AI Kickstart Bundle: Beginner's Course for Creating ChatGPT-Like Chatbots and Mastering Machine Learning
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Achieve exceptional growth with top AI skills!

Master AI Bundle: Beginner to Advanced

Gain the AI skills to build your own ChatGPT. Master machine learning, deep learning, and Python for AI with our four exclusive global certification courses and learn to train high-tech artificial intelligence models like a pro in no time!

Enroll Now & Get Flat 80% OFF!

₹9999 ₹1999/-

Achieve exceptional growth with top AI skills!

Master AI Bundle: Beginner to Advanced

Gain the AI skills to build your own ChatGPT. Master machine learning, deep learning, and Python for AI with our four exclusive global certification courses and learn to train high-tech artificial intelligence models like a pro in no time!

Enroll Now & Get Flat 80% OFF!

₹9999 ₹1999/-

Achieve exceptional growth with top AI skills!

Master AI Bundle: Beginner to Advanced

Gain the AI skills to build your own ChatGPT. Master machine learning, deep learning, and Python for AI with our four exclusive global certification courses and learn to train high-tech artificial intelligence models like a pro in no time!

Enroll Now & Get Flat 80% OFF!

₹9999 ₹1999/-

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Individual certifications

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One time Purchase

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Why Artificial intelligence is Your Best Bet at a Successful Career?

Capture one of the ripest job markets in this dry placement season by acing trending ai skills through artificial intelligence Bundle!

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A foundational understanding of Web-development Ecosystem 

Clearly explained videos by vetted web developers. 

Learn how to develop fast, responsive & interactive websites

Use of trending technologies like - Flask, Redis & Machine Learning. 

Effectively implement machine learning concepts in web development.

How to write functional yet seamless JavaScript Code.

Build fully working web applications using simple steps.

Start Your Journey to Artificial Intelligence Success

Upskill and Invest in a Glowing Artificial intelligence Career

Master and gain 11 certifications @ ₹1999 only

Transform into an advanced artificial intelligence professional from a beginner. Learn from a curated selection of self-paced, upskilling courses with detailed modules offering expert guidance.

Python Zero to Hero
AI Fundamentals
Introduction to Machine Learning
Practical Machine Learning
Deep learning fundamentals
Keras for Beginners
Vertex AI
Deep learning using Pytorch
ChatGPT for programmers
Natural Language Processing with Python
Deep learning fundamentals with Tensorflow
Python Zero to Hero
AI Fundamentals
Introduction to Machine Learning
Practical Machine Learning
Deep learning fundamentals
Deep learning using Pytorch
Deep learning fundamentals with Tensorflow
Natural Language Processing with Python
ChatGPT for programmers
Vertex AI
Keras for Beginners 
Machine Learning 
Python Zero to Hero
Deep Learning using Pytorch

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  • Certificates are issued by an edtech company GUVI.
  • Certificates are globally recognized & they upgrade your programming profile.
  • Certificates are generated after the completion of course.
  • Certificates are sharable on your LinkedIn profile
  • Certificates are issued by an edtech company GUVI.
  • Certificates are globally recognized & they upgrade your programming profile.
  • Certificates are generated after the completion of course.
  • Certificates are sharable on your LinkedIn profile

From Fundamentals to Expertise

After this Course, You’ll be Able to

Understand the key computational concepts of Python/w Machine Learning.

Run various ML algorithms for supervised-unsupervised learning. 

You will discover the definition of AI, its applications, use cases and understand terms like machine learning, neural network, CNN, RNN, deep learning & more. 

Basic foundational knowledge of Python: Work with the list, tuples, Python pip, lambada, PythonSQL, JSON & more. 

Installation of Anaconda & Jupyter Notebook IDE, you will learn how to use Keras .

Clearly understand videos by vetted web developers

Build fully working web applications using simple steps.

Clearly explained videos by vetted web developers. 

How Does Artificial intelligence Combo Course Work?

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Access and learn courses in 'My Courses' section

Complete individual courses and gain certifications

Unlock your AI Combo Pack after payment

Sign Up/Log in to your GUVI account

Complete individual courses and gain certifications

Access and learn courses in 'My Courses' section

Why Choose GUVI?

We are most trusted vernacular ed-tech company delivering world-class learning experiences by providing highly effective & finest learning solutions, breaking the language barrier in tech learning for more than 2 million learners worldwide.

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What Our Learners say about Us?

"You will be lucky to find quality courses on 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. GUVI provides a wide variety of courses both technical and non-technical. I suggest GUVI is the best choice."

"What makes 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."

"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 GUVI!"

Get Unlimited Access to Exclusive Practice Platforms

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A tool-kit specifically developed to boost the coding skills and makes you ever-ready to crack interviews.

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GUVI IDE is an Integrated Development Environment that lets you write, edit, run, test & debug your code. 

Frequently Asked Questions

What is the refund policy?

Customer satisfaction is our first priority. If you are not satisfied with the course, send a mail to [email protected] with the reason for refund and your feedback on the course, within 7 days of purchasing the course. Your refund will be processed immediately. 

Will I gain access to any sort of Forum support?

Yes. You will gain complete access to our forum support to connect with our fellow aspiring users. 

Apart from these courses, will I get access to any practice platforms?

You will gain access to CodeKata which is a gamified practice platform which hosts 1000+ curated coding problems and IDE, which is an Integrated Development Environment that lets you write, edit, run, test & debug your code. 

On what basis are the certificates rolled out?

The certificates are rolled out as and when you complete a course. 

Is it 100% online learning or should I come in person for any specific course?

It is a 100% online learning course package and there won’t be any necessity for you to be present in person. 

Should I be well versed in coding to learn from this AI combo course?

No, anyone can learn various components of AI and its functionalities through GUVI’s artificial intelligence combo pack. Besides, the bundle is completely beginner-friendly and can be learned from scratch by anyone. A little coding knowledge would be helpful, but it’s not a necessity.

When can I start learning? Or what is the duration of this course?

All 11 courses in the Beginner's AI bundle offer 100% online, self-paced learning. This means you can start and finish each course at your own convenience based on your timeline, schedule, and availability. Once purchased, you get lifetime access and unlimited learning whenever and wherever you want it.

Is there an order for learning the courses in this AI combo pack?

Yes. You can follow the learning roadmap given above and learn accordingly to ensure a seamless, structured, and organized learning experience. 

How much time would I need to learn AI completely?

AI is a vast domain that involves a lot of sections and specializations. With GUVI’s AI bundle, you can begin your AI learning journey and finish it at your own pace, ensuring better and more targeted learning. One can easily finish the entire bundle within a month. However, it would take good practice in each module over months to build a solid foundation in AI. 

How will the AI bundle help me secure a career?

A lot of college students and freshers are struggling with a lukewarm placement season and a lack of better skills. Taking up GUVI’s AI bundle will help you gain 11 global skill certifications from an IIT-M and IIM-A incubated company, which will add well to your resume and help you in your job search.

Master AI Bundle for Beginners

Buy 11 courses @ ₹1999 only 

With 7 days refund policy*

With 7 days refund policy*

Python Zero to Hero 
Beginner Module
  • Introduction to Python & Features of Python
  • PVM, Frozen Binaries & Memory management
  • Execution & Viewing the Byte Code
  • Installing & Testing Python for Windows
  • Setting the Path & Executing First Python Program
  • Comments in Python & Docstrings
  • Datatypes & Built-in datatypes
  • Bool Datatype, Sequences in Python & Sets
  • Literals in Python & Determining the Datatype of a Variable
  • Variables Rules & Conventions
  • User-defined Datatypes & Constants
  • Control Statements
  • The if Statement
  • A Word on Indentation & The if … elif … else Statement
  • The while Loop
  • The for Loop, The else Suite, The break Statement, The continue Statement
  • Infinite & Nested Loops, The pass, The assert and The return statement
  • Defining & Calling a Function
  • Returning Results, Multiple values from a Function & Pass by object reference
  • Formal, Actual & Positional Arguments
  • Keyword & Default Arguments
  • Variable Length Arguments & Recursive function
Intermediate Module
  • Anonymous Functions or Lambdas
  • Generators & Decorators
  • Structured Programming & modules
  • Input, Filter and Map
  • Strings - Creating & Length of a String
  • Indexing in Strings
  • Slicing & Reverse Strings
  • Case Modification
  • Membership, Replacement & Sub Strings
  • Split, Join, Find, Index
  • Concatenation of Strings
  • Lists- Create
  • Lists - Slicing and Updating
  • Lists - Add and Remove Items
  • List Remaining Methods
  • Membership & Nested Lists
  • List Comprehensions
  • Tuples Creating and Modifications
  • Basic Operations on Tuples
  • Sets
  • Operations on Dictionaries
  • Dictionary Methods
  • Using for loops, Sorting Elements of Dictionary using Lambdas
Advanced Module
  • OOPS Concepts & Features
  • Constructor, Variables & Namespace
  • Method types, Passing member & Inner Classes
  • Constructors in Inheritance
  • Overriding Constructors, Inheritance, Super()
  • MRO, Polymorphism, Overloading, Overriding, Duck Typing
  • Errors and Exception, Handling, etc
Expert Module
  • Files in Python, Pickle and With Statement
  • Random Accessing & Zipping
  • Regular Expressions
  • Quantifiers in Regular Expressions
  • Special Characters & Sequences
  • Single Threading, Uses of Thread & Concurrent Programming
  • Thread Class Methods & Multitasking using Multiple Threads
  • Thread Synchronization(Lock and Semaphore)
  • Deadlocks and Avoiding Deadlock
  • Thread Communications using wait() & notify() and Daemon Threads
Deep Learning Fundamentals
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
Keras for Beginners Syllabus:
Beginner Module
  • Welcome to Keras for Beginners course
  • Course Walk Through
  • Getting Started with Colab 1 - First Taste of Colab
  • Getting started with Colab 2 - More about Colab
  • Getting Started with Colab 3 - Little beyond the basics of Colab
  • Introduction to Keras 1
  • Introduction to Keras 2
  • Introduction to Keras 3
  • Introduction to Keras 4
  • Introduction to Keras 5
Intermediate Module
  • Fully Connected Networks - 0 - Project Overview
  • Fully Connected Network - 1 - Preprocessing the Data
  • Fully Connected Network - 2 - Creating the Model
  • Fully Connected Network - 3 - Training the model
  • Fully Connected Network - 4 - Saving the Model
  • Fully Connected Network - 5 - Testing and Evalution
  • Fully Connected Network - 6 - Improving the Model Performance
  • OPTIONAL SUGGESTED STUDENT PROJECT 1 - Fully Connected Network
  • Convolutional Neural Networks - 0 - Project Overview
  • APPENDIX 1 - Basics of Convolutional Neural Networks
  • Convolutional Neural Network - 1 - Data Preprocessing
  • Convolutional Neural Network - 2A - Building the Model - Conv Layers
  • Convolutional Neural Network - 2B - Building the Model - Dense Layers
  • Convolutional Neural Network - 3A - Training the model
  • Convolutional Neural Network - 3B - Improving the Network Performance
  • Convolutional Neural Network - 3C - Improving the Network Performance
  • NLP - 0 - Project Overview
  • NLP - 1A - Text Data Processing - Built-in Dataset
  • NLP - 1B - Raw Data Processing
  • NLP - 1C - Raw Data Splitting
  • NLP - 2A - Tokenize Text Data
  • NLP - 2B - Padding
  • NLP - 3A - GloVe Word Embeddings
  • NLP - 3B - Embeddings Matrix
  • NLP - 4 - Fully Connected Network for Text Analysis
  • NLP - 5 - CNNs for Text data
  • NLP - 6 - RNNs for Text Data
  • NLP - 7 - LSTMs for Text Data
  • OPTIONAL STUDENT PROJECT EXERCISES NLP
Advanced Module
  • Transfer Learning - 0 - Project Overview
  • Transfer Learning - 1 - Project Overview - Introduction to Transfer Learning
  • Transter Learning - 2 - Project Overview - Introduction to Kaggle Datasets
  • Transfer Learning - 3A - Importing Kaggle Dataset
  • Transfer Learning - 3B - Data Preprocessing
  • Transfer Learning - 4 - Base Model
  • Transfer Learning - 5 - Keras Functional API
  • Transfer Learning - 6 - Classification Layers
  • Transfer Learning - 7 - Training with fit_generator
  • Course Wrapup - Beyond The Basics
Introduction to Machine Learning:
Beginner Module
  • About Machine Learning Course
  • Installation of Anaconda
  • What is Machine Learning
  • Types of Machine Learning, Supervised Learning and Regression
  • Types of ML,Logistic Regression and Unsupervised Learning
Intermediate Module
  • SVM -What is SVM and How do they work
  • SVM-Loading and Examining our dataset
  • SVM-Building and Tweaking our SVM Classification mode
Advanced Module
  • What is Decision Tree?
  • Building the Decision Tree : Decision Tree Learning
  • Building a Decision Tree - Information Gain a Gini Impurity
  • Decision Tree Lab:Building our First Decision Tree
  • Decision Tree Lab:Viewing and Tweaking our Decision Tree
Expert Module
  • What is Overfitting
  • Random Forest Lab
  • Teamwork
  • Avoiding Overfitted Models
AI Fundamentals
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
Pratical Machine Learning:
Beginner Module
  • 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
Vertex AI
Beginner Module
  • 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
  • Feature Store
  • Introduction to MLOps and Vertex Pipelines
  • MLops Steps - 1
  • MLops Steps - 2
  • MLops Levels
Deep learning using Pytorch:
Beginner Module
  • Introduction to Google Colab and Pytorch
  • Getting started with Pytorch
  • Pytorch vs. NumPy
  • Creating matrices using Tensors
  • Applying Tensor Operations and Functions
  • Indexing, Slicing and Reshaping Tensors
  • Machine Learning vs. Artificial Intelligence vs. Deep Learning
  • Steps in Training a Deep Learning Algorithm
  • Applications of Deep Learning
  • Pytorch Implementation using Forward Propagation
Intermediate Module
  • Practical Application of Deep Learning in predicting Loan Default
  • Backward Propagation in Pytorch
  • Constructing Fully Connected Neural Networks
  • Practice Example: Fully Connected Neural Network
  • Activation Functions
  • Applying Activation Functions using Pytorch
  • Training Neural Networks
  • Loss Functions
  • Applying CE and Softmax function using Pytorch
  • Preparing Datasets in Pytorch
Advanced Module
  • Datasets and Dataloaders in Pytorch
  • Training Neural Network using CIFAR 10 dataset
  • Convolutional Neural Networks (CNN)
  • Convolutions in Pytorch
  • Pooling in CNN
  • Building CNN
  • Constructing CNN using Sequential Module
  • Overfitting in Neural Network
  • Techniques to counter overfitting
  • Final Thoughts
ChatGPT for programmers:
Beginner Module
  • Introduction to ChatGPT
  • How to get access to chatGPT
  • How to get access to chatGPT Plus & GPT - 4 Model
  • ChatGPT Basics
  • Introduction to HTML with ChatGPT
  • Explanation of fine-tuning and why it's important
  • Step-by-step guide to fine-tuning ChatGPT
  • Tips for improving response quality
  • ChatGPT Intern for Programming
Intermediate Module
  • What is GPT-3
  • Vscode and Python Setup
  • GPT 3 API Access
  • GPT 3 API Pricing
  • Explanation of chatbots and their applications
  • Step-by-step guide to creating a chatbot with ChatGPT
  • Tips for designing an effective chatbot
  • Custom Model Fine-tune
  • Access fine-tune model
Advanced Module
  • Working with large datasets and models in ChatGPT
  • Integrating ChatGPT with Other Technologies and Platforms
  • Overview of integrating ChatGPT with web applications and APIs
  • Building voice-enabled chatbots with ChatGPT - part 1
  • Building voice-enabled chatbots with ChatGPT part 2
  • Deploying ChatGPT models to production environments
Expert Module
  • Monetizing ChatGPT
  • Milestone Project Part - 1
  • Milestone Project Part - 2
Natural Language Processing with Python:
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
  • 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
  • Sentiment analysis
  • Topic modeling
  • Spam detection
Deep learning fundamentals with Tensorflow:
Beginner Module
  • Why Tensorflow?
  • Tensorflow - Tensor Basics
  • 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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