Master Artificial Intelligence & Machine Learning with Real-World Applications

Insta InfoTech® presents an advanced AI & Machine Learning course for students and professionals. Learn the fundamentals of AI, machine learning algorithms, and deep learning architectures.

Gain expertise in Python, TensorFlow, Keras, and Scikit-Learn for model building. Explore real-world applications in Natural Language Processing (NLP), Computer Vision, and Predictive Analytics. Develop AI-driven solutions with hands-on projects and industry case studies.

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AI & Machine Learning

Master the complete AI and Machine Learning stack — from Python and mathematics foundations through supervised and unsupervised learning, into deep neural networks with TensorFlow and PyTorch, NLP, computer vision, generative AI, and MLOps deployment — building production-grade intelligent systems that power recommendation engines, image classifiers, language models, and autonomous decision-making applications used across every industry.

Certificate

You will get Medhavi Skills Certificate

Partnership

Insta Infotech × Medhavi Skills University

Recognition

NCrF aligned learning pathway

AI & ML intelligence stack

Python, Maths & Classical ML

NumPy, pandas, sklearn, regression, SVM, ensembles

Learn

Deep Learning — CNNs, RNNs & Transformers

TensorFlow, PyTorch, NLP, computer vision, LLMs

Build

Generative AI, MLOps & Deployment

GANs, RAG, LLM fine-tuning, model APIs, and CI/CD

Deploy

Learn, build, and deploy — every AI concept taught through a real model that runs, predicts, and ships.

Program overview

More than fitting a model and calling it AI.

This course treats AI and Machine Learning as an engineering discipline, not a black box. Students build from Python and mathematical foundations through every classical ML algorithm, into the architecture of deep neural networks, and finally to the cutting edge — transformers, large language models, generative AI, and the MLOps pipeline that takes a trained model from notebook to production API — all grounded in real datasets and real business problems.

01 / Build, train, and evaluate

Every model runs on real data.

Every algorithm — from linear regression to transformer attention — is implemented, trained, and evaluated on real datasets. You understand the maths, write the code, measure the results, and improve the model.

02 / Think in model architecture

Why this layer? Why this loss?

Understand the design decisions behind neural network architectures — why CNNs use convolutions for images, why RNNs and LSTMs handle sequences, why attention mechanisms in transformers outperform both — and apply that reasoning to choose the right model for any problem.

03 / Career signal

Portfolio and interview prep

Build and present trained ML models, deep learning projects, and a generative AI capstone — interview-ready for ML engineer, AI developer, data scientist, NLP engineer, and computer vision roles across product, research, and enterprise AI teams.

Learning journey

A clearer learning flow.

The progression builds from Python, maths, and EDA, through classical ML algorithms, into deep learning architectures, NLP and computer vision, and finishes with generative AI, LLM applications, MLOps, and a full-scale AI capstone project.

Step 01

Python, Maths & EDA

Python for AI — NumPy, pandas, matplotlib, seaborn; linear algebra, probability, statistics; exploratory data analysis, feature engineering, and data preprocessing.

Step 02

Classical Machine Learning

Supervised — linear/logistic regression, decision trees, SVM, random forests, XGBoost; unsupervised — k-means, hierarchical clustering, PCA; model evaluation, cross-validation, and hyperparameter tuning with scikit-learn.

Step 03

Deep Learning — Neural Networks

Neural network architecture, activation functions, backpropagation, feedforward networks, CNNs for image classification, RNNs and LSTMs for sequences — implemented in TensorFlow and PyTorch.

Step 04

NLP, Computer Vision & Transformers

NLP — text preprocessing, word embeddings, BERT, GPT; computer vision — object detection, transfer learning; transformer architecture, attention mechanisms, and fine-tuning pre-trained models.

Step 05

Generative AI, MLOps & Capstone

GANs, VAEs, LLM fine-tuning, RAG pipelines, prompt engineering, model deployment with FastAPI, MLflow, Docker, and a capstone end-to-end AI application.

Syllabus

Structured for job-relevant output.

Topics organized into clean thematic groups so the section feels premium and is easy to scan.

Python and classical ML

NumPy, pandas, EDA, and feature engineering
Supervised — regression, trees, SVM, XGBoost
Unsupervised — clustering, PCA, model evaluation

Deep learning and AI

CNNs, RNNs, LSTMs — TensorFlow and PyTorch
NLP — BERT, GPT, transformers, and fine-tuning
Computer vision — object detection, transfer learning

GenAI and launch

GANs, VAEs, RAG, and LLM fine-tuning
MLOps — MLflow, FastAPI, Docker deployment
Capstone end-to-end AI application

Certification and recognition

Make the credential visible, not buried.

You will receive a Medhavi Skills Certificate. The program is presented through Insta Infotech's partnership with Medhavi Skills University, and the learning pathway is aligned with the National Credit Framework (NCrF).

Why this matters

Certificate visibility
Now highlighted in the hero and repeated in the certification block.

University partnership
Medhavi Skills University is treated as a trust signal, not a small footer note.

Recognition cue
NCrF alignment is framed as a formal learner benefit.

Module 1: Fundamentals of Computing and Programming

Introduction to Computing Systems
Describe computer hardware and software components
Define operating systems and file management concepts
Explain data representation and storage methods
Demonstrate installation and configuration of software tools
Organize digital files and folders systematically
Logic Building and Algorithms
Define programming logic and flowchart concepts
Describe algorithm development techniques
Create flowcharts for problem-solving scenarios
Develop algorithms for simple computational problems
Python Programming Basics
Describe Python syntax and programming constructs
Define variables, data types, and operators
Write Python scripts using loops and conditions
Debug Python code for syntax and logical errors

Module 2: Foundations of Data Science

Introduction to Data Science
Understand the Data Science lifecycle: data collection, preprocessing, EDA, modelling, and deployment
Distinguish roles: Data Analyst, Data Scientist, ML Engineer, and Data Engineer
Identify real-world applications across healthcare, finance, e-commerce, and social media
Analyse a real-world Data Science project case study and map lifecycle phases
Discuss role responsibilities using sample job descriptions
Present findings on how DS is applied in a chosen industry domain
Data Types & Sources
Differentiate structured, unstructured, and semi-structured data (CSV, JSON, XML)
Identify primary vs secondary data sources
Explore open datasets: Kaggle, UCI, and government data portals
Load and inspect a CSV and JSON file in Jupyter Notebook
Classify datasets from three domains by type and quality
Document dataset characteristics: size, format, and observed quality issues
Ethics & Privacy
Understand algorithmic bias and its societal implications
Study GDPR principles: data protection, consent, and anonymisation
Review responsible AI guidelines and real-world case studies on biased systems
Analyse a biased AI case study (e.g., facial recognition disparity)
Evaluate a sample dataset for potential bias indicators
Draft a short ethical-use policy for a hypothetical data project
Development Setup
Overview of the Python data science ecosystem: Jupyter, Anaconda, and VS Code
Understand virtual environments and package management using pip and conda
Introduction to Markdown for notebook documentation
Install Anaconda and configure a virtual environment
Create a Jupyter Notebook with markdown cells, code cells, and inline outputs
Install and import core libraries: NumPy, Pandas, and Matplotlib

Module 3: Programming with Python

Core Concepts
Understand Python syntax, indentation rules, and primitive data types (int, float, str, bool)
Study arithmetic, comparison, logical, and assignment operators
Understand type casting and dynamic typing
Write programs using all data types and operators
Develop a basic calculator application
Solve beginner-level exercises on type conversion and operator precedence
Control Flow
Learn if-elif-else decision structures
Understand for and while loops with break, continue, and pass statements
Study nested loops and introduction to list comprehension syntax
Build a number-guessing game using loops and conditionals
Implement FizzBuzz and multiplication tables using nested loops
Write list comprehensions to filter and transform datasets
Collections
Understand list, tuple, dictionary, and set data structures
Study mutability, ordering, indexing, and built-in methods for each type
Learn when to choose each collection structure based on use case
Build a student grade management system using dictionaries and lists
Perform CRUD operations on each collection type
Solve problems requiring set operations: union, intersection, and difference
Modular Programming
Understand function definition, parameters, return values, and scope
Learn *args, **kwargs, lambda functions, and higher-order functions (map, filter, reduce)
Study file modes and reading/writing CSV and Excel files
Write reusable utility functions for data cleaning tasks
Read a CSV file, process rows, and write results to a new file
Use lambda and map() to transform a list of records

Module 4: Object-Oriented Programming

Classes & Objects
Understand the four pillars of OOP: encapsulation, abstraction, inheritance, polymorphism
Define classes, instance variables, constructors (__init__), and the self keyword
Distinguish between class attributes and instance attributes
Model a Library Management System with Book, Member, and Library classes
Create multiple object instances and test object interaction
Implement __str__ and __repr__ methods for readable object output
Inheritance & Polymorphism
Understand single, multilevel, and multiple inheritance
Study method overriding and the super() function
Learn polymorphism through duck typing and Method Resolution Order (MRO)
Build an Employee class hierarchy: Employee → Manager → Director
Demonstrate polymorphic behaviour by calling overridden methods on different subclasses
Implement a shape calculator using polymorphism
Encapsulation & Abstraction
Understand access modifiers in Python using naming conventions (public, protected, private)
Learn property decorators (@property, @setter) for controlled attribute access
Study abstract classes and interfaces using the abc module
Implement a BankAccount class with private balance and property-based deposit/withdraw
Create an abstract Shape class with area() implemented by Circle, Rectangle, and Triangle
Test encapsulation by attempting direct attribute access and observing behaviour
Exception Handling
Understand Python's exception hierarchy and common built-in exceptions
Learn try-except-else-finally blocks and how to raise custom exceptions
Understand logging best practices for error tracking
Build a file reader with custom exceptions for missing files and invalid formats
Add structured exception handling to the BankAccount class
Implement a logging wrapper to capture and record runtime errors

Module 5: Mathematics & Statistics

Vectors & Matrices
Understand scalars, vectors, and matrices and their mathematical properties
Study matrix operations: addition, subtraction, multiplication, transpose, and inverse
Learn dot product, determinants, and their relevance in machine learning
Perform matrix operations manually and verify results using NumPy
Solve a system of linear equations using matrix methods
Visualise vector addition and linear transformation using Matplotlib
Descriptive Statistics
Study measures of central tendency (mean, median, mode) and dispersion (variance, std dev, IQR)
Understand skewness, kurtosis, and outlier detection using IQR and Z-score methods
Learn about quartiles and percentiles in the context of data distribution
Compute descriptive statistics on a real-world dataset (e.g., housing prices)
Identify outliers using IQR and Z-score techniques
Create box plots and histograms to visualise data distribution
Distributions
Study discrete distributions: Binomial and Poisson
Study continuous distributions: Normal, Uniform, and Exponential
Understand the Central Limit Theorem and fundamentals of hypothesis testing (p-value, significance level)
Plot probability density functions for Normal and Binomial distributions
Simulate sampling distributions using NumPy
Perform a t-test and interpret the p-value in context
Probability Theory
Understand sample space, events, probability axioms, and probability rules
Study conditional probability, Bayes' theorem, and its real-world applications
Learn about random variables and expected value
Simulate coin toss and dice roll experiments using Python's random module
Apply Bayes' theorem to a spam classification scenario
Calculate conditional probabilities from a given confusion matrix

Module 6: Data Manipulation & Analysis

Excel Basics,Functions & Formulas
Workbook, worksheet, data types
SUM, IF, VLOOKUP, HLOOKUP, COUNTIF
SUMIFS, AVERAGEIFS,AND,OR,NOT
LEN,RIGHT,LEFT,MID, ROUND,CEIL,TODAY,NOW
Data entry & formatting,Solve business problems
Prepare Multiple Sheet (Electrical Sheet,Shopping,Marksheet)
Invoice
Data Cleaning
Importance of clean data in analytics and decision-making
Sorting, filtering, duplicates,Charts, pivot charts
Handling blank cells, null values, and incorrect formats
Text cleaning techniques using functions (TRIM, CLEAN, SUBSTITUTE, LEFT, RIGHT, MID)
Clean a raw dataset containing missing values and duplicates
Convert text data into proper numeric/date formats
Handle errors using IFERROR and conditional formulas
Apply Data Validation (dropdown lists, restrictions) Prepare a final cleaned dataset ready for analysis
Array Operations
Understand ndarray creation methods: zeros, ones, arange, linspace, and random
Learn array indexing, slicing, boolean masking, and shape manipulation
Understand broadcasting rules for operations between arrays of different shapes
Create multi-dimensional arrays and perform slicing and masking operations
Apply broadcasting to perform element-wise operations between arrays
Reshape arrays and apply axis-based aggregation functions
Mathematical Operations
Study element-wise arithmetic and universal functions (ufuncs)
Understand vectorised operations and their performance advantage over Python loops
Learn aggregation functions (sum, mean, std) and linear algebra operations in NumPy
Benchmark vectorised NumPy operations vs Python loops for performance
Implement matrix multiplication from scratch and compare with np.dot()
Compute correlation coefficients and aggregate statistics using NumPy
Data Handling
Understand Pandas Series and DataFrame structures
Learn data loading methods: read_csv(), read_excel(), read_json(), and read_sql()
Study index management, column selection, and loc vs iloc for data access
Load datasets in three different formats (CSV, Excel, JSON)
Explore data using head(), info(), describe(), and shape
Practise column selection, row filtering, and index resetting on a real dataset
Identify and handle missing values using dropna(), fillna(), and interpolation
Detect and remove duplicate records
correct data types and normalise string fields
Understand outlier treatment strategies within a DataFrame context
Clean a messy real-world dataset: handle nulls, fix categories, remove duplicates, correct dtypes
Document data quality issues and the steps taken to resolve them
Produce and export a clean, analysis-ready DataFrame
Data Transformation
Study merge, join, and concat operations for combining DataFrames
Understand groupby mechanics and aggregation: sum, mean, count, and custom functions
Learn pivot tables, melt (wide-to-long transformation), and apply() for column transformation
Merge two datasets on a common key and verify join correctness
Compute group-level sales aggregations using groupby()
Transform wide survey data to long format using melt() and create a pivot table

Module 7: Data Modeling and Advanced Visualization

Charts
Understand chart selection principles: bar, line, pie, scatter, and histogram
Study Matplotlib's figure and axes architecture and formatting options (labels, titles, legends)
Understand subplot layouts and figure composition
Reproduce five chart types on a sales dataset with proper formatting
Customise colours, labels, and annotations for clarity
Create a 2×2 subplot grid comparing data trends across categories
Statistical Plots
Understand when to use heatmaps, pair plots, violin plots, and KDE plots
Study correlation matrices and their interpretation in multivariate analysis
Overview of Plotly for creating interactive charts
Generate a correlation heatmap for a multivariate dataset
Create pair plots and violin plots to explore feature distributions
Build a basic interactive chart using Plotly Express
Dashboard Design
Understand dashboard types: operational, strategic, and analytical
Study UI/UX principles for dashboards: layout, colour hierarchy, and responsiveness
Overview of Streamlit and Dash frameworks for Python-based dashboarding
Design a dashboard wireframe for a defined business scenario
Build a two-page Streamlit app with filters, charts, and KPI cards
Present the dashboard and explain design and metric selection decisions

Module 8: Database Management

DB Concepts
Understand relational database concepts: tables, rows, columns, primary and foreign keys
Study ER diagrams, entity relationships, and normalisation forms (1NF, 2NF, 3NF)
Overview of RDBMS platforms: MySQL, PostgreSQL, and SQLite
Design an ER diagram for a college management system
Normalise a denormalised table through all three normal forms
Implement the normalised schema in MySQL Workbench
Query Writing
Learn DDL (CREATE, ALTER, DROP), DML (INSERT, UPDATE, DELETE), and DQL (SELECT)
Study WHERE, ORDER BY, GROUP BY, and HAVING clauses with aggregate functions
Understand all JOIN types: INNER, LEFT, RIGHT, FULL OUTER, and SELF JOIN
Write 20 progressively complex SQL queries on a retail database
Solve multi-table JOIN problems across three related tables
Generate sales summary reports using GROUP BY and HAVING clauses
Advanced SQL
Understand subqueries (correlated and non-correlated) and CTEs using the WITH clause
Study window functions: ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD, and SUM OVER
Learn indexing strategies and query optimisation using EXPLAIN PLAN
Refactor nested subqueries into CTEs for improved readability
Implement window functions to calculate running totals and rank products by sales
Add indexes and compare query execution times using EXPLAIN
Python + SQL
Understand database connectivity using sqlite3 and SQLAlchemy
Learn to execute queries from Python and load results into Pandas DataFrames
Understand parameterised queries for SQL injection prevention and ORM fundamentals
Connect Python to a SQLite database and execute CRUD operations programmatically
Pull query results into a DataFrame for downstream analysis
Automate a weekly sales report using a Python-SQL data pipeline

Module 9: Data Visulization

Introduction to Power BI
Overview of BI concepts
Components: Desktop, Service, Mobile
Use cases in business analytics
Install and explore Power BI interface
Load sample dataset
Data Import & Transformation
Data sources and connectivity
ETL concepts
Power Query fundamentals
Import data from Excel/CSV
Clean and transform using Power Query
Data Modeling & DAX Fundamentals
Relationships, schema design
Star vs Snowflake schema
Measures vs Calculated Columns
Basic DAX functions
Create table relationships
Build data model
Create measures (SUM, COUNT, IF)
Build calculated columns
Data Visualization & Dashboard Development
Chart types and selection
Report vs Dashboard
KPI concepts
Interactive reporting
Create charts, slicers, filters
Design report pages
Build multi-page dashboard
Add slicers and drill-down
Publishing & Sharing & Security & Governance
Power BI Service overview
Data refresh concepts
Row-level security basics
Publish report online
Share dashboard
Apply role-based filtering

Module 10: Machine Learning

Introduction to ML
Distinguish supervised, unsupervised, semi-supervised, and reinforcement learning
Understand the ML workflow: problem definition, data collection, EDA, modelling, evaluation, and deployment
Study the bias-variance tradeoff and an overview of the Scikit-learn API
Classify ten real-world problems into appropriate ML categories
Walk through a complete Scikit-learn pipeline on the Iris dataset: load, split, train, predict, evaluate
Discuss and document common model failure modes with mitigation strategies
Regression
Understand simple and multiple linear regression and OLS estimation
Study regression assumptions and regularisation techniques: Ridge (L2), Lasso (L1), ElasticNet
Learn evaluation metrics: R², MAE, RMSE, and MAPE
Build a house price prediction model using multiple features
Apply Ridge and Lasso regression and compare performance with baseline
Plot actual vs predicted values and analyse residual plots for model diagnostics
Classification
Understand logistic regression, sigmoid function, KNN, and the curse of dimensionality
Study Decision Tree (Gini, Entropy), Random Forest, and SVM (hyperplane, margin, kernel trick)
Learn evaluation metrics: accuracy, precision, recall, F1-score, ROC-AUC, and confusion matrix
Build a customer churn classifier using logistic regression as a baseline
Compare KNN, Decision Tree, and Random Forest on the same dataset
Tune hyperparameters using GridSearchCV
plot and compare ROC curves
Clustering
Understand clustering objectives and distance metrics: Euclidean, Manhattan, and cosine
Study K-Means algorithm, centroid initialisation (K-Means++), and convergence criteria
Learn Elbow Method and Silhouette Score
overview of DBSCAN and Hierarchical Clustering
Apply K-Means clustering to a customer segmentation dataset
Use the Elbow Method to determine optimal K value
Visualise clusters in 2D using PCA-reduced features and compare K-Means vs DBSCAN
Performance Metrics
Understand train-test split and cross-validation techniques: k-fold and stratified
Study overfitting and underfitting diagnosis using learning curves
Learn confusion matrix interpretation, precision-recall tradeoff, and SHAP basics for explainability
Evaluate three models using 5-fold cross-validation and compare results
Plot learning curves to diagnose overfitting vs underfitting
Use Random Forest feature importance and basic SHAP values to explain model predictions

Module 11: AI - Artificial Intelligence

Introduction to AI
Understand AI history and the distinction between Narrow AI, General AI, and Superintelligence
Study AI subfields: ML, Deep Learning, NLP, Computer Vision, and Robotics
Understand AI search algorithms (BFS, DFS, A*) and knowledge representation concepts
Explore existing AI systems: chatbots, image recognition APIs, and recommendation engines
Implement a BFS-based pathfinder in Python
Map AI subfields to real-world product examples in a structured presentation
LLM Concepts
Understand the Transformer architecture: attention mechanism, self-attention, and positional encoding
Study the evolution from GPT to modern LLMs (GPT-4, Claude, Gemini)
Learn pretraining, fine-tuning, RLHF, tokenisation, context windows, and sampling parameters
Interact with the OpenAI and Anthropic APIs programmatically
Vary temperature and top-p parameters and compare output behaviour
Analyse token usage, estimate API costs, and compare outputs from multiple LLMs on the same prompt
Interaction Design
Understand prompt anatomy: instruction, context, input, and output format
Study zero-shot, few-shot, and chain-of-thought prompting techniques
Learn role prompting, system-level instructions, and prompt injection risks
Design and test prompts for five tasks: summarisation, classification, code generation, Q&A, and creative writing
Apply few-shot and chain-of-thought techniques and compare output quality
Build a reusable prompt template library with systematic evaluation criteria
Real-world Use Cases
Study AI applications in healthcare, finance, retail, and education
Understand responsible AI deployment: model explainability and compliance considerations
Learn the end-to-end lifecycle of a production AI system
Build a domain-specific Q&A chatbot using an LLM API with a custom system prompt
Integrate basic retrieval logic and deploy to a Streamlit interface
Document design decisions, limitations, and ethical considerations in a structured report

Module 12: Capstone Projects with AI & GenAI

End-to-End ML Project
Apply the complete Data Science workflow: scoping, data collection, EDA, feature engineering, modelling, evaluation, and deployment
Understand project documentation best practices and professional presentation skills
Study model selection and hyperparameter tuning strategies
Build a complete ML project (e.g., credit risk prediction or disease diagnosis) from data to deployment
Train, compare, and tune at least three models
deploy the best model as a REST API
Submit a project report with EDA insights, model comparison, and GitHub repository
Dashboard Project
Understand KPI definition, goal alignment, and BI dashboard design principles
Study data modelling for reporting: star schema, measures, and dimensions
Overview of Power BI or Tableau for professional business dashboards
Design and build an interactive dashboard on a business dataset (e.g., e-commerce or HR analytics)
Define 5+ KPIs and implement drill-down filters and time-series trend views
Present the dashboard with a narrative explaining insights and business recommendations
AI/NLP Project
Apply NLP and Generative AI concepts in an integrated capstone project
Understand RAG (Retrieval-Augmented Generation) architecture for knowledge-grounded chatbots
Study evaluation techniques for conversational AI: BLEU, ROUGE, and human evaluation
Build a domain-specific AI assistant using an LLM API combined with a vector database (FAISS or ChromaDB)
Implement document ingestion, embedding, retrieval, and answer generation pipeline
Deploy as a Streamlit or Gradio app
evaluate for accuracy, relevance, and safety
present to the cohort

Module 13: Industry Project and Professional Skills

AI/ML Solution Development
Describe project lifecycle and documentation methods
Define solution architecture planning
Build end-to-end AI/ML capstone project
Document technical project workflow
Communication and Workplace Readiness
Describe workplace communication standards
Define interview and presentation techniques
Prepare technical presentations
Demonstrate interview and teamwork skills

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Professional Diploma in AI-Powered Digital Marketing

Advanced Course UI UX Design

HTML, DHTML

Cascading Style Sheets (CSS)

Java Script, jQuery

Bootstrap

XML, JSON