MIT Introduction to Deep Learning 6.S191: Lecture 1
*New 2024 Edition*
Foundations of Deep Learning
Lecturer: Alexander Amini

For all lectures, slides, and lab materials: http://introtodeeplearning.com/

Lecture Outline
0:00​ - Introduction
7:25​ - Course information
13:37​ - Why deep learning?
17:20​ - The perceptron
24:30​ - Perceptron example
31;16​ - From perceptrons to neural networks
37:51​ - Applying neural networks
41:12​ - Loss functions
44:22​ - Training and gradient descent
49:52​ - Backpropagation
54:57​ - Setting the learning rate
58:54​ - Batched gradient descent
1:02:28​ - Regularization: dropout and early stopping
1:08:47 - Summary

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MIT 14.01 Principles of Microeconomics, Fall 2023
Instructor: Prof. Jonathan Gruber
View the complete course: https://ocw.mit.edu/14-01F23
YouTube Playlist: https://www.youtube.com/playli....st?list=PLUl4u3cNGP6

Prof. Gruber introduces the class by explaining microeconomics as the study of individuals and firms who make themselves as well off as possible in a world full of scarcity. He then explains the core concepts of supply and demand. Keywords: microeconomics, scarcity, constrained optimization, supply and demand

License: Creative Commons BY-NC-SA
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( Data Science Training - https://www.edureka.co/data-sc....ience-r-programming- )

This Machine Learning Algorithms Tutorial shall teach you what machine learning is, and the various ways in which you can use machine learning to solve a problem! Towards the end, you will learn how to prepare a data-set for model creation and validation and how you can create a model using any machine learning algorithm!

You can also check out this Machine Learning with Python video for more insights: https://youtu.be/nJKxWbQ1jaw

Check our complete Data Science playlist here: https://goo.gl/60NJJS

Subscribe to our channel to get video updates. Hit the subscribe button above.

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How it Works?

1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project
2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. You will get Lifetime Access to the recordings in the LMS.
4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate!

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About the Course

Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities.

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Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework.

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5. Learn tools and techniques for data transformation
6. Understand Data Mining techniques and their implementation
7. Analyse data using machine learning algorithms in R
8. Work with Hadoop Mappers and Reducers to analyze data
9. Implement various Machine Learning Algorithms in Apache Mahout
10. Gain insight into data visualization and optimization techniques
11. Explore the parallel processing feature in R

- - - - - - - - - - - - - -

Who should go for this course?

The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course:

1. Developers aspiring to be a 'Data Scientist'
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4. Business Analysts who want to understand Machine Learning (ML) Techniques
5. Information Architects who want to gain expertise in Predictive Analytics
6. 'R' professionals who want to captivate and analyze Big Data
7. Hadoop Professionals who want to learn R and ML techniques
8. Analysts wanting to understand Data Science methodologies

For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free).

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Customer Reviews:

Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "

🔥 Edureka Big Data Courses: https://www.edureka.co/big-data-and-analytics
This Edureka video gives you a brief introduction to Big Data and how it is revolutionizing in various domains. In this quick guide, the following topics will be covered:

1. What Is Big Data?
2. Popular Use case of Big Data
3. Career Prospects in Big Data

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Become an expert in the exciting new world of Big Data, get trained in cutting edge technologies, and work on real-life industry-grade projects.

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Why BIG Data?

The adoption of Big Data is growing across industries, which has resulted in an increased demand for Big Data Engineers. However, the supply is inadequate, leading to a large number of job opportunities. In this environment, professionals with appropriate skills can command higher salaries.

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3. Social Media: Sentiment Analysis

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Mentors & Instructors

Ashok Kumar Turk
Professor | NITR

Bibhudatta Sahoo
Associate Professor | NITR

Korra Sathya Babu
Assistant Professor Grade-I | NITR

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Is this program for me?

If you’re passionate about Big Data Engineering and want to pursue a career in this field, this program is for you. Whether you’re a fresher or a professional, this program is designed to equip you with the skills you need to rise to the top for a career in Big Data Engineering.


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A potential candidate must have a Bachelor’s Degree preferably in BCA, MCA, and B.Tech.


Will I get any certificate at the end of the course?

Yes, you will receive a Post-Graduate industry-recognized certificate from NIT Rourkela upon the successful completion of the course.


For more information, Please write back to us at [email protected] or call us at IND: +91-9606058418 / US: 18338555775 (toll-free).

In this video, we will build a Vision Language Model (VLM) from scratch, showing how a multimodal model combines computer vision and natural language processing for vision QA. GitHub below ↓

Want to support the channel? Hit that like button and subscribe!

GitHub Link of the Code
https://github.com/uygarkurt/VLM-PyTorch

Dataset
https://huggingface.co/dataset....s/uygarkurt/simple-i

Implement and Train ViT From Scratch for Image Recognition - PyTorch
https://youtu.be/Vonyoz6Yt9c

Implement Llama 3 From Scratch - PyTorch
https://youtu.be/lrWY4O5kUTY

Fine-Tune Visual Language Models (VLMs) - HuggingFace, PyTorch, LoRA, Quantization, TRL
https://youtu.be/3ypHZayanBI

ViT (Vision Transformer) - An Image Is Worth 16x16 Words (Paper Explained)
https://youtu.be/8phM16htKbU

What should I implement next? Let me know in the comments!

00:00 VLM Explanation
04:42 Downloading Dataset
06:39 Imports
10:09 Hyperparameters
12:37 Data Processing
16:03 ViT Definition
19:05 VLM Implementation
41:40 Sample Inference
45:20 Data Loaders
52:46 Training Loop
57:50 Inference

References
https://github.com/AviSoori1x/seemore

Buy me a coffee! ☕️
https://ko-fi.com/uygarkurt

Watch live to learn about how the deep learning frameworks in MATLAB and Simulink can be used with TensorFlow and PyTorch to provide enhanced capabilities for building and training your Machine Learning model.

Heather Gorr, PhD and Yann Debray will show you can take full advantage of the MATLAB ecosystem and integrate it with resources developed by the open-source community. You can combine workflows that include data-centric preprocessing, model tuning, model compression, model integration, and automatic code generation with models developed outside of MATLAB.

Explore the options and benefits, along with examples, of the various interoperability pathways available, including:
- Importing and exporting models from TensorFlow, PyTorch, and ONNX into and from MATLAB
- Coexecuting MATLAB alongside installations of TensorFlow and PyTorch

This is the biggest, brightest, hottest video there is about the science of fireworks. This video is brought to you by Kiwico – go to https://kiwico.com/veritasium for your first month free!

If you're looking for a molecular modeling kit, try Snatoms - a kit I invented where the atoms snap together magnetically: https://snatoms.com

Check out Gene’s channel here -- @PotatoJet

Massive thanks to Mike Tockstein from Pyrotechnic Innovations @PyroInnovations
and Will Scott from Las Vegas Display Fireworks Inc, for all your pyro knowledge and keeping us safe.

▀▀▀
Massive thanks to Gene Nagata from PotatoJet for filming this episode – check out his wonderful channel for more videos about cameras and FPV drones.

Thanks to Brandon Williams for helping with the chemistry and sourcing of materials.

Thanks to Matthew Tosh for the help with the chemistry conversation about fireworks.

Thanks to Simon Werrett for the help with the history of fireworks.

▀▀▀
Werrett, S. (2010). Fireworks: pyrotechnic arts and sciences in European history. University of Chicago Press

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Special thanks to our Patreon supporters:
Emil Abu Milad, Tj Steyn, meg noah, Bernard McGee, KeyWestr, Amadeo Bee, TTST, Balkrishna Heroor, John H. Austin, Jr., john kiehl, Anton Ragin, Diffbot, Gnare, Dave Kircher, Burt Humburg, Blake Byers, Evgeny Skvortsov, Meekay, Bill Linder, Paul Peijzel, Josh Hibschman, Mac Malkawi, Juan Benet, Ubiquity Ventures, Richard Sundvall, Lee Redden, Stephen Wilcox, Marinus Kuivenhoven, Michael Krugman, Sam Lutfi.

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Written by Derek Muller
Edited by Trenton Oliver
Animated by Ivy Tello and Fabio Albertelli
Filmed by Derek Muller, Hunter Peterson, Gene Nagata, Raquel Nuno
Production by Hunter Peterson and Stephanie Castillo
Additional video/photos supplied by Mike Tockstein/Pyrotechnic Innovations
Music from Epidemic Sound & Jonny Hyman
Produced by Derek Muller, Petr Lebedev, Emily Zhang, & Casper Mebius

This video shows how you can detect if a sequence is periodic or not, and also find its period, using the Discrete Fourier Transform.

This is the second in a series of videos about Fourier transforms.

- First video: The Discrete Fourier Transform https://www.youtube.com/watch?v=T8XZxR5H04E
- Second video: Detecting Periodicity with the DFT (this one)

Grokking Machine Learning Book:
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00:00 Introduction
2:37: Periodic sequences
12:54 In the inverse DFT

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Can a drone deliver packages safely and efficiently? 🤖 Martin Keen breaks down the 5 types of AI agents—from reflex to learning models—and their role in robotics, decision-making, and automation. Learn how goal-driven and utility-based AI adapt to workflows and complex environments.

Intro - 0:00
Simple Reflex Agent - 0:50
Model-Based Reflex Agent - 2:49
Goal-Based AI Agent - 4:20
Utility Based AI Agent- 5:43
Learning AI Agent - 6:55
Use Cases - 8:22

AI news moves fast. Sign up for a monthly newsletter for AI updates from IBM → https://ibm.biz/BdnZTX

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Learn the fundamentals of generative artificial intelligence in this beginner-friendly course (2025 edition). We’ll cover what generative AI is, how it differs from traditional AI, key architectures (GANs, VAEs, Transformers), hands-on examples, prompt engineering, and real-world applications you can build today. No prior AI or deep-learning experience required.

✅ What you’ll learn:

What is generative AI & why it matters in 2026
Core models: GAN (Generative Adversarial Network), VAE (Variational Autoencoder)
Transformer architecture and large language models (LLMs)
Text-, image-, and audio-generation use cases
Basics of prompt-engineering and building your own simple GenAI app
Ethical, bias, and safety considerations for generative systems

📌 By the end of this course you’ll be equipped to explore your own generative-AI experiments and skill up for deeper tools.

🔔 If you enjoyed this video, Subscribe & hit the bell icon so you won’t miss further AI-tutorials in 2026. Leave a comment if you have questions or want to see a project walkthrough.

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📝Feel free to share your comments below.📝

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Please write back to us at [email protected] or call us at IND: 9606058406 / US: +18885487823 (toll-free) for more information.

🔥Edureka AWS Architect Certification Training: https://www.edureka.co/aws-certification-training
This video is about the features and benefits of AWS Kinesis. It shows how AWS Kinesis can be effectively used for processing the streaming data. The topics that we will cover in this session are as follows:
· What is AWS Kinesis?
· Advantages
· Capabilities
· Use Cases
· Kinesis vs SQS
· How it works?
· Demo

Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV
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#edureka #EdurekaAWS #AWSKinesis #awstraining #cloudcomputing
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How does it work?
1. This is a 5 Week Instructor-led Online Course.
2. The course consists of 30 hours of online classes, 30 hours of assignment, 20 hours of project
3. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
4. You will get Lifetime Access to the recordings in the LMS.
5. At the end of the training, you will have to complete the project based on which we will provide you a Verifiable Certificate!
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About the Course

AWS Architect Certification Training from Edureka is designed to provide in-depth knowledge about Amazon AWS architectural principles and its components. The sessions will be conducted by Industry practitioners who will train you to leverage AWS services to make the AWS cloud infrastructure scalable, reliable, and highly available. This course is completely aligned to AWS Architect Certification - Associate Level exam conducted by Amazon Web Services.

During this AWS Architect Online training, you'll learn:
1. AWS Architecture and different models of Cloud Computing
2. Compute Services: Amazon EC2, Auto Scaling, and Load Balancing, AWS Lambda, Elastic Beanstalk
3. Amazon Storage Services: EBS, S3 AWS, Glacier, CloudFront, Snowball, Storage Gateway
4. Database Services: RDS, DynamoDB, ElastiCache, RedShift
5. Security and Identity Services: IAM, KMS
6. Networking Services: Amazon VPC, Route 53, Direct Connect
7. Management Tools: CloudTrail, CloudWatch, CloudFormation, OpsWorks, Trusty Advisor
8. Application Services: SES, SNS, SQS
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Course Objectives

On completion of the AWS Architect Certification Training, the learner will be able to:
1. Design and deploy scalable, highly available, and fault-tolerant systems on AWS
2. Understand the lift and shift of an existing on-premises application to AWS
3. Ingress and egress of data to and from AWS
4. Identifying appropriate use of AWS architectural best practices
5. Estimating AWS costs and identifying cost control mechanisms
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Pre-requisites

There are no specific prerequisites for this course. Any professional who has an understanding of IT Service Management can join this training. There is no programming knowledge needed and no prior AWS experience required.
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If you are looking for live online training, write back to us at [email protected] or call us at the US: + 18338555775 (Toll-Free) or India: +91 9606058406 for more information.

Introduction to the world of LLM (Large Language Models) in April 2023. With detailed explanation of GPT-3.5, GPT-4, T5, Flan-T5 to LLama, Alpaca and KOALA LLM, plus dataset sources and configurations.
Including ICL (in-context learning), adapter fine-tuning, PEFT LoRA and classical fine-tuning of LLM explained. When to choose what type of data set for what LLM job?

Addendum: Beautiful, new open-source "DOLLY 2.0" LLM was not published at time of recording, therefore a special link to my video explaining DOLLY 2:
https://youtu.be/kZazs6V3314

A comprehensive LLM /AI ecosystem is essential for the creation and implementation of sophisticated AI applications. It facilitates the efficient processing of large-scale data, the development of complex machine learning models, and the deployment of intelligent systems capable of performing complex tasks.

As the field of AI continues to evolve and expand, the importance of a well-integrated and cohesive AI ecosystem cannot be overstated.

A complete overview of today's LLM and how you can train them for your needs.

#naturallanguageprocessing
#LargeLanguageModels
#chatgpttutorial
#finetuning
#finetune
#ai
#introduction
#overview
#chatgpt

In 2018 the kg will be defined by Planck's constant, not a hunk of metal.
Try a free book from Audible for 30 days http://ve42.co/audible
Special thanks to the staff at NIST who made this possible: Darine Haddad, Jon Pratt, Stephan Schlamminger, and Ben Stein.

Additional footage and animations by Sean Kelley, Jennifer Lauren Lee, and Frank Seifert.

I have been obsessed with measurement for a long time and I'm not sure quite how it happened. The world's roundest object played a role in this. I guess I'm just fascinated by how difficult it is to pin down a quantity like a kilogram. A physical object seemed like a good idea until the mass of the international prototype kilogram wasn't as constant as expected. These methods of the Kibble balance and silicon sphere have shown better precision than 20 parts per billion, making them superior to the old method. The agreement between Avogadro approaches

Special thanks to Patreon Supporters:
Tony Fadell, Donal Botkin, Jeff Straathof, Zach Mueller, Ron Neal, Nathan Hansen

Support Veritasium on Patreon: http://ve42.co/patreon

Interferometer video by TSG Physics: https://www.youtube.com/watch?v=j-u3IEgcTiQ

Music from http://epidemicsound.com "ExperiMental1" by Gunnar Johnsén

Studio filming by Raquel Nuno

Learn how to fine tuning LLM models. This course will teach you fine tuning using using QLORA and LORA, as well as Quantization using LLama2, Gradient and the Google Gemma model. This crash course includes both theoretical and practical instruction to help you understand how to perform fine tuning.

💻 Code: https://github.com/krishnaik06/Finetuning-LLM

✏️ Course developed by @krishnaik06

⌨️ (0:00:00) Introduction
⌨️ (0:01:39) Quantization Intuition
⌨️ (0:34:03) Lora And QLORA Indepth Intuition
⌨️ (0:56:26) Finetuning With LLama2
⌨️ (1:20:35) 1 bit LLM Indepth Intuition
⌨️ (1:37:33) Finetuning with Google Gemma Models
⌨️ (1:59:45) Building LLm Pipelines With No code
⌨️ (2:20:33) Fine tuning With Own Custom Data

🎉 Thanks to our Champion and Sponsor supporters:
👾 davthecoder
👾 jedi-or-sith
👾 南宮千影
👾 Agustín Kussrow
👾 Nattira Maneerat
👾 Heather Wcislo
👾 Serhiy Kalinets
👾 Justin Hual
👾 Otis Morgan
👾 Oscar Rahnama

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Learn to code for free and get a developer job: https://www.freecodecamp.org

Read hundreds of articles on programming: https://freecodecamp.org/news

🔥Enroll for Intellipaat's Executive Certification Program in AI & ML: https://intellipaat.com/epgc-a....i-and-machine-learni

#MachineLearningCourse #MachineLearningFullCourse #MachineLearningTutorial #MachineLearningTutorialForBeginners #MLCourse #MachineLearningWithPython #MachineLearning #MachineLearningProjects #Intellipaat

Unlock the world of Machine Learning with this comprehensive Machine Learning Full Course 2025 by Intellipaat! This 11-hour complete machine learning course is your ultimate guide to mastering the basics and advanced concepts of machine learning. Designed for beginners and enthusiasts, this course covers key topics like machine learning algorithms, hands-on Python implementations, and real-world projects. Whether you’re starting your ML journey or brushing up your skills, this ML course has everything to accelerate your career in the tech industry.

💡 What you’ll learn in this course:
👨‍💻 Machine Learning fundamentals explained step-by-step.
👨‍💻 In-depth coverage of algorithms like regression, classification, clustering, and more.
👨‍💻 Python implementation of machine learning techniques.
👨‍💻 Insights into ML project development and deployment.
👨‍💻 Real-world examples to make concepts practical and applicable.

💻 Why choose this course?
Machine Learning is one of the most in-demand skills of the decade, and having a strong foundation in it can open doors to countless opportunities. This course not only helps you understand ML concepts but also equips you with hands-on experience to stand out in a competitive job market.

📖 Below are the concepts covered in this 'Machine Learning Full Course 2025':
00:00:00 - Introduction to Machine Learning Course
00:02:39 - What is Machine Learning?
00:53:55 - Types of ML: Supervised and Unsupervised Learning
01:11:11 - ML Examples and Myths
02:42:07 - Introduction to Reinforcement Learning
03:25:35 - Linear Regression: Introduction and Examples
03:52:00 - Linear Regression: Errors and Finding the Best Line (Hyperbole/Intercept)
04:14:08 - Linear Regression Hands-On: Single and Multiple Linear Regression
05:12:42 - R-Squared Explained
05:23:25 - Assumptions of Linear Regression
05:34:29 - Logistic Regression: Introduction
06:22:51 - Understanding Odds
06:28:36 - Probability vs. Odds
06:34:24 - Derivation of Sigmoid Function
07:19:03 - Balanced vs. Imbalanced Data
07:26:13 - Confusion Matrix
07:42:51 - Precision Explained
08:00:43 - Hands-On Logistic Regression
08:54:41 - Naive Bayes Explained
09:26:59 - Decision Tree Algorithm
09:55:51 - Understanding Entropy
10:29:14 - Types of Nodes in Decision Trees
10:36:00 - Underfitting vs. Overfitting

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Q1. Is this Machine Learning course suitable for beginners?
Yes, this course is perfect for beginners, with a strong focus on foundational concepts, step-by-step explanations, and practical applications.

Q2. What tools will I learn in this ML course?
You’ll primarily work with Python and its libraries like NumPy, Pandas, matplotlib, and scikit-learn, which are essential for machine learning projects.

Q3. How can I benefit from this Machine Learning course?
This course helps you develop skills in data analysis, algorithm implementation, and problem-solving, giving you a competitive edge in the tech industry.

➡️ About the Course
Gain expertise in Artificial intelligence and Machine Learning through an Executive Post Graduate Certification program in AI and ML offered by iHUB DivyaSampark, a Technology Innovation Hub of IIT Roorkee, in collaboration with Intellipaat This AI and ML program is in collaboration with tech giants Microsoft. Get classes and guidance directly from IIT Faculty and industry experts, with personalized 1:1 mentorship. Become IIT certified AI and ML expert with this online BootCamp.

➡️Who should take this course?
☑️ Individuals with a bachelor’s degree and a keen interest in learning AI and ML
☑️ IT professionals looking for a career transition as Machin learning experts or AI engineers.
☑️ Professionals aiming to move ahead in their IT career
☑️ Developers and Project Managers
☑️ Freshers who aspire to build their career in the field of Artificial Intelligence and Machine

✅ Key Features - (Course Features)
👉🏼 620 Hrs of Applied Learning
👉🏼 Learn from IIT Faculty & Industry Practitioners
👉🏼 50+ Industry Projects & Case Studies
👉🏼 One-on-One with Industry Mentors
👉🏼 Placement Assistance
👉🏼 Resume Preparation and LinkedIn Profile Review
👉🏼 24*7 Support
👉🏼 1:1 Mock Interview
👉🏼 Certification from Microsoft
👉🏼 Up to Rs. 50 Lakhs startup Incubation Support*

📌 Do subscribe to Intellipaat channel & come across more relevant Tech content: https://goo.gl/hhsGWb

▶️ Intellipaat Achievers Channel: https://www.youtube.com/@intellipaatachievers

📚For more information, please write back to us at [email protected] or call us at IND: +91-7022374614 / US : 1-800-216-8930