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Master Artificial Intelligence! — Get Predictive & Automation Skills in Months!

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AI & ML Course Overview

The HCL GUVI AI & ML Bundle gives you complete access to a comprehensive learning path. Master Python, Machine Learning, Deep Learning, NLP, Computer Vision, LLMs, and AI-powered application development. Learn at your own pace, build hands-on projects, gain industry-recognized skills, and prepare for real-world tech challenges.

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AI & ML Bundle Curriculum

What You Will Learn in the AI & ML Bundle

Introduction to Machine Learning

4 Modules | 4 Hours

Practical Machine Learning

4 Modules | 9 Hours

Natural Language Processing Using Python

4 Modules | 8 Hours

AI Fundamentals

4 Modules | 5 Hours

Deep Learning Fundamentals

4 Modules | 4 Hours

Deep Learning with TensorFlow

4 Modules | 4 Hours

Virtual AI Assistant Powered by GPT3 API & Python

4 Modules | 4 Hours

Vertex AI

4 Modules | 14 Hours

Generative AI

4 Modules | 5 Hours

LangChain

1 Modules | 2 Hours

Scikit-Learn for Machine Learning Part-1

4 Modules | 3 Hours

Web Development

34 Courses

English, Tamil, Telugu, Kannada, Malayalam & Hindi

Data Science

9 Courses

English, Tamil, Telugu, Kannada, Malayalam & Hindi

Database & Cloud Computing

19 Courses

English, Tamil, Telugu, Hindi & Bengali

Programming

16 Courses

Available in English, Tamil, Telugu, Kannada, Malayalam & Hindi

Network & Security

4 Courses

Available in English, Hindi & Bengali                                             

UI/UX Design

4 Courses

Available in English & Hindi                                                                    

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Earn Industry-Recognized Certificates

Each time you complete a course in the AI & ML Bundle, you’ll receive a professional certificate issued by HCL GUVI. All certificates are ISO-certified, globally recognized, and shareable on LinkedIn and resumes, helping you showcase your skills, strengthen your profile, and stand out to recruiters.

Earn Industry-Recognized Certificates

Each time you complete a course, you’ll receive a professional certificate issued by HCL GUVI (an IIT-M & IIM-A incubated EdTech company). All certificates are ISO-certified, industry-recognized, and easily shareable on LinkedIn and in your resume, helping you enhance your profile and stand out to recruiters.

Industry-Recognized Credentials

Get certificates that are valued by recruiters and tech companies.

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Proof of Real Skills

Every certificate reflects practical knowledge and tools you’ve mastered — evidence of real, job-ready skills.

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Earn Industry-Recognized Certificates

Each time you complete a course in the AI & ML Bundle, you’ll receive a professional certificate issued by HCL GUVI. All certificates are ISO-certified, globally recognized, and shareable on LinkedIn and resumes, helping you showcase your skills, strengthen your profile, and stand out to recruiters.

Industry-Recognized Credentials

Get certificates that are valued by recruiters and tech companies.

Shareable on LinkedIn

Add every completed-course certificate to LinkedIn and impress potential employers with consistent growth.

Proof of Real Skills

Every certificate reflects practical knowledge and tools you’ve mastered — evidence of real, job-ready skills.

Resume-Ready Format

Download each certificate instantly and attach it to job applications, portfolios, and resumes effortlessly.

Certify Your Skills with the Premium Pass

Bonus Features You Get

CodeKata

An interactive coding platform with 1500+ curated problems by IT experts. CodeKata tracks your progress and builds a coding profile to highlight your skills for job opportunities.

WebKata

WebKata is a developing practice platform for beginners, built with top-notch web development problem statements focusing on Modern HTML, CSS, and JavaScript. 

SQLKata

SQLKata is a hands-on tool to master SQL through concept-driven exercises. It builds skills in query writing, optimization, and real-world database management.

Debugging

Debugging a series of programs curated by industry experts. Practicing debugging will help you get started and become familiar with programming. 

What Our Learners Say

Real Stories. Real Outcomes.

"You will be lucky to find quality courses on HCL GUVI. I must say that I was really lucky to get courses at such a low and discounted price. I got more than 9 hours of video and around 20 more resources to gain knowledge of programming. HCL GUVI provides a wide variety of courses both technical and non-technical. I suggest HCL GUVI is the best choice."

IT Analyst

"What makes HCL GUVI different is that it provides vernacular courses which make it easy to understand & practice platforms like CodeKata & WebKata, all these helped me improve my practical programming skills in the front end, back end. I highly recommend it if you want to get started with something new."

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"I have opted for a combo-course that starts with the basics of the popular technologies and made me a pro in its domain... Codekata is very damn helpful to crack placements because it improved my efficiency in coding... we just need to be persistent and that’s all it takes. I am very happy to encounter HCL GUVI!"

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Frequently Asked Questions

What do I get with this bundle?

You get full access to all AI & ML courses — from Python and Machine Learning to Deep Learning, NLP, Computer Vision, and building real AI-powered applications. Every course has hands-on projects, assignments, expert guidance, and certificates when you finish.

Do I need to pay every month?

Nope! Just a one-time payment of ₹999. No hidden fees or subscriptions.

How long can I access the courses?

Forever! Learn at your own pace, anytime and anywhere.

Will I get certificates?

Yes! Every course you complete gives you an industry-recognized certificate you can share on LinkedIn or your resume

Are the lessons live or pre-recorded?

All lessons are pre-recorded, so you can study whenever it works for you — mornings, evenings, or weekends.

Can I practice what I learn?

Absolutely! You’ll work on real projects, hands-on labs, and assignments so you can actually apply your skills.

What if I don’t like the bundle?

No worries! There’s a 7-day money-back guarantee. If it’s not for you, just ask for a refund within the first week.

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Introduction to Machine Learning Syllabus:
Beginner Module
  • Introduction to Machine Learning
  • Types of ML- Supervised Learning
  • Types of ML- Unsupervised Learning
  • Types of ML - Reinforcement Learning
Intermediate Module
  • Data Handling in ML - Introduction
  • Data Handling in ML - Data Cleaning
  • Supervised Learning - Linear Regression
  • Supervised Learning - Decision Tree & Ensemble Methods
Advanced Module
  • Unsupervised Learning - Clustering
  • Unsupervised Learning - Association
  • Unsupervised Learning - PCA
  • Model Evaluation
Expert Module
  • Case Study- 1 E-Commerce Recommendation
  • Case Study- 2 Heart Disease Prediction
  • Case Study- 3 Finance Fraud Detection
  • Introduction to Generative AI
  • Advanced Topics - Transfer Learning
  • Advanced Topics - AutoML

Practical Machine Learning Syllabus:

Beginner Module
  • Course Intro
  • Machine Learning Refresher - Intro, Types & Applications
  • Machine Learning Refresher - Linear Regression
  • Machine Learning Refresher - Logistic Regression
  • Machine Learning Project LifeCycle
  • ML Model Training Process
  • Training a Classification Task - Python Implementation
  • Gradient Descent - Error Surfaces
  • Gradient Descent - Computation Graphs
  • Gradient Descent - Algorithm, Geometric Intuition
Intermediate Module
  • Gradient Descent - Implementation for Linear Regression
  • Gradient Descent - Importance of Learning Rate
  • Gradient Descent - Common terminology & Hyperparameters
  • Gradient Descent - Types
  • Python Implementation of end-to-end ML Model Training
  • Common Issues during Training & Methods to tackle - 1
  • Common Issues during Training & Methods to tackle - 2
  • Bias-Variance Tradeoff
  • Data Augmentation, Cross-Validation & Regularization
  • Early Stopping Method & Implementation
Advanced Module
  • L1 & L2 Regularization Methods
  • Implementation showing the effects of Regularization
  • Properties A Loss Function Should Have
  • Standard Loss functions for Classification
  • Standard Loss functions for Regression
  • Python Implementation of Loss Functions
  • Evaluation of Trained Machine Learning Model
  • Evaluation Metrics for Regression Tasks
  • Classification Metrics - Accuracy, Confusion Matrix
  • Precision, Recall, F1-score & others
Expert Module
  • Python Implementation of Evaluation Metrics
  • Exploratory Data Analysis (EDA)
  • Feature Engineering - Intro & Significance
  • EDA and Feature Engineering in Python - Part 1
  • EDA and Feature Engineering in Python - Part 2
  • EDA and Feature Engineering in Python - Part 3
  • Curse of Dimensionality
  • Dimensionality Reduction & PCA Intro
  • Principal Component Analysis - Foundations
  • Principal Component Analysis - Calculation with Example
  • PCA demo on MNIST dataset
  • K-Nearest Neighbors Algorithm
  • KNN Implementation from scratch
Natural Language Processing Using Python Syllabus:
Beginner Module
  • What is NLP?
  • Bag of words, Tokenization and Stopwords
  • Stemming & Lemmatization
  • N gram and Smoothing Techniques
  • POS Tagging
Intermediate Module
  • NER Recognition
  • Details on TF-IDF
  • How to build TF-IDF from scratch?
  • Regular Expressions
  • Examples with Regex
  • Word2vec
  • GloVe Embedding
  • Text Feature Extraction
  • Using Scikit Learn for Classification
Advanced Module
  • Overview
  • LDA on Text Document
  • Non-Negative Matrix Factorization & Evaluation Metrics
  • Explaining RNN & LSTM
  • CNN
  • Keras Classification
  • Sentiment Analysis Theory
Expert Module
  • Probability Based Language Models
  • Language Model for Text Generation & Applications
  • Conditional Text Generation & Evaluation Metrics
  • Vector Space Models
  • Evaluation Metrics and Applications of ie
  • Project Guided Session - Sentiment Analysis
  • Project Guided Session - Topic Modeling
  • Project Guided Session - Spam Detection
AI Fundamentals Syllabus:
Beginner Module
  • Course Introduction
  • Descriptive Statistics
  • Levels of Data
  • Measures of Variability
  • Quartiles
  • Outlier
  • Probabilities
Intermediate Module
  • Introduction to AI
  • Domains of AI
  • AI Life cycle
Advanced Module
  • Introduction to ML
  • Supervised learning
  • Unsupervised learning
  • Evaluation of algorithms
Expert Module
  • Introduction to NLP
  • Steps of NLP
  • TF-Idf
  • Introduction to Computer Vision
  • Introduction to ANN
  • Introduction to CNN
  • CNN Operations
Deep Learning Fundamentals Syllabus:
Beginner Module
  • Intro to Deep Learning
  • History of Deep Learning
  • Types of NN, Applications
  • Environment Setup, colab & Kaggle Intro
Intermediate Module
  • Intro and Perceptron Model
  • Coding Perceptron - Python, Numpy, Tensorflow
  • Multi-Layer Perceptrons - Coding a single layer
  • Coding a Multi-layer Perceptron, Learnable Parameters
  • Intuition on Neural Networks Working
  • Neural Networks Demo using playground
  • Activation Functions - Need & Properties
  • Activation Functions - Types & Implementation
Advanced Module
  • Training Process of Neural Network
  • Loss Functions - Classification & Regression
  • Gradient Descent Algorithm
  • Backpropagation for Single Neuron & Layer
  • Backpropagation for Multi-layer Network
  • Common Concepts in Training Process
  • Optimizers Evolution - SGD to Adam
Expert Module
  • Project on Regression using Tensorflow
  • Project on Image Classification using Tensorflow
Deep Learning with TensorFlow Syllabus:
Beginner Module
  • Why Tensorflow?
  • Tensorflow - Tensor Basics 1
  • Tensorflow - Tensor Basics 2
  • Tensorflow - GPUs, Numpy Compatibility
  • Tensorflow - Data Pipelines 1
  • Tensorflow - Data Pipelines 2
Intermediate Module
  • Neural Networks Demo using playground
  • Model building using Keras Sequential API
  • Model building using Functional API & Subclassing
  • NN Model Lifecycle with Tensorflow
  • Project on Regression using Tensorflow
  • Tensorboard Visualization Tool
Advanced Module
  • Common Concepts in Training Process
  • Project on Image Classification using Tensorflow
  • GradientTape & tf.function()
  • Custom Training Loop Implementation
  • Issues while Training Neural Networks
  • Demonstration of Underfitting & Overfitting
Expert Module
  • Vanishing Gradient Problem
  • Regularization & Dropout
  • Batch Normalization
  • Early Stopping in Training
  • Transfer Learning & Finetuning in Neural Networks

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Virtual AI Assistant Powered by GPT3 API & Python Syllabus:
Beginner Module
  • Development Environment Setup Windows
  • Development Environment Setup MAC
  • Text To Speech Basics
  • Text to Speech CMD
  • Time Function
  • Date Function
Intermediate Module
  • Greeting Function
  • Wishme Function
  • TakeCommandCMD Function
  • TakeCommandMIC Function
  • Send Email - 1
  • Send Email - 2
Advanced Module
  • Search on Wikipedia
  • Search on Google
  • Search on YouTube
  • Weather Updates Function
  • News Updates Function
  • Wake Word Detection (NLP)
Expert Module
  • What is GPT-3 API
  • GPT 3 API Access
  • GPT 3 API Pricing
  • How to use GPT-3 API With Python Code
  • Adding 300+ Voices
  • GPT3 Powered AGI Voice Assistant
  • GPT - 3 Integration With New Voices
Vertex AI Syllabus:
Beginner Module
  • Course Introduction
  • How to create datasets using different types of data?
  • Types of Models - Tabular Data
  • Types of Models - Video Data
  • Building and training models in Vertex AI
  • Introduction to AutoML
  • Hands on - Training Model
  • Building and annotating models
  • Annotating a Dataset
  • Labelling Data
Intermediate Module
  • Introduction to Feature Store
  • Feature Store - Data Retention
  • Featurestore
  • Introduction to MLOps and Vertex Pipelines
  • MLops Steps - 1
  • MLops Steps - 2
  • MLops Levels
Advanced Module
  • Hyperparameter Tuning on Vertex AI
  • Examples
  • Tuning the hyperparameters
  • How to build object detection model and annotate the model?
  • Creation of Dataset
  • Training the model
  • Evaluating the model
  • How to create Workbench, write and run the code?
  • Types of Notebooks- Managed Notebooks
  • Types of Notebooks- User-managed Notebooks
  • How to use Cloud Storage in Vertex AI?
  • Types of Cloud Storage
  • Securing the data
  • Buckets
Expert Module
  • Using workbench accessing the Cloud Storage
  • Creation of Notebook - Part 1
  • Creation of Notebook - Part 2
  • Creation of Buckets
  • How to deploy the model?
  • Image Classification Model
  • Other Models
  • How to build image classification model in Vertex AI?
  • Evaluating a model
  • Predictions
Generative AI Syllabus:
Beginner Module
  • Introduction to Generative AI
  • Course Introduction
  • Generative AI Applications
  • Gen AI in Natural Language Processing
  • Transformer Architecture in Detail
  • Gen AI Model Architectures
Intermediate Module
  • Generative AI Model Training Process
  • OpenAI Models and APIs
  • Prompt Engineering
  • Building Application with OpenAI API
Advanced Module
  • Building Application with OSS Models
  • Finetuning an LLM and drawbacks
  • RAG Pipeline in Detail
  • Components of RAG
Expert Module
  • Project: Building a Medical Chatbot - Intro
  • Project: Documents preprocessing and Chunking
  • Project: Creating Embeddings and Vector Store
  • Project: End to End Medical Chatbot
LangChain Syllabus:
Beginner Module
  • Introduction
  • What is LangChain?
  • Why use LangChain?
  • Features of LangChain
  • Development Environment Setup Windows
  • Development Environment Setup Mac
  • Creating and Running a Workflow
  • Components of LangChain
  • Chatbot
  • Website Summarization App
  • Milestone Project : ChatToPDF
Scikit-Learn for Machine Learning Part-1 Syllabus:
Beginner Module
  • Introduction to scikit-learn for machine learning
  • Preprocessing & Pipelines (Advanced): Preprocessing Data
  • Preprocessing & Pipelines (Advanced): Preprocessing Data - Practical 1
  • Preprocessing & Pipelines (Advanced): Preprocessing Data - Practical 2
Intermediate Module
  • Preprocessing & Pipelines (Advanced): Handling Missing Data
  • Preprocessing & Pipelines (Advanced): Handling Missing Data - Practical
  • Classification: Supervised learning workflow
  • Classification: Binary Classification(Log Reg)
Advanced Module
  • Classification: k-Nearest Neighbors - 1
  • Practical(Classification: k-Nearest Neighbors) - 2
  • Classification : DT and Ensemble Learnings
  • Classification : DT and Ensemble Learnings - Practical
Expert Module
  • Hyperparameter Tuning
  • Hyperparameter Tuning - Practical
  • Conclusion
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