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Achieve exceptional growth with top AI skills!

Master AI Bundle: Beginner to Advanced

Enroll Now & Get Flat 62% OFF!

₹9999 ₹2999/-

Achieve exceptional growth with top AI skills!

Master AI Bundle: Beginner to Advanced

Enroll Now & Get Flat 62% OFF!

₹9999 ₹2999/-

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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.

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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.

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How Does Artificial intelligence Combo Course Work?

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Complete individual courses and gain certifications

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Complete individual courses and gain certifications

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What Our Learners say about Us?

Midhun Devasia

"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."

Arjun Govindan

"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."

Pranav Mahadeokar

"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!"

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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 with the subject line "AI combo pack – Refund” , 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.

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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

Achieve exceptional growth with top AI skills!

Master AI Bundle: Beginner to Advanced

Achieve exceptional growth with top AI skills!

Master AI Bundle: Beginner to Advanced

Achieve exceptional growth with top AI skills!

Master AI Bundle: Beginner to Advanced

Start Your Journey to become AI Engineer!

Unmatched Benefits for You

Unmatched Benefits for You

Our Learners Work at Top Companies

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

Why Choose GUVI?

2M+

Total Learners

190+

Total Mentors

10M+

Lines of code submission

1600+

Videos

4.4

Accredited by

Accredited by

Start Your Journey to Artificial intelligence Success

Upskill and Invest in a Glowing Artificial intelligence Career

Get all 11 courses @ ₹2999 only

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

Not Believing us? Look What Our Learners Got to Say!

Learn from Content Curated by Top Industry experts from

Enrich Your CV with Global Certifications

From Fundamentals to Expertise

After this Course, You’ll be Able to

Achieve Your Career Goals with This Course

How Does Artificial intelligence Combo Course Work?

Unlock your AI Combo Pack after payment

Sign Up/Log in to your GUVI account

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

What Our Learners say about Us?

Get Unlimited Access to Exclusive Practice Platforms

CodeKata

WebKata

Debugging

IDE

Frequently Asked Questions

What is the refund policy?

Will I gain access to any sort of Forum support?

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

On what basis are the certificates rolled out?

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

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

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