Now Aligned To Microsoft Official Curriculum
Accelerate your AI career with globally aligned certification pathways, enterprise-ready data science skills, and advanced GenAI workflows.
After completing this program, you’ll be able to:
Create Enterprise-ready AI and GenAI workflows
Prepare for Official Microsoft Certification
Get Azure, Azure AI, Azure Data ecosystem exposure
Design and implement end-to-end data science pipelines
Apply appropriate machine learning algorithms to solve business problems
Integrate generative AI capabilities into data workflows
Communicate technical findings to non-technical stakeholders
Develop data visualization dashboards that drive decision-making
Collaborate effectively in cross-functional teams
Azure fundamentals exposure
Azure AI exposure
Advanced prompt engineering workflows
Generative AI productivity frameworks
Retrieval Augmented Generation (RAG) concepts
Python and key data science libraries
SQL for data querying
Tableau/PowerBI for visualization
Machine learning frameworks
Cloud-based data platforms
Version control systems
Generative AI APIs and frameworks
Work on real-world projects that mirror current industry challenges:
Retail Sales & Customer Intelligence Dashboard
Employee Attrition Prediction System
Real Estate Price Intelligence Platform
Manufacturing Defect Detection System
AI Resume Analyzer & Job Match System
Enterprise Knowledge Assistant (RAG-Based Chatbot)
Expanded preparation for roles such as:
AI / GenAI Engineer
Machine Learning Engineer
AI Solutions Associate
AI Workflow Specialist
NLP Engineer
Data & AI Consultant
AI Research Associate
Basic Syntax, Variables and Data Types, Operators
Conditional Statements, Loops, Control Flow Statements: break and continue
Lists, Tuples, Sets, and Dictionaries
Functions
File Handling
Exception Handling
Classes and Objects
Python Testing
Application development using Python Framework – FlasK API
Hands on Project – Build a mini, beginner friendly project with Python (Mini Project 1)
RDBMS Concepts
Database design and modelling
DDL, DML, DQL and TCL Statements in SQL
SQL Joins and Subqueries
Backup and Restore
Window Functions
Hands on Project – Build a mini project querying and manipulating data from a sample database. (Mini Project 2)
Core Statistics – Descriptive stats, probability distributions, hypothesis testing.
EDA with Python( Numpy, Pandas)
Data Loading, Cleaning and Preprocessing
Understanding Data with Statistical Summary
Univariate, Bivariate and Multivariate Analysis
Understanding Data Visualization using matplotlib, seaborn and pyplot
Mini Project – Analyze real-world datasets to extract insights and prepare data for modelling.(Mini Project 3)
What is Artificial Intelligence?
Machine Learning vs. Deep Learning vs. Data Science
Data preparation and Feature Engineering
Balancing dataset using SMOTE
Supervised Machine Learning
Regression and classification algorithms
Linear Models, Logistic Regression, KNN, SVM, Decision Trees, Naive Bayes
Performance Metrics
Unsupervised Learning
Clustering algorithms (K-Means, Agglomerative Hierarchical clustering, …)
Dimensionality Reduction using PCA
Association Rule Mining using APRIORI
Hands on Project – Build and deploy a predictive model using Scikit-Learn. (Mini Project 4)
Improving the Performance of the Models
Ensemble Methods – Bagging, Boosting and Stacking
Random Forest Classifier
Hyperparameter Tuning
Techniques to Tune Hyperparameters – Manual Search, Grid Search, Random Search
Hands on Project – Improve the performance of the models created in the earlier project (Mini Project 5)
Collecting data using Webscraping Techniques
BeautifulSoup
Hands on Project – Scraping data, apply ML and deploy the application (Mini Project 6)
The Neural Network
Artificial Neural Network (ANN)
Convolutional Neural Network (CNN)
Recurrent Neural Network and LSTM
Hands on Project – Build and Deploy Neural network models (Mini Project 7)
Introduction to NLP
Building Blocks of NLP – Corpus, Tokenization, Stop Words, Stemming, Lemmatization, POS Tagging, Named Entity Recognition (NER)
Word Embeddings
Sentiment Analysis
Bag of Words, Word2Vec
Hands on Project – Build and Deploy an application that uses NLP techniques (Mini Project 8)
Introduction to Generative AI
Generative Models
Generative Adversarial Networks (GAN)
GPT and Transformers
VAE
Large Language Models
Gen AI Tools
Prompt Engineering Basics
Hands on Project – Build and Deploy Gen AI applications (Mini Project 9)
Tableau Overview
Data Sources
Collecting and Assembling Data
Creating Visualizations
Filters
Dashboard
Hands on Project – Create Dashboards and Reports using Tableau (Mini Project 10)
Describe Cloud Concepts – Cloud Computing, Cloud Services, Cloud Service Types
Azure Architecture and Services – Core Components, Compute and Networking, Azure Storage
Principles of Responsible AI:
Implement AI Solutions by Using Microsoft Foundry
Model Deployment and Configuration
Interfacing with Models
Content and Data Extraction Using analyzers (audio, document, image, video), defining schemas, and encoding images.
Python Basics: Interpreting Python SDK code syntax, understanding how code calls and interacts with AI models and Azure Content Understanding services.
ML Developer
Category Growth Analyst
Internal Audit Data Analyst, Grade Asst Manager
Scholar Trainee - WILP
Technical Consultant
Jr. Data Analyst
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