Artificial intelligence courses

Master Artificial Intelligence
With Industry Experts

Industry-focused AI program covering Python, Machine Learning, Computer Vision, NLP, and Generative AI with 5 months of in-depth training + LIVE project mentoring, hands-on cloud lab practice, and industry certification.

(36,100 reviews)

Choose Your Learning Path

Flexible plans designed to fit your schedule and learning style

Blended Learning

Self Learning + Live Mentoring

₹45,005

Self Learning + Live Mentoring
NASSCOM® Certification
1 Year Access To Elearning
10 Capstone & 1 Client Project
Job Assistance
24*7 Leaner assistance and support

Live Virtual

Instructor Led Live Online

₹70,005

NASSCOM® Certification
9-Month | 780 Learning Hours
100-Hour Live Online Training10
Capstone & 1 Client Project
365 Days Flexi Pass + Cloud Lab
Internship + Job Assistance

Classroom

In-Person Classroom Training

₹81,005

NASSCOM® Certification
9-Month | 780 Learning Hours
100-Hour Classroom Sessions
10 Capstone & 1 Client Project
Cloud Lab Access
Internship + Job Assistance

Tools & Technologies You'll Master

Python Programming

My SQL & No SQL

Power BI

Tableau

Statistics & R

TensorFlow

Why Skill AI Institute?

Industry-leading training backed by certification and real-world projects.

Expert Trainers

Ph.ds And Industry Experts

Elite faculty from prestigious
Universities with deep research
And coaching expertise

Career Guidance

Expert Counselors

Personalized counselling for career Enhancement in managerial roles

Specialized Syllabus

Specialized Syllabus For Managers

Focused on data science for decision making, Managing data science projects with essential technical overview

5 Case Studies

Practical Decision-making Cases

Techniques for scenarios with certainty, Low uncertainty and high certainty from Decision tree to monte carlo simulation

Flexible Financing Options

We’re dedicated to making our programs accessible. Pay in easy installments at 0% interest with no hidden costs.

EMI Available

Bajaj Finserv & ShopSe

Admission Closing

31st December 2026

Course Syllabus

Artificial Intelligence Foundation – 6 Modules

Module 1: Artificial Intelligence Overview

• Evolution Of Human Intelligence
• What Is Artificial Intelligence?
• History Of Artificial Intelligence
• Why Artificial Intelligence Now?
• Areas Of Artificial Intelligence
• AI Vs Data Science Vs Machine Learning

Module 2: Deep Learning Introduction

• Deep Neural Network
• Machine Learning vs Deep Learning
• Feature Learning in Deep Networks
• Applications of Deep Learning Networks

Module 3: Tensorflow Foundation

• TensorFlow Structure and Modules
• Hands-On:ML modeling with TensorFlow

Module 4: Computer Vision Introduction

• Image Basics
• Convolution Neural Network (CNN)
• Image Classification with CNN
• Hands-On: Cat vs Dogs Classification with CNN Network

Module 5: Natural Language Processing (Nlp)

• NLP Introduction
• Bag of Words Models
• Word Embedding
• Hands-On:BERT Algorithm

Module 6: Ai Ethical Issues And Concerns

• Issues And Concerns Around Ai
• Ai And Ethical Concerns
• Ai And Bias
• Ai:Ethics, Bias, And Trust

Python Foundation – 4 Modules

Module 1: Python Basics

• Introduction of python
• Installation of Python and IDE
• Python Variables
• Python basic data types
• Number & Booleans, strings
• Arithmetic Operators
• Comparison Operators
• Assignment Operators

Module 2: Python Control Statements

• IF Conditional statement
• IF-ELSE
• NESTED IF
• Python Loops basics
• WHILE Statement
• FOR statements
• BREAK and CONTINUE statements

Module 3: Python Data Structures

• Basic data structure in python
• Basics of List
• List: Object, methods
• Tuple: Object, methods
• Sets: Object, methods
• Dictionary: Object, methods

Module 4: Python Functions

• Functions basics
• Function Parameter passing
• Lambda functions
• Map, reduce, filter functions

Statistics Essentials – 4 Modules

Module 1: Overview Of Statistics

• Introduction to Statistics
• Descriptive And Inferential Statistics
• Basic Terms Of Statistics
• Types Of Data

Module 2: Harnessing Data

• Random Sampling
• Sampling With Replacement And Without Replacement
• Cochran’s Minimum Sample Size
• Types of Sampling
• Simple Random Sampling
• Stratified Random Sampling
• Cluster Random Sampling
• Systematic Random Sampling
• Multi stage Sampling
• Sampling Error
• Methods Of Collecting Data

Module 3: Exploratory Data Analysis

• Exploratory Data Analysis Introduction
• Measures Of Central Tendencies: Mean,Median And Mode
• Measures Of Central Tendencies: Range, Variance And Standard Deviation
• Data Distribution Plot: Histogram
• Normal Distribution & Properties
• Z Value / Standard Value
• Empirical Rule and Outliers
• Central Limit Theorem
• Normality Testing
• Skewness & Kurtosis
• Measures Of Distance: Euclidean, Manhattan And Minkowski Distance
• Covariance & Correlation

Module 4: Hypothesis Testing

• Hypothesis Testing Introduction
• P- Value, Critical Region
• Types of Hypothesis Testing
• Hypothesis Testing Errors : Type I And Type II
• Two Sample Independent T-test
• Two Sample Relation T-test
• One Way Anova Test
• Application of Hypothesis testing

Machine Learning Associate – 9 Modules

Module 1: Machine Learning Introduction

• What Is ML? ML Vs AI
• Clustering, Classification And Regression
• Supervised Vs Unsupervised

Module 2: Python Numpy Package

• Introduction to Numpy Package
• Array as Data Structure
• Core Numpy functions
• Matrix Operations, Broadcasting in Arrays

Module 3: Python Pandas Package

• Introduction to Pandas package
• Series in Pandas
• Data Frame in Pandas
• File Reading in Pandas
• Data munging with Pandas

Module 4: Visualization With Python - Matplotlib

• Visualization Packages (Matplotlib)
• Components Of A Plot, Sub-Plots
• Basic Plots: Line, Bar, Pie, Scatter

Module 5: Python Visualization Package - Seaborn

• Seaborn: Basic Plot
• Advanced Python Data Visualizations

MODULE 6: Ml Algo: Linear Regresssion

• Introduction to Linear Regression
• How it works: Regression and Best Fit Line
• Modeling and Evaluation in Python

MODULE 7: Ml Algo: Logistic Regression

• Introduction to Logistic Regression
• How it works: Classification & Sigmoid Curve
• Modeling and Evaluation in Python

MODULE 8: Ml Algo: K Means Clustering

• Understanding Clustering (Unsupervised)
• K Means Algorithm
• How it works : K Means theory
• Modeling in Python

MODULE 9: Ml Algo: Knn

• Introduction to KNN
• How It Works: Nearest Neighbor Concept
• Modeling and Evaluation in Python

Machine Learning Expert – 7 Modules

Module 1: Feature Engineering

• Introduction to Feature Engineering
• Feature Engineering Techniques: Encoding, Scaling, Data Transformation
• Handling Missing values, handling outliers
• Creation of Pipeline
• Use case for feature engineering

Module 2: Ml Algo: Support Vector Machine (Svm)

• Introduction to SVM
• How It Works: SVM Concept, Kernel Trick
• Modeling and Evaluation of SVM in Python

Module 3: Principal Component Analysis (Pca)

• Building Blocks Of PCA
• How it works: Finding Principal Components
• Modeling PCA in Python

Module 4: Ml Algo: Decision Tree

• Introduction to Decision Tree & Random Forest
• How it works
• Modeling and Evaluation in Python

Module 5: Ensemble Techniques - Bagging

• Introduction to Ensemble technique
• Bagging and How it works
• Modeling and Evaluation in Python

Module 6: Ml Algo: Naïve Bayes

• Introduction to Naive Bayes
• How it works: Bayes’ Theorem
• Naive Bayes For Text Classification
• Modeling and Evaluation in Python

Module 7: Gradient Boosting, Xgboost

• Introduction to Boosting and XGBoost
• How it works?
• Modeling and Evaluation of in Python

Advanced Data Science – 8 Modules

Module 1: Time Series Forecasting - Arima

• What is Time Series?
• Trend, Seasonality, cyclical and random
• Stationarity of Time Series
• Autoregressive Model (AR)
• Moving Average Model (MA)
• ARIMA Model
• Autocorrelation and AIC
• Time Series Analysis in Python

Module 2: Sentiment Analysis

• Introduction to Sentiment Analysis
• NLTK Package
• Case study: Sentiment Analysis on Movie Review

Module 3: Regular Expressions With Python

• Regex Introduction
• Regex codes
• Text extraction with Python Regex

Module 4: Ml Model Deployment With Flask

• Introduction to Flask
• URL and App routing
• Flask application – ML Model deployment

Module 5: Advanced Data Analysis With Ms Excel

• MS Excel core Functions
• Advanced Functions (VLOOKUP, INDIRECT..)
• Linear Regression with EXCEL
• Data Table
• Goal Seek Analysis
• Pivot Table
• Solving Data Equation with EXCEL

Module 6: Aws Cloud For Data Science

• Introduction of cloud
• Difference between GCC, Azure, AWS
• AWS Service ( EC2 instance)

Module 7: Azure For Data Science

• Introduction to AZURE ML studio
• Data Pipeline
• ML modeling with Azure

Module 8: Introduction To Deep Learning

• Introduction to Artificial Neural Network, Architecture
• Artificial Neural Network in Python
• Introduction to Convolutional Neural Network, Architecture
• Convolutional Neural Network in Python

Database: Sql And Mongodb – 7 Modules

Module 1: Database Introduction

• DataBase Overview
• Key concepts of database management
• Relational Database Management System
• CRUD operations

Module 2: Sql Basics

• Introduction to Databases
• Introduction to SQL
• SQL Commands
• MY SQL workbench installation

Module 3: Data Types And Constraints

• Numeric, Character, date time data type
• Primary key, Foreign key, Not null
• Unique, Check, default, Auto increment

Module 4: Databases And Tables (Mysql)

• Create database
• Delete database
• Show and use databases
• Create table, Rename table
• Delete table, Delete table records
• Create new table from existing data types
• Insert into, Update records
• Alter table

Module 5: Sql Joins

• Inner Join, Outer Join
• Left Join, Right Join
• Self Join, Cross join
• Windows function: Over, Partition, Rank

Module 6: Sql Commands And Clauses

• Select, Select distinct
• Aliases, Where clause
• Relational operators, Logical
• Between, Order by, In
• Like, Limit, null/not null, group by
• Having, Sub queries

Module 7 : Document Db/no-sql Db

• Introduction of Document DB
• Document DB vs SQL DB
• Popular Document DBs
• MongoDB basics
• Data format and Key methods

Git – 5 Modules

Module 1: Git Introduction

• Purpose of Version Control
• Popular Version control tools
• Git Distribution Version Control
• Terminologies
• Git Workflow
• Git Architecture

Module 2: Git Repository And Github

• Git Repo Introduction
• Create New Repo with Init command
• Git Essentials: Copy & User Setup
• Mastering Git and GitHub

Module 3: Commits, Pull, Fetch And Push

• Code Commits
• Pull, Fetch and Conflicts resolution
• Pushing to Remote Repo

Module 4: Tagging, Branching And Merging

• Organize code with branches
• Checkout branch
• Merge branches
• Editing Commits
• Commit command Amend flag
• Git reset and revert

Big Data Foundation – 3 Modules

Module 1: Big Data Introduction

• Big Data Overview
• Five Vs of Big Data
• What is Big Data and Hadoop
• Introduction to Hadoop
• Components of Hadoop Ecosystem
• Big Data Analytics Introduction

Module 2 : Hdfs And Map Reduce

• HDFS – Big Data Storage
• Distributed Processing with Map Reduce
• Mapping and reducing stages concepts
• Key Terms: Output Format, Partitioners,
• Combiners, Shuffle, and Sort

Module 3: Pyspark Foundation

• PySpark Introduction
• Spark Configuration
• Resilient distributed datasets (RDD)
• Working with RDDs in PySpark
• Aggregating Data with Pair RDDs

Bi Analyst – 4 Modules

Module 1: Tableau Fundamentals

• Introduction to Business Intelligence & Introduction to Tableau
• Interface Tour, Data visualization: Pie chart, Column chart, Bar chart.
• Bar chart, Tree Map, Line Chart
• Area chart, Combination Charts, Map
• Dashboards creation, Quick Filters
• Create Table Calculations
• Create Calculated Fields
• Create Custom Hierarchies

Module 2: Power-bi Basics

• Power BI Introduction
• Basics Visualizations
• Dashboard Creation
• Basic Data Cleaning
• Basic DAX Function

Module 3 : Data Transformation Techniques

• Exploring Query Editor
• Data Cleansing and Manipulation:
• Creating Our Initial Project File
• Connecting to Our Data Source
• Editing Rows
• Changing Data Types
• Replacing Values

Module 4: Connecting To Various Data Sources

• Connecting to a CSV File

Artificial Intelligence(Ai) Expert – 11 Modules

Module 1: Neural Networks

• Structure of neural networks
• Neural network – core concepts(Weight initialization)
• Neural network – core concepts(Optimizer)
• Neural network – core concepts(Need of activation)
• Neural network – core concepts(MSE & RMSE)
• Feed forward algorithm
• Backpropagation

Module 2: Implementing Deep Neural Networks

• Introduction to neural networks with tf2.X
• Simple deep learning model in Keras (tf2.X)
• Building neural network model in TF2.0 for MNIST dataset

Module 3: Deep Computer Vision - Image Recognition

• Convolutional neural networks (CNNs)
• CNNs with Keras-part1
• CNNs with Keras-part2
• Transfer learning in CNN
• Flowers dataset with tf2.X(part-1)
• Flowers dataset with tf2.X(part-2)
• Examining x-ray with CNN model

Module 4 : Deep Computer Vision - Object Detection

• What is Object detection
• Methods of Object Detections
• Metrics of Object detection
• Bounding Box regression
• labelimg
• RCNN
• Fast RCNN
• Faster RCNN
• SSD
• YOLO Implementation
• Object detection using cv2

Module 5: Recurrent Neural Network

• RNN introduction
• Sequences with RNNs
• Long short-term memory networks(part 1)
• Long short-term memory networks(part 2)
• Bi-directional RNN and LSTM
• Examples of RNN applications

Module 6: Natural Language Processing (Nlp)

• Introduction to Natural language processing
• Working with Text file
• Working with pdf file
• Introduction to regex
• Regex part 1
• Regex part 2
• Word Embedding
• RNN model creation
• Transformers and BERT
• Introduction to GPT (Generative Pre-trained Transformer)
• State of art NLP and projects

Module 7: Prompt Engineering

• Introduction to Prompt Engineering
• Understanding the Role of Prompts in AI Systems
• Design Principles for Effective Prompts
• Techniques for Generating and Optimizing Prompts
• Applications of Prompt Engineering in Natural Language Processing

Module 8: Reinforcement Learning

• Markov decision process
• Fundamental equations in RL
• Model-based method
• Dynamic programming model free methods

Module 9: Deep Reinforcement Learning

• Markov decision process
• Fundamental equations in RL
• Model-based method
• Dynamic programming model free methods

TESTIMONIALS

What Our Students Have To Say

Skill AI

4.7

Based on 12 reviews

review us on

Ashok Bhattacharya

13:31 11 Jan 26

One of the best Ai training institute, The best part is the live project and the practical learning experience leading to placement

Aarti Jaiswar

09:39 09 Jan 26

Skill AI has very professional placement services. Their internships are very fruitful and really helped boost my profile. After completing the internship, I started receiving many interview calls and messages from different companies. Highly recommended for career growth.

Nusrat Shaikh

09:28 09 Jan 26

Skill AI's placement service is simply wow. They created my profile like an experienced professional and provided training in the same way.
Because of this, I am attracting many companies for job interviews on LinkedIn.
Truly impressed with their support and guidance.

Archana Jaiswar

09:27 09 Jan 26

Skill Ai has very professional placement services. Their internships are very fruitful and really helped boost my profile.After completing the internships, I started receiving many interviews calls and messages from different companies. Highly recommended for career growth.