Advanced Program in Data Science, ML and Generative AI
Duration: 380 Hrs
Certificate of Completion
4.6/5
Overall Ratings by our Students
Expert Faculty
Live Session
Small Batch Size
Job Oriented Programs
Overview
Our program is designed to equip learners with comprehensive knowledge and hands-on skills across entire data science and AI life cycle. The program aims to build strong foundations in data analysis, statistics and programming, while progressively advancing into machine learning, deep learning, natural language processing and generative AI.
Through real life projects, learner will gain experience in solving business problems, deploying models on cloud platforms.
Course Curriculum
- Excel Basics Refresher
- Formulas & Functions
- Lookup & Reference Mastery
- Data Cleaning & Transformation
- Power Query for ETL
- Statistics in Excel
- Data Modelling & Forecasting
- Data Visualization & Storytelling
- Excel Automation
- Integration with Other
- Mini Project
- Introduction to Databases & SQL
- Basic SQL Queries
- SQL Operators
- Joins & Relationships
- Aggregation & Grouping
- Subqueries & Nested Queries
- Functions in SQL
- Views, Stored Procedures & Triggers
- Advanced SQL for Data Science
- SQL Performance Tuning
- Mini Project
- Introduction to Power BI & Workflow
- Connecting to Data Sources
- Data Modelling & Relationships
- Introduction to DAX (Data Analysis Expressions)
- Advanced DAX
- Data Visualization & Storytelling
- AI Features in Power BI
- Security & Deployment
- Mini Project
- Introduction to Python & Environment Setup
- Python Basics
- Working with Data Structures
- Object-Oriented Programming (OOP)
- Working with External Files
- NumPy for Numerical Computing
- Pandas for Data Analysis
- Data Visualization
- Introduction to Statistics in Python
- Automation & APIs
- Role of Statistics in Data Science
- Descriptive Statistics
- Probability Basics
- Inferential Statistics
- Hypothesis Testing
- Statistical Modeling
- Linear Algebra Fundamentals
- Linear Algebra in ML
- Mini Project
- Introduction to Machine Learning
- Types of Machine Learning
- ML Workflow
- Data Preprocessing & Feature Engineering
- Introduction to Regression
- Introduction to Classification
- Introduction to Clustering
- Model Evaluation & Validation
- Hyperparameter Tuning Basics
- Introduction to ML Libraries –
- Recap of ML Fundamentals
- Linear Regression Deep Dive
- Logistic Regression
- Decision Trees
- Random Forests
- Support Vector Machines
- K-Nearest Neighbors
- Gradient Boosting
- Time Series Forecasting
- Mini Project
- Introduction to Unsupervised Learning
- K-Means Clustering
- Hierarchical Clustering
- DBSCAN & Density-Based Methods
- Dimensionality Reduction
- t-SNE & UMAP for Visualization
- Anomaly Detection with Isolation Forest
- Mini Project
- Introduction to Deep Learning
- Neural Network Architecture & Forward Propagation
- Backpropagation & Optimization
- Building Neural Networks in PyTorch
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs) & LSTMs
- Transformers & Attention Mechanisms
- Transfer Learning & Fine-Tuning
- Model Evaluation, Overfitting, Regularization
- TensorFlow/Keras Overview (Awareness)
- Mini Project
- Introduction to NLP
- Text Preprocessing
- Text Representation
- Sequence Modeling with RNNs & LSTMs
- Attention Mechanism
- Transformers in NLP
- Pre-trained Language Models
- NLP Project
- Introduction to Generative AI
- Generative Models Overview
- Large Language Models (LLMs) Fundamentals
- Prompt Engineering
- Fine-Tuning & Adaptation of LLMs
- Generative AI for Text
- Generative AI for Images
- Responsible AI & Ethics
- Generative AI Capstone Project
- Introduction to MLOps
- Model Packaging
Course Highlights
Data Science
Machine Learning
NLP
Generative AI
Course Fees
Get yourself enrolled now!
₹1,50,000/-
*Additional GST @ 18%
Course Certificate
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Career Prospects
Our learning-based model opens up multiple avenues and opportunities for students.
Data Analyst
BI Analyst
Data Scientist
AI/ML Engineer
Data Engineer
Applied AI Specialist
Quantitative Analyst
Machine Learning Engineer
Students Feedback
I did the C++ course from DVOC. it was a very well-structured and well-designed course. It made my concepts so clear that I could ace my tests and I'm so grateful to DVOC for this. It also made me really comfortable with the basics, which in turn made it easier and fun to learn other programming languages. The explanations and helpful advice are way beyond anything I have ever received during school. Also, a special shout out to the faculty for being patient while I made 76238 errors.
The Graphic Designing course that I did from DVOC gave me a huge advantage to my skill set. The faculty that taught me were amazing and the way they taught was fun and insightful at the same time. This Graphic designing course actually helped me to present my projects and assignments in a much better and more detailed way. I want to thank DVOC for guiding me and giving me my times worth.
I did a graphics designing course from DVOC. It was a very informative course that cleared all the bases and even though some new tips and tricks. It made me really confident and well-versed with the system. Even the faculty were great they were able to clear all my doubts and were patient to correct my errors.Overall I had a great experience with DVOC and would definitely recommend it to others.
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