🔥 Data Analyst Master's Program (Discount Code: YTBE15): https://www.simplilearn.com/data-analyst-masters-certification-training-course?utm_campaign=SQLForDataAnalytics0122-YvaddgkneEg&utm_medium=DescriptionFF&utm_source=youtube
🔥 Professional Certificate Program In Data Analytics: https://www.simplilearn.com/pgp-data-analytics-certification-training-course?utm_campaign=SQLForDataAnalytics0122-YvaddgkneEg&utm_medium=DescriptionFF&utm_source=youtube

This video on "SQL for Data Analytics 2023" by Simplilearn will help you understand the various skills needed for data analytics in SQL, and you will know how SQL is used for Data Analytics. This SQL tutorial will also help you master all the basic and advanced-level concepts of SQL programming, along with the demos for the same.
Below are the topics we have covered in this SQL For Data Analysis 2023 video.
00:00:00 Introduction to SQL
00:03:32 What is SQL Tutorial?
00:15.33 What is DBMS?
00:32:30 Installation of MySQL on PC
00:40:10 SQL Syntax, Data types, Commands
01:00:32 What are SQL Operators?
01:23:15 What are SQL Expressions?
01:34:02 How to create a database in MySQL?
01:40:44 What is SQL Table Tutorial?
01:57:39 SQL Select Statement tutorial
02:19:41 What is SQL Clause?
02:44:29 SQL Insert Statement tutorial
02:53:48 SQL Update Statement tutorial
03:01:12 SQL Delete Statement tutorial
03:10:21 What is SQL Join?
03:51:37 What are Keys in SQL?
04:04:53 What are Aggregate Functions?
04:21:12 What are Scalar Functions?
04:37:10 SQL Group by and Order by
04:57:08 How to use Where Clause in SQL?
05:01:24 SQL Where vs. Having Clause
05:21:15 What are Subqueries?
05:31:27 How to Create a user-defined Function in SQL?
05:42:51 What are Commit and Rollback in SQL?
05:58:35 SQL Like Clause Tutorial
06:11:52 How to find Nth highest Salary?
06:25:58 WebSQL Tutorial
06:41:00 Cast and Convert Function in SQL
06:57:21 What is View In SQL?
07:17:03 SQL Interview Questions for Beginners

Dataset Link: https://drive.google.com/drive..../folders/1U8CF0Xx3Ni

🔥Free Data Analytics Course: https://www.simplilearn.com/learn-data-analytics-for-beginners-skillup?utm_campaign=SQLForDataAnalytics0122&utm_medium=Description&utm_source=youtube

✅Subscribe to our Channel to learn more about the top Technologies: https://bit.ly/2VT4WtH

⏩ Check out the Business Analytics tutorial videos: https://bit.ly/3hQFfDP

#DataAnalyticsWithSQL #DataAnalyticsInSQLFullCourse #DataAnalytics #DataAnalyticsUsingSQL #DataAnalyticsCourse #DataAnalyticsCourseForBeginners #SQLTutorial #SQLTutorialForBeginners #sql #sqlcourse #BusinessAnalystCourse #BusinessAnalyst #BusinessAnalytics #Simplilearn

What is SQL(Structured Query Language)?
SQL is a domain-specific language used in programming and designed for managing data held in a relational database management system or for stream processing in a relational data stream management system.

✅ About Data Analyst Master's Program

This Data Analytics course in collaboration with IBM makes you a Data Analytics expert. Our Data Analyst course helps you learn analytics tools and techniques, how to work with SQL databases, R and Python, how to create data visualizations and apply statistics and predictive analytics in a business environment. This Data Analytics certification also features Masterclasses from IBM experts.

✅ What are the Data Analyst course learning objectives?

Simplilearn’s Data Analyst course delivered in collaboration with IBM will provide you with extensive expertise in the booming field of data analytics. This Data Analytics training will teach you how to master descriptive and inferential statistics, hypothesis testing, regression analysis, data blending, data extraction, and forecasting. Through this Data Analyst course, you will also gain expertise in data visualization techniques using Tableau and Power BI, and learn how to organize data and design dashboards.

✅ Why become a Data Analyst?

The World Economic Forum predicts that, by 2025, Data Analysts will be one of the hottest jobs on the market. Organizations view data analysis as one of the most crucial skills due to the value that they can help derive from data. With over 2.5 quintillion bytes of data created each day, data is more abundant and accessible than ever in today’s business environment, and with the ever-increasing skill gap in data analytics today, the value of Data Analysts is continuing to grow, creating new jobs, and career advancement opportunities.

✅ Tools Covered

- Microsoft Excel
- Tableau
- NumPy
- Pandas
- SciPi
- Power BI
- Python
- R Programming
- MySQL

👉Learn more at: https://www.simplilearn.com/data-analyst-masters-certification-training-course#why-become-a-data-analyst?utm_campaign=SQLForDataAnalytics0122&utm_medium=Description&utm_source=youtube

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

𝐄𝐝𝐮𝐫𝐞𝐤𝐚'𝐬 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐌𝐚𝐬𝐭𝐞𝐫𝐬 𝐩𝐫𝐨𝐠𝐫𝐚𝐦: https://www.edureka.co/masters....-program/data-scient
🔥Data Science with Python Certification Course: https://www.edureka.co/data-sc....ience-python-certifi
🔥Integrated MS+PGP Program in Data Science & AI:https://www.edureka.co/dual-ce....rtification-programs
This Edureka Data Science Full Course video will help you understand and learn Data Science Algorithms in detail. This Data Science Tutorial is ideal for both beginners as well as professionals who want to master Data Science Algorithms.
00:00:00 Introduction
00:01:31 What is Data Science?
00:03:26 Who is a Data Scientist?
00:35:36 Top 10 Reasons to Learn Data Science
00:40:12 Data Science Basics
01:33:28 Data Life Cycle
01:37:03 Statistics and Probability
03:09:33 Hypothesis Testing Statistics
04:02:26 What is Machine Learning?
04:19:21 Linear Regression
04:45:59 Logistic Regression
05:34:29 Decision Tree Algorithm
06:19:50 Random Forest
06:47:02 KNN Algorithm
07:19:27 Naive Bayes Classifier
07:39:45 Support Vector Machine
08:05:14 K- Means Clustering Algorithm
08:25:22 Apriori Algorithm Explained
08:55:04 Reinforcement Learning
09:16:30 What is Deep Learning?
09:40:24 Introduction to Keras
10:11:51 Data Science Roadmap
10:21:05 Data Science vs Data Analytics
10:31:05 Data Science for Non Programmers
10:59:00 Data Science Interview Questions

🔴 𝐋𝐞𝐚𝐫𝐧 𝐓𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐅𝐨𝐫 𝐅𝐫𝐞𝐞! 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐘𝐨𝐮𝐓𝐮𝐛𝐞 𝐂𝐡𝐚𝐧𝐧𝐞𝐥: https://edrk.in/DKQQ4Py
📝Feel free to share your comments below.📝

🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬

🔵 DevOps Online Training: http://bit.ly/3VkBRUT
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🔵 DevOps Engineer Masters Program: http://bit.ly/3Oud9PC
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🌕 Full Stack Developer Masters Program : http://bit.ly/3tWCE2S
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🌕 Python Developer Masters Program : https://bit.ly/3EV6kDv
🔵 Azure Cloud Engineer Masters Program: http://bit.ly/3AEBHzH

🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐔𝐧𝐢𝐯𝐞𝐫𝐬𝐢𝐭𝐲 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐬

🔵 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

🔵 Advanced Certificate Program in Cloud Computing with E&ICT Academy, IIT Guwahati: https://bit.ly/43vmME8

🌕Advanced Certificate Program in Cybersecurity with E&ICT Academy, IIT Guwahati: https://bit.ly/3Pd2utG

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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.

ChatGPT has taken the world by storm and GPT4 is out soon. While that’s great, wouldn't you like to run your own chatbot, locally and for free (unlike GPT4)? No more queues, nothing to pay, a choice of models and the ability to create your own characters? What’s that? It doesn’t even need a GPU? Sounds too good to be true? Welcome to the text-generation-webui! A gradio web UI for running Large Language Models like GPT-J 6B, OPT, GALACTICA, LLaMA, Pygmalion and many, many more! Whisper and TTS included, allowing you to speak to your characters and have them speak back too.

As with anything AI, you’ll have the very best experience of a simple, error free install every time on Linux.
For Microsoft Windows the exact same steps should also work, but as any Windows user knows anything could go wrong at any time!
As for Mac - not sure!

Thanks to huggingface there is a massive library of models, many with Open Source licenses just ready for you to use! Create any custom character you like and take it for a spin - even with just 8GB VRAM. Some models work with much less too 😀

If you don’t like standard installations, you can also use the obfuscated method. It’s just 3 steps:
1. Install using the 1-click installer
2. Download your model(s)
3. Start the webui!

See the pinned tweet for more links ;)

I’m pretty sure everyone will want to try this, so expect to see lots more of this interface as others catch on! Just remember where you saw it first - right here on the Nerdy Rodent channel!

== Links! ==
* Installing Anaconda for MS Windows Beginners - https://youtu.be/OjOn0Q_U8cY
* How do I create an animated SD avatar? - https://youtu.be/Z7TLukqckR0
* Stable Diffusion Playlist! - https://youtube.com/playlist?l....ist=PLjC8P1vEncQDVOC

== Stable Diffusion Playlists ==
* Interested in adding things to your AI Art? Try these!
Dreambooth Playlist - https://youtube.com/playlist?l....ist=PLjC8P1vEncQD-QY
* Textual Inversion Playlist - https://youtube.com/playlist?l....ist=PLjC8P1vEncQDSDL

Note that GitHub repositories update often! Be sure to check the GitHub README for the current info ;)m

In this video I show you how to calculate the mean and std across multiple channels of the data you're working with which you will normally then use for normalization to obtain a 0 mean and standard deviation (std) of 1.


People often ask what courses are great for getting into ML/DL and the two I started with is ML and DL specialization both by Andrew Ng. Below you'll find both affiliate and non-affiliate links if you want to check it out. The pricing for you is the same but a small commission goes back to the channel if you buy it through the affiliate link.
ML Course (affiliate): https://bit.ly/3qq20Sx
DL Specialization (affiliate): https://bit.ly/30npNrw
ML Course (no affiliate): https://bit.ly/3t8JqA9
DL Specialization (no affiliate): https://bit.ly/3t8JqA9

GitHub Repository:
https://github.com/aladdinpers....son/Machine-Learning

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https://www.amazon.com/shop/aladdinpersson

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PyTorch Playlist:
https://www.youtube.com/playli....st?list=PLhhyoLH6Ijf

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

▀▀▀
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.

▀▀▀
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

🔥Edureka PGP in AI & ML: https://www.edureka.co/post-gr....aduate/machine-learn
This Edureka video on What is Text Classification in Machine Learning gives you a brief overview of text classification. In this quick guide, the following topics will be covered:

1) What is Text Classification?
2) Use Case of Text Classification

Python Tutorial Playlist: https://goo.gl/WsBpKe
Blog Series: http://bit.ly/2sqmP4s

#PythonEdureka #Edureka #textclassification #pythontextclassification #pythonprojects #pythonprogramming #pythontutorial #PythonTraining
----------------------------------------------
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Become an expert in the exciting new world of AI & Machine Learning, get trained in cutting edge technologies and work on real-life industry-grade projects.
----------------------------------------------

Why Machine Learning & AI?

Because of the increasing need for intelligent and accurate decision making, there is an exponential growth in the adoption of AI and ML technologies. Hence these are poised to remain the most important technologies in the years to come.

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

PG Program in Machine Learning & AI

1. Ranked 4th among NITs by NIRF
2. Ranked among Top 50 Institutes in India
3. Designated as Institute of National Importance

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

Program Features

1. Mentorship from NITW faculty
2. Placement Assistance
3. Alumni Status
4. Industry Networking

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

Industry Projects

1. Building a Conversational ChatBot
2. Predictive Model for Auto Insurance
3. E-commerce Website - Sales Prediction

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

Mentors & Instructors

Dr. RBV Subramaanyam
Professor NITW

Dr. DVLN Somayajulu
Professor NITW

Dr. P. Radha Krishna
Professor NITW

Dr. V. Ravindranath
Professor JNTU Kakinada

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

Is this program for me?

If you’re passionate about AI & ML 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 in a career in AI & ML.

Is there any eligibility criteria for this program?

A potential candidate must have one of the following prerequisites: Degrees like BCA, MCA, and B.Tech or Programming experience Should have studied PCM in 10+2

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

Yes, you will receive a Post-Graduate industry-recognized certificate from E & ICT Academy, NIT Warangal upon 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).

🔥Edureka Tensorflow Training: https://www.edureka.co/ai-deep....-learning-with-tenso
This Edureka LSTM Explained video will help you in understanding why we need Recurrent Neural Networks (RNN) and what exactly it is. It also explains few issues with training a Recurrent Neural Network and how to overcome those challenges using LSTMs.
00:00 Introduction
00:36 Agenda
00:47 Introduction to NLP
01:46 Ways to Process Text Data
02:57 Recurrent Neural Networks
22:02 Long Short-term Memory
51:46 LSTM Use Cases
53:45 Real Time Applications of LSTM

🔹Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE
🔹Check our complete Deep Learning With TensorFlow Blog Series: http://bit.ly/2sqmP4s

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#Edureka #DeepLearningEdureka #LSTMExplained #NeuralNetworks #DeepLearningTraining #DeepearningTutorial #EdurekaTraining

How it Works?

1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each.
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. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate!

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

About the Course
Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.

Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.


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

Who should go for this course?

The following professionals can go for this course:

1. Developers aspiring to be a 'Data Scientist'

2. Analytics Managers who are leading a team of analysts

3. Business Analysts who want to understand Deep Learning (ML) Techniques

4. Information Architects who want to gain expertise in Predictive Analytics

5. Professionals who want to captivate and analyze Big Data

6. Analysts wanting to understand Data Science methodologies

However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio.

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

Why Learn Deep Learning With TensorFlow?
TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.


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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This is a 1 hour general-audience introduction to Large Language Models: the core technical component behind systems like ChatGPT, Claude, and Bard. What they are, where they are headed, comparisons and analogies to present-day operating systems, and some of the security-related challenges of this new computing paradigm.
As of November 2023 (this field moves fast!).

Context: This video is based on the slides of a talk I gave recently at the AI Security Summit. The talk was not recorded but a lot of people came to me after and told me they liked it. Seeing as I had already put in one long weekend of work to make the slides, I decided to just tune them a bit, record this round 2 of the talk and upload it here on YouTube. Pardon the random background, that's my hotel room during the thanksgiving break.

- Slides as PDF: https://drive.google.com/file/....d/1pxx_ZI7O-Nwl7ZLNk (42MB)
- Slides. as Keynote: https://drive.google.com/file/....d/1FPUpFMiCkMRKPFjhi (140MB)

Few things I wish I said (I'll add items here as they come up):
- The dreams and hallucinations do not get fixed with finetuning. Finetuning just "directs" the dreams into "helpful assistant dreams". Always be careful with what LLMs tell you, especially if they are telling you something from memory alone. That said, similar to a human, if the LLM used browsing or retrieval and the answer made its way into the "working memory" of its context window, you can trust the LLM a bit more to process that information into the final answer. But TLDR right now, do not trust what LLMs say or do. For example, in the tools section, I'd always recommend double-checking the math/code the LLM did.
- How does the LLM use a tool like the browser? It emits special words, e.g. |BROWSER|. When the code "above" that is inferencing the LLM detects these words it captures the output that follows, sends it off to a tool, comes back with the result and continues the generation. How does the LLM know to emit these special words? Finetuning datasets teach it how and when to browse, by example. And/or the instructions for tool use can also be automatically placed in the context window (in the “system message”).
- You might also enjoy my 2015 blog post "Unreasonable Effectiveness of Recurrent Neural Networks". The way we obtain base models today is pretty much identical on a high level, except the RNN is swapped for a Transformer. http://karpathy.github.io/2015..../05/21/rnn-effective
- What is in the run.c file? A bit more full-featured 1000-line version hre: https://github.com/karpathy/ll....ama2.c/blob/master/r

Chapters:
Part 1: LLMs
00:00:00 Intro: Large Language Model (LLM) talk
00:00:20 LLM Inference
00:04:17 LLM Training
00:08:58 LLM dreams
00:11:22 How do they work?
00:14:14 Finetuning into an Assistant
00:17:52 Summary so far
00:21:05 Appendix: Comparisons, Labeling docs, RLHF, Synthetic data, Leaderboard
Part 2: Future of LLMs
00:25:43 LLM Scaling Laws
00:27:43 Tool Use (Browser, Calculator, Interpreter, DALL-E)
00:33:32 Multimodality (Vision, Audio)
00:35:00 Thinking, System 1/2
00:38:02 Self-improvement, LLM AlphaGo
00:40:45 LLM Customization, GPTs store
00:42:15 LLM OS
Part 3: LLM Security
00:45:43 LLM Security Intro
00:46:14 Jailbreaks
00:51:30 Prompt Injection
00:56:23 Data poisoning
00:58:37 LLM Security conclusions
End
00:59:23 Outro

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This Edureka "Statistics & PRobability For Data Science" video will introduce you to the concepts of statistics using Data Science. You will learn about Descriptive and Inferential statistics in detail with a demo. The following topics are covered in this Statistics for Data Science tutorial:
00:00:00 Introduction
00:01:11 Probability
00:02:13 Probability - Terminologies
00:03:33 Probability - Types of Events
00:04:46 Probability - Distribution
00:05:10 Probability - PDF
00:07:00 Probability - Normal Distribution
00:11:36 Probability - Central Limit Theorem
00:11:40 Probability - Types of Probability
00:12:01 Probability - Marginal Probability
00:12:48 Probability - Joint Probability
00:13:14 Probability - Conditional Probability
00:15:11 Probability - Bayes Theorem
00:16:30 Probability - Applications
00:17:12 Statistics
00:17:40 Statistics - Terminologies
00:19:12 Statistics - Sampling technique
00:21:30 Statistics - Types of Statistics
00:24:26 Statistics - Measures of Center
00:26:02 Statistics - Measures of Spread
00:32:22 Statistics - Hands-on
00:33:26 Statistics - Information Gain
00:38:32 Statistics - Inferential Statistics

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

📌𝐓𝐞𝐥𝐞𝐠𝐫𝐚𝐦: https://t.me/edurekaupdates
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📌𝐌𝐞𝐞𝐭𝐮𝐩: https://www.meetup.com/edureka/
📌𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲: https://www.edureka.co/community/

#statistics #statisticsfordatascience #datascience #edureka

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🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚'𝐬 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 𝐚𝐧𝐝 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬 𝐜𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐚𝐧𝐝 𝐜𝐨𝐮𝐫𝐬𝐞𝐬 (𝐔𝐬𝐞 𝐂𝐨𝐝𝐞: 𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎) : https://www.edureka.co/program....ming-and-frameworks-
This Edureka video on Programming For Beginners will help you to Learn Programming basics with examples. This Programming for beginner video covers all the topics for all the three categories of learners - beginner, intermediate and experienced professionals.

Below are the programming languages covered in this Programming Full course video:

00:00:00 Introduction
00:01:30 Agenda
00:02:44 Pyhton Basics
00:04:27 Why use python?
00:25:09 Comparision Operators
00:43:16 range
00:48:26 Counter
00:55:20 Defaultdict
00:58:47 When to use Sets in python
01:01:23 Python set operations
01:12:35 What is an Array ?
01:14:19 How to create arrays in python
01:31:34 Slicing an array
01:34:09 Looping through an Array
01:37:36 Classes and objects in python
01:42:47 Python Classes
02:04:20 Java
02:10:00 Variables in /java
02:31:56 Operator in java
02:46:36 Loops in Java
02:59:32 Object Oriented Programming (OOPS)
03:03:51 OOPS Concept
03:27:13 OOPS Characteristics
03:34:54 Exception in java
03:46:07Exception Handling Methods
03:54:44 History of C
03:55:36 Features of C Language
04:00:46 Keywords
04:03:22 Strings
04:05:05 Identifiers
04:06:49 Rules for Identifier
04:09:46 Operators
04:14:34 Datatypes
04:16:18 Local Variable
04:21:28 PreProcessor Directives
04:27:31 Control Statement
04:45:37 Loops
04:55:58 Pointers
05:00:17 Functions
05:04:36 Rules for using Functions
05:06:19 How to use Functions
05:08:03 Data Structure
05:10:38 One-Dimensional Array
05:14:05 Lists
05:27:53 I+Undirected Graphs
05:31:20 Files
05:35:39 Strings
05:39:58 Structure
05:41:41 Union
05:43:25 Structure v/s Union
05:44:16 Memory allocations
05:48:35 Sorting algorithm
05:53:45 Insertion Algorithm
05:58:56 SelectionAlgorithm
06:01:31 Selection Algorithm
06:04:06 Features of C++ Language
06:04:58 Installation of C++ in windows
06:07:33 Structure of C++ Program
06:10:09 Identifers
06:11:52 Keywords
06:15:19 Arrays
06:22:13 Lists
06:36:01 Functions
06:42:55 Structure
06:43:47 Enumeration
06:47:14 Variable types
06:52:24 Modifiers
06:53:16 Type of Qualifiers
06:54:00 Types of operators
06:56:00 Control Statement
07:06:12 Loops
07:10:31 Features of OOPS
07:12:15 Encapsulation
07:20:00 Interface
07:21:44 Constructor
07:22:36 Destructor
07:28:38 Deallocation
07:30:21 Multithreading
07:33:48 Interview Questions
09:29:23 What is Javascript
09:33:42 Benefits of Javascript
10:18:31 Loops in Javascript
10:39:14 Javascript Array
10:41:50 Javascript Array Methods
11:03:23 Why Learn coding



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🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬

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Check out the latest (and most visual) video on this topic! The Celestial Mechanics of Attention Mechanisms: https://www.youtube.com/watch?v=RFdb2rKAqFw

This is the last of a series of 3 videos where we demystify Transformer models and explain them with visuals and friendly examples.

Video 1: The attention mechanism in high level https://www.youtube.com/watch?v=OxCpWwDCDFQ

Video 2: The attention mechanism with math https://www.youtube.com/watch?v=UPtG_38Oq8o

Video 3 (This one): Transformer models

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00:00 Introduction
01:50 What is a transformer?
04:35 Generating one word at a time
08:59 Sentiment Analysis
13:05 Neural Networks
18:18 Tokenization
19:12 Embeddings
25:06 Positional encoding
27:54 Attention
32:29 Softmax
35:48 Architecture of a Transformer
39:00 Fine-tuning
42:20 Conclusion

Welcome to this comprehensive SQL tutorial course. This course covers the basics of relational databases and SQL, including setting up MySQL, inserting data, and working with aggregation, grouping, and pagination in SQL queries. It also covers advanced topics such as combining tables using joins, executing SQL queries using Python and SQL Alchemy, and solving technical interview questions. By the end of this course, you'll have the knowledge and confidence to excel in SQL.

✏️ Course created by@jovianhq

Important Links
🔗 Relational Databases notebook - https://jovian.com/aakashns/re....lational-databases-a
🔗 Advanced SQL Aggregation & Joins notebook - https://jovian.com/aakashns/ad....vanced-sql-aggregati
🔗 SQL data file: https://raw.githubusercontent.....com/harsha547/Classi

A database is an organized collection of structured information, typically stored in the form of tables (rows & columns). Relational databases allow storing and retrieving different kinds of related information e.g. products, customers, and orders for an online shopping site. Structured Query Language or SQL (pronounced "sequel") is the most widely used language for interacting with relational databases, and is an essential skill for Data Science professionals.

⭐️ Contents ⭐️
⌨️ (0:00:00) Introduction
⌨️ (0:01:28) Relational Databases & SQL
⌨️ (0:03:05) Setting up MySQL
⌨️ (0:29:23) Inserting Data into the Table
⌨️ (0:56:32) Practice Exercises
⌨️ (1:22:12) Aggregation, grouping & pagination in SQL queries
⌨️ (2:23:17) Mapping and Arithmetic Functions
⌨️ (2:54:53) Working with Dates
⌨️ (3:12:16) Combining Tables using Joins
⌨️ (3:30:14) Executing SQL queries using Python and SQL Alchemy
⌨️ (4:11:06) 3 step approach to Interview Questions
⌨️ (4:36:58) Interview Q - Apple
⌨️ (4:38:59) Interview Q - Linkedin
⌨️ (4:43:09) Interview Q - Meta
⌨️ (4:50:41) Interview Q - Uber
⌨️ (4:59:18) Interview Q - Amazon
⌨️ (5:07:27) Interview Q - Google

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A visual description of Bayes' Theorem and the Naive Bayes algorithm, and an application to spam detection.
No previous knowledge is needed, aside from knowing how to multiply and divide, a visual mind and a desire to learn.

For a code implementation, check out this repo:
https://github.com/luisguiserr....ano/manning/tree/mas

0:00 Introduction
0:39 Spam Detector
4:59 Problem
10:34 Naive Bayes Classifier
17:00 Bayes Theorem

🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐀𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 𝐂𝐨𝐮𝐫𝐬𝐞 - 𝐁𝐞𝐠𝐢𝐧𝐧𝐞𝐫𝐬 𝐭𝐨 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝: https://www.edureka.co/advance....d-artificial-intelli (Use code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎")
In this Edureka video on “ Top 10 Generative AI tools”, you will learn about the top 10 Generative AI tools, I aim to show you what are the most trending Generative AI tools and how it is going to change our world. What do you think about these new AI Tools?

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