https://github.com/krishnaik06..../Google-Gemini-Crash
In this video we will learn about we can create gen ai Large Language models and Large Image models app using Google Gemini pro and Vision.

Timestamp:
00:00:00 Introduction
00:01:22 Google Gemini Pro And Vision Demo
00:28:48 End To End LLM and LIM App
00:54:06 Conversational Q&A APP
01:08:30 Chat With Multiple Pdf
01:37:39 MultiLanguage Invoice extractor
02:03:26 Health MAnagement APP Using Gemini Pro
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Support me by joining membership so that I can upload these kind of videos
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►Data Science Projects:
https://www.youtube.com/watch?v=S_F_c9e2bz4&list=PLZoTAELRMXVPS-dOaVbAux22vzqdgoGhG&pp=iAQB

►Learn In One Tutorials

Statistics in 6 hours: https://www.youtube.com/watch?v=LZzq1zSL1bs&t=9522s&pp=ygUVa3Jpc2ggbmFpayBzdGF0aXN0aWNz

Machine Learning In 6 Hours: https://www.youtube.com/watch?v=JxgmHe2NyeY&t=4733s&pp=ygUba3Jpc2ggbmFpayBtYWNoaW5lIGxlYXJuaW5n

Deep Learning 5 hours : https://www.youtube.com/watch?v=d2kxUVwWWwU&t=1210s&pp=ygUYa3Jpc2ggbmFpayBkZWVwIGxlYXJuaW5n

►Learn In a Week Playlist

Statistics:https://www.youtube.com/watch?v=11unm2hmvOQ&list=PLZoTAELRMXVMgtxAboeAx-D9qbnY94Yay

Machine Learning : https://www.youtube.com/watch?v=z8sxaUw_f-M&list=PLZoTAELRMXVPjaAzURB77Kz0YXxj65tYz

Deep Learning:https://www.youtube.com/watch?v=8arGWdq_KL0&list=PLZoTAELRMXVPiyueAqA_eQnsycC_DSBns

NLP : https://www.youtube.com/watch?v=w3coRFpyddQ&list=PLZoTAELRMXVNNrHSKv36Lr3_156yCo6Nn
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How does AI learn? Is AI conscious & sentient? Can AI break encryption? How does GPT & image generation work? What's a neural network?
#ai #agi #qstar #singularity #gpt #imagegeneration #stablediffusion #humanoid #neuralnetworks #deeplearning

I used this to create neural nets:
https://alexlenail.me/NN-SVG/index.html

More info on neural networks
https://youtu.be/aircAruvnKk?si=Go9XAXR8TqmhX-5m

How stable diffusion works
https://youtu.be/sFztPP9qPRc?si=aF-doepEiaiDrG6z

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AI isn’t just replacing jobs — it’s creating AI workforces that replace entire departments with digital teams that run 24/7 without salaries, burnout, or hiring bottlenecks. In this video, you’ll learn how to build AI agents and multi-agent workforces that automate real business processes like booking meetings, preparing presentations, transcribing calls, and assigning tasks, using the Relevance AI platform and powered by integrations with Gamma.app, HubSpot, Google Meet, Trello, and more. I’ll show you an end-to-end build of a personal AI assistant workforce that researches participants, designs presentation decks, manages scheduling, and sends follow-up summaries — all while you sleep. If you want to stay ahead in the $1 trillion AI automation market, monetize automation skills, and become the person companies trust to guide their AI transformation, this is your roadmap to surviving and thriving in the new era of autonomous digital employees.

⏱️ Timestamps:
00:00 - What We’re Covering
01:58 - Chapter 1: Foundations
05:28 - What Is an AI Workforce?
07:00 - How AI Workforces Actually Work
10:53 - Foundations Recap
11:47 - Chapter 2: Building
11:55 - Relevance AI Orientation
14:55 - What We’re Building
18:08 - Meeting Booker Agent
27:48 - Participant Finder Agent
31:07 - Orchestrator Agent
39:39 - Gamma Agent
47:39 - Lead Locator Agent
52:42 - Note Taker Agent
56:54 - Full AI Workforce Demo
59:14 - Publishing to Relevance Marketplace
1:01:12 - Chapter 3: Monetization (Selling workforces)

Proud partners with Relevance.ai and Gamma.app

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai

October 21, 2025
This lecture covers deep reinforcement learning.

To learn more about enrolling in this course, visit: https://online.stanford.edu/co....urses/cs230-deep-lea

To follow along with the course schedule and syllabus, visit: https://cs230.stanford.edu/syllabus/

More lectures will be published regularly.
View the playlist: https://www.youtube.com/playli....st?list=PLoROMvodv4r

Andrew Ng
Founder of DeepLearning.AI
Adjunct Professor, Stanford University’s Computer Science Department

Kian Katanforoosh
CEO and Founder of Workera
Adjunct Lecturer, Stanford University’s Computer Science Department

🔥 Advanced Certificate Program In Data Science: https://www.simplilearn.com/pgp-data-science-certification-bootcamp-program?utm_campaign=AIvsMLvsDLvsDataScience-vNc2z2u_nh0&utm_medium=DescriptionFF&utm_source=youtube
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In this video, we will cover AI vs. ML vs. DL vs. Data Science in detail. From this video, you will be able to understand the difference between AI, ML, DL, and Data Science.
0:00 Deep Learning
2:31 Machine Learning
3:49 Artificial Intelligence
5:26 Deep Learning

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About Data Science with Python Certification Course:
Ranked #1 Data Science program by Economic Times
The Data Science with Python certification course provides a complete overview of Python's Data Analytics tools and techniques. Learning Python is a crucial skill for many Data Science roles. Acquiring knowledge in Python will be the key to unlocking your career as a Data Scientist.

Data Science with Python Course Overview:
The Python Data Science course teaches you to master the concepts of Python programming. Through this Data Science with Python certification training, you will learn Data Analysis, Machine Learning, Data Visualization, Web Scraping, & NLP. Upon course completion, you will master the essential tools of Data Science with Python.

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Benefits
Data Science is an evolving field, and Python has become a required skill for 46 percent of jobs in Data Science. The demand for Data Science professionals will grow an estimated 1581 percent by 2020 and professionals with Python skills will have an additional advantage.

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The demand for Data Science with Python programming professionals has surged, making this course well-suited for participants at all levels of experience. This Data Science with Python course is beneficial for analytics professionals willing to work with Python, Software, and IT professionals interested in the field of analytics, and anyone with a genuine interest in Data Science.

Pre-requisites for this Data Science with Python course:
To best understand the Python Data Science course, it is recommended that you begin with the courses including, Introduction to Data Science in Python, Math Refresher, Data Science in Real Life, and Statistics Essentials for Data Science. These courses are offered as free companions with this training.

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🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - +18445327688

Learn how to use Pandas and Python for Data Analysis, to Data Cleaning and Data Wrangling. You will learn by creating real life projects interactively to help you take the next step in your Data Science Career.

Learn more about the projects at https://www.datawars.io/freecodecamp

⚠️ Important! We encourage you to try to resolve the projects by yourself first! And watch the solution afterwards ⚠️

💻 Course created by Santiago Basulto from DataWars.
🔗 Find more interactive Data Science projects solve at: https://www.datawars.io

⭐️ Projects Covered ⭐️
⌨️ (0:00:00) Introduction

⌨️ (0:02:22) DataFrames practice: working with English Words [🟢 Easy]
This project focuses on the basics of pandas DataFrames, including understanding its structures and modifying them. The data we're using is a big dictionary of english words.
🔗 Solve the project by yourself: https://www.datawars.io/fcc-english-words

⌨️ (0:33:26) Filtering and sorting with Pokemon data [🟢 Easy]
This project focuses on the main tasks of Data Analysis: filtering and sorting and question/answering. The dataset includes information of pokemons from all generations (to make it more fun!)
🔗 Solve the project by yourself: https://www.datawars.io/fcc-filtering-pokemon

⌨️ (1:24:30) The Birthday Paradox in the NBA [🟡 Intermediate]
The Birthday Paradox answers the question: how many people do you need in the same room in order to have a probability of at least 50% that two people share a birthday. The answer is astonishing! You'll use your findings to find which teams in the NBA share player's birthdays.
🔗 Solve the project by yourself: https://www.datawars.io/fcc-birthday-nba

⌨️ (1:55:28) Matching Strings by Similarity using Levenshtein distance [🟡 Intermediate]
One of the most challenging tasks of Data Cleaning is dealing with Strings. This project is all about string handling and advanced techniques, as using the Combinatorics and the Levenshtein distance to find irregularities in company names.
🔗 Solve the project by yourself: https://www.datawars.io/fcc-string-similarity

⌨️ (2:24:15) Data Cleaning with Google Playstore dataset [🟡 Intermediate]
An all-encompassing project that covers all the aspects of Data Cleaning, including: finding and fixing null values, duplicate values, outliers and more. The data was scraped from the Google Playstore which means that is full of irregularities. The project finishes with some Data Analysis tasks!
🔗 Solve the project by yourself: https://www.datawars.io/fcc-data-cleaning-playstore

⌨️ (3:18:34) Premier League Match Analysis [🔴 Advanced]
This project increases the complexity of your Data Analysis skills, as it combines Data Cleaning, with some analysis based on grouping operations. The dataset comes from the Premier League, the top-division Football/Soccer league in England.
🔗 Solve the project by yourself: https://www.datawars.io/fcc-premier-league

⌨️ (3:53:46) NBA 2017 season analysis: joining and groupby practice [🔴 Advanced]
This project puts your Data Wrangling skills to a test, by asking you to merge different dataframes, clean them, and finish doing some analysis and question/answering. The dataset contains the full information of 2017 NBA statistics.
🔗 Solve the project by yourself: https://www.datawars.io/fcc-nba-analysis


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🔥Enroll for the UI / UX Design Course: https://intellipaat.com/ui-ux-design-course/

#UIUXDesignFullCourse #UIUXCourse #UIUXTraining #Intellipaat

This UI UX Design Full Course covers all the important skill sets required to get you a UI UX job!

We will begin this UI UX Course with an Introduction to UI UX Design. Then we will get introduced to Figma, how to do Brainstorming and Empathization in Figma, as well as how to use predefined templates and features from Figjam.

Next, we will cover How to use inspiration from the internet to create design, how to recognize UI patterns, Atomic Design principles, and much more about UI and UX technicalities as highlighted on the screen. Finally, we will help you understand how you can create UI Components in Figma as well as some of the important Figma PlugIns and their Use Cases.

This UI UX Course suits those who aspire to become UI UX Designers

🔵 Following topics are covered in this UI UX Design Full Course:
00:00:00 - Introduction to UI UX Course
00:03:15 - Introduction to Figma Products
00:37:46 - What is Affinity Map And How to Create?
00:49:46 - Collaborative Power
00:50:50 - Brainstorming & Empathization in Figma
01:09:23 - Using Predefined Templates and Features in FigJam
01:49:47 - Drawing Inspiration from the Internet
02:10:56 - Atomic Design Principle
02:28:38 - Principles of Good UI Design
02:47:20 - UI Design Styles
03:00:52 - What is a Wireframe?
03:07:30 - Jakob's Law
03:25:37 - Aesthetic Usability Effect
03:33:10 - Hick's Law
03:50:50 - Goal - Gradient Effect
03:57:45 - Miller's Law
04:08:26 - Serial Position Effect
04:11:25 - Von Restroff Effect
04:15:35 - Zeigarnik Effect
04:22:52 - Law of Common Region
04:24:58 - Gestalt Law of UX Designing
04:38:14 - UI Designing Principles
05:02:00 - Drawing Inspiration from the Internet
05:09:09 - Hands-on Session
07:30:03 - Components in Figma
07:56:42 - Figma Plugins & It’s Use Cases
10:31:46 - UI UX Designing Interview Questions And Answers

✅ What is a UI UX course?
The UI/UX Design Specialization brings a design-centric approach to user interface and user experience design and offers practical, skill-based instruction centered around a visual communications perspective, rather than one focused on marketing or programming alone.

✅What is the UI UX design course for?
The UI/UX Design Specialization brings a design-centric approach to user interface and user experience design and offers practical, skill-based instruction centered around a visual communications perspective, rather than one focused on marketing or programming alone.

➡ About the Course
Become a UI/UX professional by building a strong UX portfolio with our UI UX Course in just 6 months!

UI UX Design Course by iHUB, IIT Roorkee (An Innovation Hub of IIT Roorkee)
Learn UI/UX from eminent IIT Faculty & Industry Experts
Gain Generative AI skills in the UI UX Industry
2 Days Campus Immersion at IIT Roorkee

➡Who should take this course?
Web designers
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Anyone aspiring to pursue UX UI Design

✅ Key Features - (Course Features)
👉🏼50+ Live sessions across 6 months
👉🏼250 Hrs of Applied Learning
👉🏼30+ Industry Projects & Case Studies
👉🏼Learn from IIT Faculty & Industry Practitioners
👉🏼Top 2 performers per batch will receive INR 80,000 in fellowship*
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➡ What’s Covered in This Program? -
What’s Covered in This Program? -
✅ PART - 1 Introduction to UI/UX Design, design process, and user research
✅ PART - 2 Fundamentals of UI and UX design and how to Apply them
✅ PART - 3 Polishing your UI/UX Design and Learning about the UX Industry
✅ PART - 4 Getting Industry Ready

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In this beginner-friendly crash course, I’ll show you how to use real-world data with Python to create something cool from scratch.

Timeline:

00:00 - Getting Started
0:54 - What is Machine Learning
3:19 - Machine Learning Pipeline
7:18 - Popular ML Libraries & Tools
12:42 - Importing Data
20:17 - Jupyter Shortcuts
25:16 - A Real Machine Learning Problem
29:13 - Preparing the Data
32:34 - Learning & Predicting
37:28 - Calculating Model Accuracy
44:10 - Persisting Models
48:32 - Visualizing Decision Trees

Playlist Link: https://www.youtube.com/watch?v=rE9XJGakAUM&list=PLNIQLFWpQMRVKC_zohdgl3pjDk4rlN3jc

🚩 Subscribe ➜ https://bit.ly/45IwoxJ

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Was this video on Python using Machine Learning helpful? Let me know your thoughts in the comments.

#python #artificialintelligence #machinelearning

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Learn advanced features, shortcuts, and workflows to get the most from Claude Code

For more information about Stanford's online Artificial Intelligence programs visit: https://stanford.io/ai

This lecture covers:
1. The course (10 mins)
2. Human language and word meaning (15 mins)
3. Word2vec introduction (15 mins)
4. Word2vec objective function gradients (25 mins)
5. Optimization basics (5 mins)
6. Looking at word vectors (10 mins or less)

Key learning: The (astounding!) result that word meaning can be represented rather
well by a (high-dimensional) vector of real numbers

To learn more about enrolling in this course visit: https://online.stanford.edu/co....urses/cs224n-natural

To follow along with the course schedule and syllabus visit: hhttps://web.stanford.edu/class..../archive/cs/cs224n/c

Professor Christopher Manning
Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science
Director, Stanford Artificial Intelligence Laboratory (SAIL)

In this video, we'll go through data preprocessing steps for 3 different datasets. We'll also go in depth on a dimensionality reduction technique called Principal Component Analysis.

Coding challenge for this video:
https://github.com/llSourcell/....How_to_Make_Data_Ama

Charles-David's Winning Code:
https://github.com/alkaya/earthquake-cotw

Siby Jack Grove's Runner-up code:
https://github.com/sibyjackgro....ve/Earthquake_predic

Please subscribe. And like. And comment. That's what keeps me going.

More Learning Resources:
http://www.cs.ccsu.edu/~markov..../ccsu_courses/datami
http://www.slideshare.net/jaso....nrodrigues/data-prep
http://iasri.res.in/ebook/win_....school_aa/notes/Data
http://staffwww.itn.liu.se/~ai....dvi/courses/06/dm/le
http://ufldl.stanford.edu/wiki..../index.php/Data_Prep
http://machinelearningmastery.....com/how-to-prepare-d
https://plot.ly/ipython-notebo....oks/principal-compon

Public datasets:
https://github.com/caesar0301/....awesome-public-datas
https://aws.amazon.com/public-datasets/
http://archive.ics.uci.edu/ml/index.html
https://dreamtolearn.com/ryan/1001_datasets

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Deep learning currently provides state-of-the-art performance in computer vision, natural language processing, and many other machine learning tasks. In this talk, we will learn when deep learning is useful (and when it isn't!), how to implement some simple neural networks in Python using Theano, and how to build more powerful systems using the OpenDeep package.

Our first model will be the 'hello world' of deep learning - the multilayer perceptron. This model generalizes logistic regression as your typical feed-forward neural net for classification.

Our second model will be an introduction to unsupervised learning with neural nets - the denoising auto-encoder. This model attempts to reconstruct corrupted inputs, learning a useful representation of your input data distribution that can deal with missing values.

Finally, we will explore the modularity of neural nets by implementing an image-captioning system using the the OpenDeep package.

Markus Beissinger
Recent graduate from the Jerome Fisher Program in Management and Technology dual degree program at the University of Pennsylvania (The Wharton School and the School of Engineering and Applied Science), and current Master's student in computer science. Focus on machine learning, startups, and management.

Slides: http://goo.gl/P9QGnV

In this video I show you examples of how to perform transfer learning in various ways, either having trained a model yourself, using keras.applications or the TensorFlow hub. I also show how you can then pick out specific chunks of the pretrained models and remove specific layers, and then how you could freeze those layers, add layers on top and perform fine tuning.

Download TensorFlow Hub (w. pip it's pip install tensorflow-hub):
https://anaconda.org/conda-forge/tensorflow-hub

I learned a lot and was inspired to make these TensorFlow videos by the TensorFlow Specialization on Coursera. Below you'll find both affiliate and non-affiliate links, the pricing for you is the same but a small commission goes back to the channel if you buy it through the affiliate link.
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🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐒𝐞𝐥𝐞𝐧𝐢𝐮𝐦 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠: https://www.edureka.co/seleniu....m-certification-trai code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎")
This video by Edureka helps you understand how to handle multiple windows when you are testing an application using Selenium. Following are the topics covered in this video:
00:00:00 Introduction
00:01:22 What is Selenium Webdriver?
00:03:29 Basic pre-requisites to run the test cases
00:04:05 Handling multiple windows in Selenium
00:05:29 Demo

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⏩ NEW Top 10 Technologies To Learn In 2023 - https://youtu.be/udD_GQVDt5g

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Learn how to build a Mobile App with HTML, CSS, JavaScript and Google Firebase. Go here to try the interactive browser-version: https://scrimba.com/learn/firebase

✏️ This course was created by Rafid Hoda, a teacher at Scrimba. You can follow Rafid on Twitter here: https://twitter.com/rafidhoda

We recommend that you learn basic HTML, CSS, and JavaScript before taking this course.

💫 Links mentioned:

🔗 EpochConverter: https://www.epochconverter.com/
🔗 Meta viewport explained by Kevin Powell: https://www.youtube.com/watch?v=SElOdK2qTpI

🛠 Tools used:
🔗 Firebase: https://firebase.google.com/
🔗 Favicon: https://favicon.io/
🔗 Netlify: https://www.netlify.com/
🔗 Figma: https://www.figma.com/
🔗 Google Fonts: https://fonts.google.com/

⭐️ Code ⭐️
🔗 Download via Scrimba: https://scrimba.com/learn/firebase

🐱 Want to get access to the cute cat images used in this course?
→ Search for "Peach and Goma cat" on Google Image.

⭐️ Contents ⭐️
⌨️ (0:00:00) Introduction
⌨️ (0:01:57) Let's Build a Mobile App with Firebase
⌨️ (0:06:02) Setting up app skeleton
⌨️ (0:09:53) Adding CSS
⌨️ (0:14:16) Aside: Firebase Realtime Database
⌨️ (0:24:21) Adding Firebase to project
⌨️ (0:29:13) Security Rules
⌨️ (0:31:46) innerHTML to append li to ul
⌨️ (0:35:23) Refactoring
⌨️ (0:40:15) Aside: Turning an Object into an Array
⌨️ (0:44:28) Aside: Fetching database items in realtime using onValue
⌨️ (0:54:54) Updating items in realtime
⌨️ (0:57:41) For loop to render database items
⌨️ (1:00:42) Let's smash the bug
⌨️ (1:03:32) Aside: Flexbox flex-wrap
⌨️ (1:04:54) Aside: Flexbox gap
⌨️ (1:06:15) Adding CSS for li to wrap items
⌨️ (1:10:22) Getting ID of item in database
⌨️ (1:14:35) Replacing innerHTML with createElement
⌨️ (1:19:48) Aside: Removing items from Firebase
⌨️ (1:25:09) Removing an item when clicked
⌨️ (1:29:22) Only fetching items from database if snapshot exists
⌨️ (1:33:28) Adding hover styles to buttons
⌨️ (1:35:20) Aside: user-select
⌨️ (1:37:04) Aside: Setting the viewport
⌨️ (1:38:58) Making the app more mobile-friendly
⌨️ (1:40:35) Aside: Favicon
⌨️ (1:42:42) Adding favicon and phone icons
⌨️ (1:43:58) Aside: Web Application Manifest
⌨️ (1:46:39) Turning web app into "mobile" app
⌨️ (1:47:40) Personalise your app
⌨️ (1:49:10) Deploy to Netlify
⌨️ (1:51:48) Add app to home screen
⌨️ (1:53:44) Share your creation
⌨️ (1:55:32) Recap
⌨️ (1:59:16) Outro + Credits

Follow Scrimba on YouTube: https://www.youtube.com/c/Scrimba
Follow Scrimba on Twitter: https://twitter.com/scrimba

🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐏𝐌𝐏 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐄𝐱𝐚𝐦 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 : https://www.edureka.co/pmp-cer....tification-exam-trai 𝐂𝐨𝐝𝐞: 𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎)
This Edureka tutorial on Project Quality Management will give you an insight into the various process and activities covered during Project development in order to maintain and manage the quality of a project.
00:00:00 - Introduction
00:01:47 - Project Quality Management
00:05:42 - Quality Management Overview
00:12:40 - Quality Management Processes

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🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬

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🌕 Post Graduate Program in DevOps with Purdue University: https://bit.ly/3Ov52lT

🔵 Advanced Certificate Program in Data Science with E&ICT Academy, IIT Guwahati: http://bit.ly/3V7ffrh


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

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Unlock the potential of ChatGPT in your data analysis journey! 📊

In this video, we delve into how ChatGPT, a powerful language model, can be a game-changer for interpreting and extracting insights from your data. From understanding its capabilities to implementing it in real-world scenarios, we've got you covered. Whether you're a seasoned data analyst or just getting started, join us to explore the fusion of AI and data analytics. Subscribe for in-depth tutorials and stay ahead in the world of data science!

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➡️ About Post Graduate Program In Data Analytics
This Data Analytics Program is ideal for all working professionals and prior programming knowledge is not required. It covers topics like data analysis, data visualization, regression techniques, and supervised learning in-depth via our applied learning model with live sessions by leading practitioners and industry projects.

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