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🔥Generative AI Course: Masters Program: https://www.edureka.co/masters....-program/generative-

Join us for the AI & ML Full Course 2025, a complete live tutorial hosted by edureka! designed to help you master Artificial Intelligence and Machine Learning from the ground up. This live session will guide you through key concepts such as supervised and unsupervised learning, deep learning, neural networks, natural language processing, and real-world model deployment. Whether you're a beginner looking to enter the world of AI or a professional aiming to enhance your skills, this course is structured to make complex topics easy to understand. Using hands-on projects and Python-based examples, you’ll gain the practical knowledge needed to build intelligent systems and data-driven applications. This session is ideal for students, job seekers, developers, and tech enthusiasts who want to stay ahead in the AI/ML landscape in 2025. Make sure to join live to engage with instructors in real time and earn your certificate of participation. Subscribe to our channel and turn on notifications so you don’t miss this powerful learning experience.
00:00:00 Introduction
00:01:47 What is Artificial Intelligence?
00:07:49 Types of Artificial Intelligence
00:18:56 AI vs Machine Learning vs Deep Learning
00:32:41 Artificial Intelligence with Python
02:11:47 What is Machine Learning?
02:23:29 Types of Machine Learning Models
02:30:35 Machine Learning Algorithm
02:52:29 Linear Regression Algorithm
02:59:55 Logistic Regression Algorithm
03:47:04 Linear Regression Vs Logistic Regression
03:50:35 Decision Tree Algorithm
04:36:09 Random Forest
05:01:24 KNN Algorithm
05:33:47 Naive Bayes Classifier
05:55:28 Support Vector Machine
06:20:59 K- Means Clustering Algorithm
06:44:19 Hierarchical Clustering
06:50:41 Apriori Algorithm Explained
07:08:11 What is Deep Learning?
07:30:14 Artificial Neural Network
08:02:35 Convolutional Neural Network
08:23:06 Recurrent Neural Networks
08:52:02 LSTM Explained
09:45:59 Transformers Neural Networks Explained
09:56:02 What are GANs?
10:08:22 Future of AI/ML
10:22:58 Artificial Intelligence Interview Questions & Answers

🔴 𝐋𝐞𝐚𝐫𝐧 𝐓𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐅𝐨𝐫 𝐅𝐫𝐞𝐞! 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐘𝐨𝐮𝐓𝐮𝐛𝐞 𝐂𝐡𝐚𝐧𝐧𝐞𝐥: https://edrk.in/DKQQ4Py

📢📢Check out the latest 2025 video on Top 10 Technologies for the most up-to-date insights!
📌 Top 10 Technologies to Learn in 2025 → https://youtu.be/5kjWh8lBxC4

📝Feel free to share your comments below.📝

🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬

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RE-live your 90s with this beautiful compilation of the most romantic Bollywood songs. Don't forget to tell us your favorite one.

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Song Details:
Meri Mehbooba - 00:00
Humko Sirf Tumse - 05:31
Pyaar Nahin Karna Jahan - 09:47
Teri Chunnariya - 13:54
Dekha Tujhe Toh - 18:17
Teri Mohabbat Ne Dil - 24:52
Kitna Pyaara Tujhe Rabne Banaya - 28:18
Jaati Hoon Main - 33:51
Kahin Mujhe Pyar Hua Toh Nahin - 37:05
Soldier Soldier Meethi Baaten - 43:19
Jadoo Hai Tera - 49:33
Jo Haal Dil Ka - 56:07
Aankhon Se Tune Kya Keh Diya - 01:00:16

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Agentic AI is taking over conversations — but what does it really mean? In this video, we explore how Agentic AI goes beyond prompts and conversations to act, learn, and evolve — just like real-world autonomous systems. From perception to action, from reasoning to learning, we dive deep into the architecture of modern agents. You’ll learn how to build your own AI agents using tools like GPT-4, LangChain, and OpenAI's Agent SDK, and why the Model Context Protocol (MCP) is a critical layer for intelligent orchestration. Whether you're building with LLMs, deploying code autonomously, or scaling intelligent systems, this video has the insights to elevate your understanding.

My Linkedin Profile: https://www.linkedin.com/in/bytemonk/

📌 Timestamps
00:00 – Hook: Agentic AI – Hype or Reality?
00:35 – How Agentic AI Goes Beyond Autonomous AI
00:49 – Perceive, Reason, Act, Learn: The Agentic Loop
02:32 – How Modern Agents Actually Work (Architecture Breakdown)
04:14 – Real-World Example: Code Deployment Agent
05:38 – Tools to Build Agents: LangChain, Agent SDK & More
07:00 – What is MCP & Why It Matters in Agentic Workflows
07:55 – Final Thoughts: Future of AI is Agentic


https://www.youtube.com/playli....st?list=PLJq-63ZRPdB
https://www.youtube.com/playli....st?list=PLJq-63ZRPdB
https://www.youtube.com/playli....st?list=PLJq-63ZRPdB
https://www.youtube.com/playli....st?list=PLJq-63ZRPdB
https://www.youtube.com/playli....st?list=PLJq-63ZRPdB
https://www.youtube.com/playli....st?list=PLJq-63ZRPdB

AWS Certification:
AWS Certified Cloud Practioner: https://youtu.be/wF1pldkQrOY
AWS Certified Solution Architect Associate: https://youtu.be/GzomXNLFgkk
AWS Certified Solution Architect Professional: https://youtu.be/KFZrBxSA9tI

#AgenticAI #AIEngineering #LangChain #GPT4 #AutonomousAI #MCP #SoftwareEngineering

#gpt3 #openai #gpt-3

How far can you go with ONLY language modeling? Can a large enough language model perform NLP task out of the box? OpenAI take on these and other questions by training a transformer that is an order of magnitude larger than anything that has ever been built before and the results are astounding.

OUTLINE:
0:00 - Intro & Overview
1:20 - Language Models
2:45 - Language Modeling Datasets
3:20 - Model Size
5:35 - Transformer Models
7:25 - Fine Tuning
10:15 - In-Context Learning
17:15 - Start of Experimental Results
19:10 - Question Answering
23:10 - What I think is happening
28:50 - Translation
31:30 - Winograd Schemes
33:00 - Commonsense Reasoning
37:00 - Reading Comprehension
37:30 - SuperGLUE
40:40 - NLI
41:40 - Arithmetic Expressions
48:30 - Word Unscrambling
50:30 - SAT Analogies
52:10 - News Article Generation
58:10 - Made-up Words
1:01:10 - Training Set Contamination
1:03:10 - Task Examples

https://arxiv.org/abs/2005.14165
https://github.com/openai/gpt-3

Abstract:
Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general.

Authors: Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, Dario Amodei

Links:
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React: Use Array.map() to Dynamically Render Elements
#100DaysOfCode

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng continues his lecture about support vector machines, including soft margin optimization and kernels.

This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed.

Complete Playlist for the Course:
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CS 229 Course Website:
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Stanford University:
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Stanford University Channel on YouTube:
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🔥Edureka Machine Learning Certification Training: https://www.edureka.co/machine....-learning-certificat
This Edureka video on 'Statistics For Machine Learning' is the second class in the Python Machine Learning Series which gives a brief introduction to Statistics and Probability for Machine learning.

🟠 𝐂𝐥𝐚𝐬𝐬 𝐒𝐜𝐡𝐞𝐝𝐮𝐥𝐞 🟠

[𝗦𝗮𝘁𝘂𝗿𝗱𝗮𝘆 - 𝟭𝟱𝘁𝗵 𝗔𝘂𝗴𝘂𝘀𝘁]
Class 1 - Introduction To Machine Learning
Class 2 - Statistics For Machine Learning
Class 3 - Data Exploration 1 - ETL
Class 4 - Data Exploration 2 - Visualization
Class 5 - Data Exploration 3 - Data Cleaning
[𝐒𝐮𝐧𝐝𝐚𝐲 - 𝟏𝟔𝐭𝐡 𝐀𝐮𝐠𝐮𝐬𝐭]
Class 6 - Data Modeling 1 - Feature Engineering
Class 7 - Data Modeling 2 - Training The Model
Class 8 - Data Modeling 3 - Model Evaluation
Class 9 - Machine Learning Example
Class 10 - Advanced Machine Learning

Python Tutorial Playlist: https://goo.gl/WsBpKe
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#Edureka #PythonEdureka #pythonML #pythonMLseries #statisticsforML #pythonprojects #pythonprogramming #pythontutorial #PythonTraining #learnPython
--------------------------
How it Works?

Edureka’s Machine Learning Course using Python is designed to make you grab the concepts of Machine Learning. Machine Learning training will provide a deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in the python programming language and many more.
--------------------------
Why Learn Machine Learning using Python?

Data Science is a set of techniques that enables computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms.
--------------------------
Who should go for this Machine Learning Certification Training using Python?
- Developers aspiring to be a ‘Machine Learning Engineer'
- Analytics Managers who are leading a team of analysts
- Business Analysts who want to understand Machine Learning (ML) Techniques
- Information Architects who want to gain expertise in Predictive Analytics
- 'Python' professionals who want to design automatic predictive models
--------------------------
For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775

The world is entering a new era of warfare, with cyber and autonomous weapons taking center stage. These technologies are making militaries faster, smarter, more efficient. But if unchecked, they threaten to destabilize the world.
DW takes a deep dive into the future of conflict, uncovering an even more volatile world. Where a cyber intrusion against a nuclear early warning system can unleash a terrifying spiral of escalation; where “flash wars” can erupt from autonomous weapons interacting so fast that no human could keep up.
Germany’s Foreign Minister Heiko Maas tells DW that we have already entered the technological arms race that is propelling us towards this future. “We’re right in the middle of it. That’s the reality we have to deal with.”
And yet the world is failing to meet the challenge. Talks on controlling autonomous weapons have repeatedly been stalled by major powers seeking to carve out their own advantage. And cyber conflict has become not just a fear of the future but a permanent state of affairs.
DW finds out what must happen to steer the world in a safer direction, with leading voices from the fields of politics, diplomacy, intelligence, academia, and activism speaking out.

Chapters
00:00 - Introduction
02:37 - The Cyber Nuclear Nightmare
17:05 - Flash Wars And Autonomous Weapons
30:12 - Trading Markets And Flash Crashes
31:45 - Time To Act

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#FurureWars #AutonomousWeapons #CyberAttacks

How are technologies like ChatGPT created? And what does the future hold for AI language models?

This talk was filmed at the Royal Institution on 29th September 2023, in collaboration with The Alan Turing Institute.

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Watch the Q&A: https://youtu.be/9i2x2HyeW-Y

Generative AI refers to a type of artificial intelligence that involves creating new and original data or content. Unlike traditional AI models that rely on large datasets and algorithms to classify or predict outcomes, generative AI models are designed to learn the underlying patterns and structure of the data and generate novel outputs that mimic human creativity.

ChatGPT is perhaps the most well-known example, but the field is far larger and more varied than text generation. Other applications of generative AI include image and video synthesis, speech generation, music composition, and virtual reality.

In this lecture, Mirella Lapata will present an overview of this exciting—sometimes controversial—and rapidly evolving field.

Mirella Lapata is professor of natural language processing in the School of Informatics at the University of Edinburgh. Her research focuses on getting computers to understand, reason with, and generate natural language. She is the first recipient (2009) of the British Computer Society and Information Retrieval Specialist Group (BCS/IRSG) Karen Sparck Jones award and a Fellow of the Royal Society of Edinburgh, the ACL, and Academia Europaea.

00:00 Intro
2:38 Generative AI isn’t new – so what’s changed?
8:43 How did we get to ChatGPT?
12:38 How are Large Language Models created?
22:48 How good can a LLM become?
26:57 Unexpected effects of scaling up LLMs
28:05 How can ChatGPT meet the needs of humans?
32:30 Chat GPT demo
38:07 Are Language Models always right or fair?
40:21 The impact of LLMs on society
42:54 Is AI going to kill us all?

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Don McLaughlin, efkinel lo, Martin Paull, Ben Wynne-Simmons, Ivo Danihelka, Kevin Winoto, Jonathan Killin, Stephan Giersche, William Billy Robillard, Jeffrey Schweitzer, Frances Dunne, jonas.app, Tim Karr, Alan Latteri, David Crowner, Matt Townsend, THOMAS N TAMADA, Andrew McGhee, Paul Brown, David Schick, Dave Ostler, Osian Gwyn Williams, David Lindo, Roger Baker, Rebecca Pan
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Baraka is a 1992 American non-narrative documentary film directed by Ron Fricke and produced by Mark Magidson.

The word Baraka is defined within various Eastern religions as a powerful spiritual blessing that flows throughout many different aspects of life.

The Baraka project was filmed by a only 5 person crew, over a period of 14 months, in 24 different countries, and while spanning 6 of the 7 continents.

This is a short documentary released in 2008 about the 8K scanning and restoration process undertaken with the original film stock

Baraka was shot on 70mm film and then scanned at an oversampled 8K Ultra High Resolution (8,192 pixels across the frame) with DTS-HD Master Audio 5.1 @96k/24bits. It was then output onto Blu-ray in Full High Definition 1080p at a 16:9 Widescreen aspect ratio.

The scan done at 8K was oversampled, which means the film was scanned at a higher resolution than what was necessary for the final 1080p output that they intended and produced.

The purpose of scanning at 8K though was to create a super sharp, but down-sampled version of the film scan once it was output at Full HD 1080p. Thus, the resulting image from the 8K scan is a sharper quality 1080p output than if they had just scanned the film at 1080p or at a 4K scan resolution. The 1080p output from the 8K scan is also now perhaps an even better quality version than the original film print when viewed digitally on either a 1080p or 4K display.

The unanswered question though is if an 8K scan from the 65mm native film stock would produce an even more desirable looking image at 4K than at the 1080p resolution output which they had decided upon for this production.

But without there being any 4K sample outputs available (AFAIK) from the original film print to 8K scan (in order to rule out whether or not a 4K output might introduce any undesirable flaws, artifacts, or reveal any weaknesses from the physical film print at such a high resolution scan) it remains a question that still can’t be fully addressed.

In this video they also briefly mention DVD, which may be a bit confusing, but I can confirm that the 8K scan was never output at 480p resolution and was only ever released at 1080p resolution on Blu-ray.

One can still purchase the full Baraka 1080p film on Blu-ray from the 8K scan, which also includes this short documentary titled "Restoration" as a special feature on the disc. The Blu-ray disc also includes another 1+ hour documentary titled "A Closer Look” which is a BTS documentary on the shooting and production of Baraka itself.

Thus, the full 1080p version of the Baraka film, the "A Closer Look" documentary, and this short "Restoration" documentary are all included together on a single Blu-ray disc which is available from the link here: https://amzn.to/2Ii5na4

A final note in that I do not possess any copyrights to the content of this video nor is it my intention to infringe upon any existing copyrights of this content in any way. I also have no personal connection or otherwise to the producers of this video or to the creators of the Baraka film.

Welcome, to the Building with Instruction-Tuned LLMs: A Step-by-Step Guide workshop!

We will be taking questions during the event. Please submit your question or upvote others' here:
https://app.sli.do/event/erFLUz3s8yWhRZkFAkSx9b

Speakers
Dr. Greg Loughnane, Head of Product & Curriculum at FourthBrain
https://www.linkedin.com/in/gregloughnane/

Chris Alexiuk, is an LLM Instructor at FourthBrain
https://www.linkedin.com/in/csalexiuk/

Let us know how we're doing? We will be giving out discount codes for a selected number of people who fill out the survey:
https://forms.gle/qzBWfovxPTLPeVRN6

Looking to connect with your peer learners, share projects, and swap advice? Join our AI community:
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To learn more about us and signup for future events:
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Prepare for the course

This video explains the Transformer architecture in a very detailed way, including most math formulas in the paper, and the neural network operations behind it. The Transformer is the foundation of many powerful language models like BERT, GPT3, RoBERTa, XLNET, ELECTRA, T5. Understanding how it works in detail might help you modify, optimize, or improve it in the way you want.


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0:00 - Intro
1:10 - Architecture overview
1:56 - Encoder
3:58 - Residual connection & layer normalization
6:59 - Decoder
11:14 - Attention mechanism
14:30 - Scaled dot-product attention
20:22 - Learned projection layers
26:32 - Multi-head attention
28-39 - Encoder-decoder attention
31:18 - Encoder self-attention
31:34 - Decoder self-attention
33:58 - Position-wise feedforward network
36:41 - Word embedding
39:34 - Positional encoding
47:37 - Why self-attention

What Is GPT-3 Series
https://www.youtube.com/playli....st?list=PLoS8jSwcU-c

Paper: Attention Is All You Need
https://arxiv.org/abs/1706.03762

Abstract
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best
performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English- to-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.0 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.

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This video will introduce you to SQL Server. The tutorial will give you insights into the usage of SQL Server. You will get to know about SQL Server editions. Further, you will learn about the advantages of SQL Server Instances. The demonstration examples will educate you more on the topic.

The following topics are covered in the tutorial:

What is SQL Server?
Usage of SQL Server
SQL Server Editions
Advantages of SQL Server Instances
Hands-on Demonstration

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Master SQL and improve your career prospects with Simplilearn’s SQL Database Training Course. Functional knowledge of SQL (Structured Query Language), the leading programming language for relational database management systems, is in high demand and can set you apart in the job market. This SQL certification course gives you all of the information you need to successfully start working with SQL databases and make use of the database in your applications. Learn how to correctly structure your database, author efficient SQL statements, and clauses, and manage your SQL database for scalable growth.

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There are no prerequisites for this database training course. This course can be taken up by anyone who wants to learn SQL.

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As with his previous incarnations, Godzilla can release pulses of radioactive energy. He first demonstrated this in Godzilla: King of the Monsters where, after absorbing the radiation of a nuclear blast and Mothra’s life force, Godzilla starts to heat up with a fiery red glow, burning and melting objects in his vicinity while becoming completely immune to Ghidorah's gravity beams. This energy overload grants him control over tremendously powerful thermonuclear pulses that incinerates most of Ghidorah's body and a large portion of Boston with him. The shape of these particular pulses also resembles Mothra's wings while the sound of the explosions resembles Mothra's screech, for this ability is a result of Godzilla and Mothra's symbiotic bond, and can only be used in the most specific of circumstances.

I edited the scene a little, removing some irrelevant humans parts, keeping it as seamless and objective as possible, focusing only on the Kaijus.

Source: 3840x2160
Upscale: 7680x4320

© 2019 Warner Bros. Entertainment Inc.

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This video tutorial on probability and statistics gives a basic overview of the chi-square distribution. It explains how to use the chi-square distribution to conduct goodness of fit test to determine whether the null hypothesis should be accepted or rejected.

In this video, we will discuss -
00:00 Research and Null Hypothesis
01:19 Expected and Observed Frequency
01:47 Chi-Square Test
03:56 Example

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Learning Objectives of Simplilearn Data Scientist Program:
Data Scientist is one of the hottest professions. IBM predicts the demand for Data Scientists will rise by 28% by 2020. Simplilearn's Data Science certification course co-developed with IBM encourages you to master skills including statistics, hypothesis testing, data mining, clustering, decision trees, linear and logistic regression, data wrangling, data visualization, regression models, Hadoop, Spark, PROC SQL, SAS Macros, recommendation engine, supervised, and unsupervised learning and more.

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Extended motivational speech to study deep learning mathematically.

I gave this talk at an NSF Town Hall where the goal was to discuss successes of deep learning especially in light of more traditional fields (other talks can be found here: https://www.nsf.gov/events/event_summ.jsp?cntn_id=304013&org=CISE).

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This Random Forest Algorithm tutorial will explain how the Random Forest algorithm works. By the end of this video, you will be able to understand what is Machine Learning, what is a Classification problem, applications of Random Forest, why we need Random Forest, how it works with simple examples, and how to implement a Random Forest algorithm in Machine Learning. This video is a part of the Machine Learning with Python Series.

Below are the topics covered in this Random Forest Algorithm tutorial:
00:00 - 02:08 Applications of Random Forest Algorithm
02:08 - 02:59 Agenda
02:59 - 04:07 Classification Algorithms
04:07 - 05:36 Why Random Forest?
05:36 - 06:40 What is Random Forest Algorithm?
06:40 - 11:01 What is a Decision Tree?
11:01 - 14:18 How does the Decision Tree algorithm work?
14:18 - 17:27 How does the Random Forest algorithm work?
17:27 - 45:34 Use Case - IRIS Flower Analysis using Python

Dataset Link - https://drive.google.com/drive..../folders/1MQ5Nnhj3gs


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What is Random Forest Algorithm?
The random forest algorithm is a supervised machine learning algorithm that takes randomly selected data and creates different decision trees. It then makes the collection of votes from trees to decide the class of the test object.

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About Simplilearn Machine Learning course:

A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with the knowledge and hands-on skills required for certification and job competency in Machine Learning.

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What skills will you learn from this Machine Learning course?

By the end of this Machine Learning course, you will be able to:

1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.

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Who should take this Machine Learning Training Course?

We recommend this Machine Learning training course for the following professionals in particular:

1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning

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