
Normality Tests (Shapiro-Wilk, Anderson-Darling)
Transformations for Normality (Log, Box-Cox, Yeo-Johnson)

SECTION 10: DATA VISUALIZATION

Essential Plot Types (Bar, Histogram, Boxplot, Scatter, etc.)
Advanced Visualizations (Heatmaps, Pairplots, Violin, KDE)
Identifying Patterns, Trends & Anomalies

SECTION 11: ML-SPECIFIC STATISTICS

Maximum Likelihood Estimation (MLE)
Bias-Variance Tradeoff
Cross-Validation Methods
Bootstrap & Resampling
Covariance Matrices
Information Theory (Entropy, KL Divergence)
PCA Statistical Foundations
Model Evaluation Metrics (AUC, F1, RMSE, etc.)

SECTION 12: TIME SERIES STATISTICS

Autocorrelation (ACF)
Partial Autocorrelation (PACF)
Stationarity
Moving Averages
Trend & Seasonality

PART 3: MACHINE LEARNING COMPLETE

SECTION 1: DATA PREPROCESSING & FOUNDATIONS

1.1 What is Statistics?
1.2 Types of Data
1.3 Descriptive vs Inferential Statistics
1.4 Measures of Central Tendency
1.5 Measures of Dispersion
1.6 Quartiles, Percentiles & IQR
1.7 Outlier Detection
1.8 Sampling Methods
1.9 Data Visualization Basics

SECTION 2: SIMPLE SUPERVISED LEARNING

2.1 Simple Linear Regression
2.2 Multiple Linear Regression
2.3 Ridge Regression (L2)
2.4 Lasso Regression (L1)
2.5 Elastic Net
2.6 Polynomial Regression
2.7 Logistic Regression (Binary)
2.8 Multiclass Logistic Regression
2.9 Evaluation Metrics for Regression

SECTION 3: CLASSIFICATION METRICS & VALIDATION

3.1 Classification Metrics
3.2 ROC Curves & AUC
3.3 Precision–Recall Curves
3.4 Cross-Validation
3.5 Bias–Variance Tradeoff

SECTION 4: HANDLING REAL-WORLD PROBLEMS

4.1 Class Imbalance Problem
4.2 Resampling Techniques
4.3 Algorithmic Approaches
4.4 Evaluation for Imbalanced Data

SECTION 5: TREE-BASED MODELS

5.1 Decision Trees
5.2 Random Forest
5.3 Bagging & Bootstrap Aggregating
5.4 AdaBoost
5.5 Gradient Boosting Machines (GBM)
5.6 XGBoost
5.7 LightGBM
5.8 CatBoost
5.9 Ensemble Methods (Stacking, Voting)

SECTION 6: OTHER SUPERVISED ALGORITHMS

6.1 Naive Bayes
6.2 K-Nearest Neighbors (KNN)
6.3 Support Vector Machines (SVM)

SECTION 7: ADVANCED TOPICS

7.1 Feature Engineering
7.2 Feature Selection Methods
7.3 Principal Component Analysis (PCA)
7.4 t-SNE
7.5 Linear Discriminant Analysis (LDA)
7.6 UMAP

SECTION 8: UNSUPERVISED LEARNING

8.1 K-Means Clustering
8.2 K-Means++ Initialization
8.3 Hierarchical Clustering
8.4 DBSCAN
8.5 HDBSCAN
8.6 Gaussian Mixture Models (GMM)
8.7 Isolation Forest
8.8 Local Outlier Factor (LOF)
8.9 One-Class SVM

SECTION 9: MODEL INTERPRETATION & DEPLOYMENT

9.1 Feature Importance
9.2 Permutation Importance
9.3 SHAP
9.4 LIME
9.5 Partial Dependence Plots (PDP)
9.6 Grid Search
9.7 Randomized Search
9.8 Bayesian Optimization
9.9 Scikit-Learn Pipelines

SECTION 10: TIME SERIES (OPTIONAL)

10.1 Time Series Components
10.2 Stationarity & Differencing
10.3 ACF & PACF
10.4 Moving Averages
10.5 Exponential Smoothing
10.6 ARIMA
10.7 SARIMA
10.8 Prophet
10.9 LSTM for Time Series

PART 4: DEEP LEARNING COMPLETE

SECTION 2: NATURAL LANGUAGE PROCESSING (NLP)

2.1 Text Preprocessing
2.2 Text Representation
2.3 Recurrent Neural Networks (RNN)
2.4 Long Short-Term Memory (LSTM)
2.5 Gated Recurrent Unit (GRU)
2.6 Bidirectional RNNs
2.7 Encoder–Decoder Architecture
2.8 Attention Mechanism
2.9 Sequence-to-Sequence Models
2.10 Practice Projects

SECTION 3: TRANSFORMERS & MODERN NLP

3.1 Transformer Architecture
3.2 Self-Attention Mechanism
3.3 Multi-Head Attention
3.4 BERT
3.5 GPT
3.6 T5
3.7 Model Variants (RoBERTa, DistilBERT, ALBERT)
3.8 Transfer Learning & Fine-Tuning
3.9 Hugging Face Transformers
3.10 Practice Projects

SECTION 4: COMPUTER VISION

4.1 Convolutional Neural Networks (CNNs)
4.2 Convolution & Pooling Operations
4.3 LeNet & AlexNet
4.4 VGGNet
4.5 ResNet
4.6 Inception / GoogleNet
4.7 MobileNet & EfficientNet
4.8 Transfer Learning in Vision
4.9 Data Augmentation
4.10 Object Detection (YOLO, Faster R-CNN)
4.11 Image Segmentation (U-Net, Mask R-CNN)
4.12 Vision Transformers (ViT)
4.13 Practice Project

PART 5: GENERATIVE AI & AGENTIC AI

SECTION 1: TRANSFORMER ARCHITECTURE MASTERY

1.1 Attention Mechanisms
1.2 Positional Encodings
1.3 Encoder–Decoder Architecture
1.4 Tokenization
1.5 Transformer Variants

SECTION 2: LARGE LANGUAGE MODELS (LLMs)

2.1 LLM Architecture Components
2.2 Context Window & Memory Management
2.3 Sampling & Generation
2.4 Alignment Techniques
2.5 Advanced Architectures
2.6 Model Optimization

SECTION 3: PROMPT ENGINEERING

3.1 Prompt Fundamentals
3.2 Prompting Strategies
3.3 Advanced Reasoning Techniques
3.4 Structured Outputs
3.5 Prompt Security & Optimization

SECTION 4: AGENTIC AI FOUNDATIONS

4.1 Agent Fundamentals
4.2 Perception-Action Loop
4.3 Planning & Reasoning
4.4 Agent Capabilities
4.5 Agent Memory Systems
4.6 Agent State Management

SECTION 5: AGENT ARCHITECTURES

5.1 Single-Agent Systems
5.2 ReAct Agent Implementation
5.3 Multi-Agent Systems
5.4 Hierarchical Agents
5.5 Manager–Worker Patterns
5.6 Collaborative vs Competitive Agents
5.7 Advanced Agent Patterns

SECTION 6: AGENTIC FRAMEWORKS

6.1 LangChain
6.2 LangGraph
6.3 AutoGen
6.4 CrewAI
6.5 Framework Comparison
6.6 Human-in-the-Loop Integration

SECTION 7: RETRIEVAL-AUGMENTED GENERATION (RAG)

7.1 RAG Fundamentals
7.2 Embeddings & Similarity
7.3 Vector Databases
7.4 Document Processing
7.5 Retrieval Techniques
7.6 Advanced RAG Patterns
7.7 Graph RAG
7.8 Context Optimization
7.9 RAG Evaluation

SECTION 8: MULTIMODAL AI

8.1 Multimodal Fundamentals
8.2 Vision-Language Models
8.3 Image Generation & Editing
8.4 Audio Processing
8.5 Video Understanding
8.6 Multimodal RAG
8.7 Document Understanding
8.8 Multimodal Prompting

SECTION 9: FINE-TUNING & MODEL OPTIMIZATION

9.1 Fine-Tuning Fundamentals
9.2 Dataset Preparation
9.3 Parameter-Efficient Fine-Tuning (PEFT)
9.4 Instruction Tuning
9.5 Supervised Fine-Tuning (SFT)
9.6 Alignment Fine-Tuning
9.7 Catastrophic Forgetting
9.8 Continual Learning
9.9 Knowledge Distillation
9.10 Model Merging

SECTION 10: MULTI-AGENT SYSTEMS

10.1 Multi-Agent Architecture
10.2 Communication Protocols
10.3 Agent Routing & Orchestration
10.4 Role Design & Delegation
10.5 Coordination Mechanisms

SECTION 11: AGENT MEMORY & PERSISTENCE

11.1 Memory Architecture Design
11.2 Short-Term Memory
11.3 Long-Term Memory
11.4 Episodic Memory
11.5 Semantic Memory
11.6 Memory Operations
11.7 Memory Summarization
11.8 Cross-Session Persistence
11.9 State Management

SECTION 12: REAL-TIME & EVENT-DRIVEN AGENTS

12.1 Event-Driven Architecture
12.2 Webhooks
12.3 FastAPI for Agent APIs
12.4 WebSocket Connections
12.5 Background Tasks & Async Processing
12.6 Streaming Responses
12.7 External Integrations

SECTION 13: OBSERVABILITY & MONITORING

13.1 Observability Fundamentals
13.2 Tracing & Debugging
13.3 LangSmith Platform
13.4 Helicone Analytics
13.5 Grafana Dashboards

SECTION 14: SAFETY & GUARDRAILS

14.1 AI Safety Principles
14.2 Content Moderation
14.3 Input Validation & Sanitization
14.4 Output Validation
14.5 Prompt Injection Defense
14.6 Jailbreak Prevention
14.7 Hallucination Mitigation
14.8 Constitutional Constraints
14.9 Safety Classifiers
14.10 Human Oversight
14.11 Red-Teaming

SECTION 15: EVALUATION & TESTING

15.1 Evaluation Framework Design
15.2 Offline Evaluation
15.3 Online Evaluation
15.4 LLM-as-Judge
15.5 AutoGen Evaluators
15.6 RAG-Specific Evaluation
15.7 Agent Evaluation
15.8 Human Evaluation

SECTION 16: DOMAIN-SPECIFIC APPLICATIONS

16.1 Finance & Banking
16.2 Healthcare & Clinical
16.3 Legal Applications
16.4 Retail & E-Commerce
16.5 Technology & Software Development
16.6 Education & Tutoring
16.7 Business Intelligence & Analytics
16.8 Content Creation & Marketing
16.9 Research & Knowledge Work

SECTION 17: MODEL CONTEXT PROTOCOL (MCP)

17.1 MCP Fundamentals
17.2 MCP Server Development
17.3 MCP Client Integration
17.4 LLM Connectors
17.5 MCP Security
17.6 MCP Deployment

SECTION 18: SPECIALIZED AGENT PATTERNS

18.1 SQL Agents
18.2 Code Analysis & Review Agents
18.3 Test Generation Agents
18.4 Documentation Agents
18.5 Research & Synthesis Agents
18.6 Data Curation Agents
18.7 Recommendation Agents
18.8 Planning & Scheduling Agents

What requirement do you need?

There are no prerequisites to attend this course online or ofline.
Elementary programming knowledge will be of advantage.

Training Supports & Benefits

Faculty are from IIT & IIM

Learn from the World’s Best Faculty & Industry Experts
Learn with fun Hands-on Exercises & Assignments
Participate in Hackathons & Group Activities

One to One Support

Dedicated Faculty
9AM to 6 PM Support
Participate in Hackathons & Group Activities

Career Assistance

Resume Building & Mock Interview Preparation.
We offer personalized access to our Learning Management System (LMS).
Moc Interview Practise + Real time Test

Our Student Placed in...

Students Reviews

I saw DV Analytics is a best platform to build your career. Deb Sir and other faculty of this institution are very friendly and helping in nature. They guide us in every steps. They teach us in easy way how we catch this topic very thoroughly. After every session they provide us assignments which will make us strong in technical. Believe me, I am from Commerce background, but in this technical I am strong now because of faculties. They provide us materials, recording videos. Recording videos has no limits. Multiple times you can listen it and make notes and do assignments in time. That will help you any company you go i.e Product based or Service based. After study they provide how to create a profile in Linked In, Resume Preparation for your interviews. That's all about DV Analytics. Being a Data scientist DV Analytics is a best platform. So, if you want to grow your career join DV Analytics as soon as possible. Thank You !

Sagar Mohakul

I'm a student at DV Analytics right now, and I couldn't be more satisfied. I wholeheartedly endorse DV Analytics to anyone wishing to advance their data analytics skills, whether they are novices or seasoned professionals. The course material is interesting and applicable. Deb Sir is a true Data Scientist expert, with a wealth of knowledge and experience in the field. Thank you, DV Analytics!

ajit

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I am a DV Student and having a great experience of learning. I have built a good programming skill. Everything seemed impossible and I was not so confident about my programming skills before joining DV but now the things have changed....I am confident enough with my programming skills. A Special Thanks to Dev Sir our Teacher who worked so hard for each and every student to be successful

Abdul Sahid
DV Analytics is a best Data Science Institute. With a wide and extraordinary classes, they also helps us with business development strategies, projects across different industries.

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I would like to thank DV Analytics support staff and faculties especially Dev sir. I joined DV recently and I was not confident because of my non IT background but after attending regular classes and doing assignments I feel very confident that I can be a data scientist. This is one of the best institute to learn data science for IT as well as non IT students.

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Reflecting on my time at DV Analytics Training Institute, it was a truly quality and interesting experience. The curriculum was enriching, and the engaging learning environment made every aspect of the training enjoyable. Grateful for the valuable skills gained during this memorable journey

Dvanalytics Sales 1

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If you really think that it's hard to be a data scientist or data Analyst then Dv Analytics is the right place to join to know how easy it is to be a data analyst . I am saying because earlier I myself even though that coding is really hard but the day I joined and started learning i came to know it's all easy if you get a right tutor because the way they will nurture you in this 6 month's will definitely make you get placed in some good MNC. #Dv Analytics #Dvtian

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Great place to learn! Dev Sir is very committed to every student's success. They have started offering online classes since the pandemic began. All classes are live, and all doubts are clarified. The class material and assignments is more than adequate for you to grasp all concepts. I would highly recommend for anyone interested in Data Science.

Shreejil PV
Certainly! DV Analytics stands out as the best data science training institute with placement support. The comprehensive curriculum, expert trainers, and hands-on projects provide a robust learning experience. The institute's emphasis on real-world skills and industry connections ensures students are well-prepared for the job market. The dedicated support staff and effective placement assistance make it an ideal choice for aspiring data scientists. I am grateful for my experience there, and I am now confidently pursuing a career in data science, all thanks to DV Analytics.

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