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This Edureka video on 'Support Vector Machine Tutorial For Beginners' covers A brief introduction to Support Vector Machine in Python with a use case to implement SVM using Python.

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----------------------------------------
About the Course :

Edureka’s Machine Learning Course using Python is designed to make you grab the concepts of Machine Learning. The Machine Learning training will provide 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 python programming language. Furthermore, you will be taught of Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to automate real life scenarios using Machine Learning Algorithms. Towards the end of the course we will be discussing various practical use cases of Machine Learning in python programming language to enhance your learning experience.
--------------------------------------
Why Learn Machine Learning with Python?

Data Science is a set of techniques that enables the 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. This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning.
--------------------------------------------
Who should go for this Course?

Edureka’s Python Machine Learning Certification Course is a good fit for the below professionals:
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
--------------------------------
Got a question on the topic? Please share it in the comment section below and our experts will answer it for you.
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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🔥Purdue - Applied Generative AI Specialization - https://www.simplilearn.com/applied-ai-course?utm_campaign=yWg5nhxRCpY&utm_medium=DescriptionFirstFold&utm_source=Youtube
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🔥Advanced Executive Program In Applied Generative AI - https://www.simplilearn.com/applied-generative-ai-course?utm_campaign=yWg5nhxRCpY&utm_medium=DescriptionFirstFold&utm_source=Youtube

This Generative AI Full Course 2025 by Simplilearn provides a structured learning path, starting with the fundamentals of Generative AI and a detailed roadmap to mastering it. Learners explore top AI technologies, including DeepSeek R1, Deep Learning, and Search GPT, followed by essential tools like LangChain. The course dives into Generative Adversarial Networks (GANs), Transformers, and Long Short-Term Memory (LSTM) networks, forming the foundation of modern AI models. It then introduces Large Language Models (LLMs), Machine Learning concepts, and Reinforcement Learning, leading into practical applications like ChatGPT analysis and OpenAI Sora. Advanced topics include LLM benchmarking, Hugging Face tutorials, and OpenAI's ChatGPT O1 model. Practical insights into Generative AI tools for job interviews, Agentic AI, and AI monetization strategies help learners stay ahead in the field. The course concludes with a look at Google Quantum AI and Machine Learning interview preparation, ensuring a strong grasp of both theoretical and applied AI concepts.

00:00:00 - Introduction to Gen AI Full Course 2025
00:00:45 - What is Gen AI
00:11:24 - Roadmap Gen AI
01:16:27 - What is Machine Learning
01:23:26 - Deep Learning
01:24:50 - Introduction to LLM
01:47:32 - What are Gen AI Agents
02:24:34 - Agentic AI
03:21:06 - Machine Learning Tutorial
05:26:29 - Reinforcement Learning
05:42:34 - Deepseek r1
05:53:47 - Install Deepseek
06:44:30 - Hugging Face and its tutorial
06:52:47 - Search GPT
06:55:02 - Langchain
07:51:37 - Generative Adversarial Tutorial
09:21:57 - Introduction to agentic workflow
09:31:19 - What are Gans
09:32:20 - Transformers in AI
09:44:49 - LSTM
10:04:40 - CHatgpt analyse
10:15:36 - Openai sora
10:24:29 - LLM Benchmarkeing
10:39:53 - Open ai chatgpt o1 model
10:48:16 - Google Quantam AI
10:53:49 - Majorana
10:58:58 - Gen ai tools for job interview
12:59:19 - Deep learning Interview questions
13:05:13 - Claude 3 sonnet

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➡️ About Professional Certificate Program in Generative AI and Machine Learning

Dive into the future of AI with our Generative AI & Machine Learning course, in collaboration with E&ICT Academy, IIT Guwahati. Learn tools like ChatGPT, OpenAI, Hugging Face, Python, and more.

Key Features:
✅ Program completion certificate from E&ICT Academy, IIT Guwahati
✅ Curriculum delivered in live virtual classes by seasoned industry experts
✅ Exposure to the latest AI advancements, such as generative AI, LLMs, and prompt engineering
✅ Interactive live-virtual masterclasses delivered by esteemed IIT Guwahati faculty
✅ Opportunity to earn an 'Executive Alumni Status' from E&ICT Academy, IIT Guwahati
✅ Eligibility for a campus immersion program organized at IIT Guwahati
✅ Exclusive hackathons and “ask-me-anything” sessions by IBM
✅ Certificates for IBM courses and industry masterclasses by IBM experts
✅ Practical learning through 25+ hands-on projects and 3 industry-oriented capstone projects
✅ Access to a wide array of AI tools such as ChatGPT, Hugging Face, DALL-E 2, Midjourney and more
✅ Simplilearn's JobAssist helps you get noticed by top hiring companies

Skills Covered:
✅ Generative AI
✅ Prompt Engineering
✅ Chatbot Development
✅ Supervised and Unsupervised Learning
✅ Model Training and Optimization
✅ Model Evaluation and Validation
✅ Ensemble Methods
✅ Deep Learning
✅ Natural Language Processing
✅ Computer Vision
✅ Reinforcement Learning
✅ Machine Learning Algorithms
✅ Speech Recognition
✅ Statistics

Learning Path:
✅ Program Induction
✅ Programming Fundamentals
✅ Python for Data Science (IBM)
✅ Applied Data Science with Python
✅ Machine Learning
✅ Deep Learning with TensorFlow (IBM)
✅ Deep Learning Specialization
✅ Essentials of Generative AI, Prompt Engineering & ChatGPT
✅ Advanced Generative AI
✅ Capstone
Electives:
✅ ADL & Computer Vision
✅ NLP and Speech Recognition

👉 Learn More At: https://www.simplilearn.com/iitg-generative-ai-machine-learning-program?utm_campaign=yWg5nhxRCpY&utm_medium=Description&utm_source=Youtube

Reinforcement Learning with Human Feedback (RLHF) is a method used for training Large Language Models (LLMs). In the heart of RLHF lies a very powerful reinforcement learning method called Proximal Policy Optimization. Learn about it in this simple video!

This is the first one in a series of 3 videos dedicated to the reinforcement learning methods used for training LLMs.

Full Playlist: https://www.youtube.com/playli....st?list=PLs8w1Cdi-zv

Video 0 (Optional): Introduction to deep reinforcement learning https://www.youtube.com/watch?v=SgC6AZss478
Video 1 (This one): Proximal Policy Optimization
Video 2: Reinforcement Learning with Human Feedback https://www.youtube.com/watch?v=Z_JUqJBpVOk
Video 3 (Coming soon!): Deterministic Policy Optimization

00:00 Introduction
01:25 Gridworld
03:10 States and Action
04:01 Values
07:30 Policy
09:39 Neural Networks
16:14 Training the value neural network (Gain)
22:50 Training the policy neural network (Surrogate Objective Function)
33:38 Clipping the surrogate objective function
36:49 Summary

Get the Grokking Machine Learning book!
https://manning.com/books/grok....king-machine-learnin
Discount code (40%): serranoyt
(Use the discount code on checkout)

Reinforcement Learning with Human Feedback (RLHF) is a method used for training Large Language Models (LLMs). In the heart of RLHF lies a very powerful reinforcement learning method called Proximal Policy Optimization. Learn about it in this simple video!

This is the first one in a series of 3 videos dedicated to the reinforcement learning methods used for training LLMs.

Full Playlist: https://www.youtube.com/playli....st?list=PLs8w1Cdi-zv

Video 0 (Optional): Introduction to deep reinforcement learning https://www.youtube.com/watch?v=SgC6AZss478
Video 1: Proximal Policy Optimization https://www.youtube.com/watch?v=TjHH_--7l8g
Video 2 (This one): Reinforcement Learning with Human Feedback
Video 3 (Coming soon!): Deterministic Policy Optimization

00:00 Introduction
00:48 Intro to Reinforcement Learning (RL)
02:47 Intro to Proximal Policy Optimization (PPO)
4:17 Intro to Large Language Models (LLMs)
6:50 Reinforcement Learning with Human Feedback (RLHF)
13:08 Interpretation of the Neural Networks
14:36 Conclusion

Get the Grokking Machine Learning book!
https://manning.com/books/grok....king-machine-learnin
Discount code (40%): serranoyt
(Use the discount code on checkout)

🚀 Small Language Models (SLMs): The Future of AI? 🤖

Most people think AI—like ChatGPT—needs huge servers and an internet connection. While that’s true for Large Language Models (LLMs), AI doesn’t have to be massive!

Introducing Small Language Models (SLMs)—AI that’s lightweight, efficient, and runs entirely offline. 📱💡

🔍 What You’ll Learn in This Video:
✅ SLMs vs. LLMs – What’s the difference?
✅ How SLMs work – Train them on your data for specific tasks.
✅ Real-world examples – AI for customer support, healthcare, and education.
✅ Why SLMs are the future – Privacy-friendly, low-energy, and cost-effective!

🌎 Why This Matters:
Unlike LLMs that require cloud computing, SLMs can run directly on your phone, laptop, or even a Raspberry Pi! No internet needed, no privacy concerns, and perfect for businesses, remote areas, and industries with strict data security requirements.

💡 SLMs Are Changing AI Forever!
• Instant AI-powered customer support 🏢
• Medical assistance in remote areas 🏥
• Personalized education tools for students 📚
• Energy-efficient AI for sustainability 🌱

From scratch implementation of Naive Bayes Classifier in Python. In the video I explain the theory briefly and focus is on the actual implementation. Specifically this is implementation of Gaussian Naive Bayes Classifier.

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
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In this video I explain how Word2Vec works and it's two model variants in Continuous Bag of words (CBOW) and Skip-Gram. I also give an intuitive understanding of what embeddings are, why they are important as this is fundamentally what this algorithm is trying to learn.

Timestamps:
0:00 - Introduction to Word2vec
0:54 - Understanding Embeddings
5:20 - CBOW model of Word2Vec
8:46 - Skip-Gram model of Word2Vec
9:34 - Outro

In a quest to teach neural networks via transformers to write Python code. Project name: Generative Python Transformers!

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Uploaded in 4K UHD, HDR, and Dolby Atmos to provide the highest viewing quality. You must have an HDR-capable screen to view HDR video, otherwise, YouTube will automatically show you SDR.

I started the clip very early to give context about the grim state of the battlefield and the arrival of the Rohirrim as all hope seemed lost. Montage and edit from the December 1, 2020 release in 4K Ultra HD Blu-Ray with Dolby Atmos 5.1 audio. The coloring has been totally remastered by film director Peter Jackson to get a consistent look throughout all of the movies.

Director Peter Jackson's quote about the re-release: "The thing with 4K is not just to go for pristine sharpness, it is to preserve the cinematic look of it at the same time as everything becoming crisp."

The quality of this release is incredible. The visuals are gorgeous and it is worth it to watch all extended editions of the movies over again!

Respective owners of filmed content: New Line Cinema, Warner Brothers. No copyright infringement intended, just a humble fan that thinks this incredible scene should be on YouTube under fair use. Footage from Lord of the Rings Return of the King Extended Edition.

Training a large-scale deep net is a computationally expensive process, and common CPUs are generally insufficient for the task. GPUs are a great tool for speeding up training, but there are several other options available.

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A CPU is a versatile tool than can be used across many domains of computation. However, the cost of this versatility is the dependence on sophisticated control mechanisms needed to manage the flow of tasks. CPUs also perform tasks serially, requiring the use of a limited number of cores in order to build in parallelism. Even though CPU speeds and memory limits have increased over the years, a CPU is still an impractical choice for training large deep nets.

Vector implementations can be used to speed up the deep net training process. Generally, parallelism comes in the form of both parallel processing and parallel programming. Parallel processing can either involve shared resources on a single computer, or distributed computing across a cluster of nodes.

The GPU is a common tool for parallel processing. As opposed to a CPU, GPUs tend to hold large numbers of cores – anywhere from 100s to even 1000s. Each of these cores is capable of general purpose computing, and the core structure allows for large amounts of parallelism. As a result, GPUs are a popular choice for training large deep nets. The Deep Learning community provides GPU support through various libraries, implementations, and a vibrant ecosystem fostered by nVidia. The main downside of a GPU is the amount of power required to run one relative to the alternatives.

The “Field Programmable Gate Array”, or FPGA, is another choice for training a deep net. FPGAs were originally used by electrical engineers to design mock-ups for different computer chips without having to custom build a chip for each solution. With an FPGA, chip function can be programmed at the lowest level – the logic gate. With this flexibility, an FPGA can be tailored for deep nets so as to require less power than a GPU. Aside from speeding up the training process, FPGAs can also be used to run the resultant models. For example, FPGAs would be useful for running a complex convolutional net over thousands of images every second. The downside of an FPGA is the specialized knowledge required during design, setup, and configuration.

Another option is the “Application Specific Integrated Circuit”, or ASIC. ASICs are highly specialized, with designs built in at the hardware and integrated circuit level. Once built, they will perform very well at the task they were designed for, but are generally unusable in any other task. Compared to GPUs and FPGAs, ASICs tend to have the lowest power consumption requirements. There are several Deep Learning ASICs such as the Google Tensor Processing Unit (TPU), and the chip being built by Nervana Systems.

There are a few parallelism options available with distributed computing such as data parallelism, model parallelism, and pipeline parallelism. With data parallelism, different subsets of the data are trained on different nodes in parallel for each training pass, followed by parameter averaging and replacement across the cluster. Libraries like TensorFlow support model parallelism, where different portions of the model are trained on different devices in parallel. With pipeline parallelism, workers are dedicated to tasks, like in an assembly line. The main idea is to ensure that each worker is relatively well-utilized. A worker starts the next job as soon as the current one is complete, a strategy that minimizes the total amount of wasted time.

Parallel programming research has been active for decades, and many advanced techniques have been developed. Generally, algorithms should be designed with parallelism in mind in order to take full advantage of the hardware. One such way to do this is to decompose the data model into independent chunks that each perform one instance of a task. Another option is to group all the tasks by their dependencies, so that each group is completely independent of the others. As an addition, you can implement threads or processes that handle different task groups. These threads can be used as a standalone solution, but will provide significant speed improvements when combined with the grouping method. To learn more about this topic, follow this link to the Open HPI Massive Open Online course (MOOC) on parallel programming - https://open.hpi.de/courses/parprog2014.

Credits
Nickey Pickorita (YouTube art) -
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Dan Partynski (Copy Editing) -
https://www.linkedin.com/in/danielpartynski
Marek Scibior (Prezi creator, Illustrator) -
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Jagannath Rajagopal (Creator, Producer and Director) -
https://ca.linkedin.com/in/jagannathrajagopal

This video explores the GPT-2 paper "Language Models are Unsupervised Multitask Learners". The paper has this title because their experiments show how massive language models trained on massive datasets can perform tasks like Question Answering and Translation by carefully formatting them as language modeling inputs.

Paper Links
GPT-2 Paper: https://cdn.openai.com/better-....language-models/lang
AllenNLP GPT-2 Demo: https://demo.allennlp.org/next....-token-lm?text=Joel%
The Illustrated GPT-2: http://jalammar.github.io/illustrated-gpt2/
Combining GPT2 and BERT to make a fake person: https://www.bonkerfield.org/20....20/02/combining-gpt-

Thanks for watching! Please Subscribe!

_At Fully Connected, the MLOps conference held by Weights & Biases, Peter Welinder discusses how OpenAI (developers of GPT-4 and DALL-E 2) uses Weights & Biases for model training, with an emphasis on sharing training runs, the ability to create reports, which different teams use heavily to document their hypotheses, experiments, and conclusions resulting in mini scientific papers that provide insights into the work being done at OpenAI._

*Transcript*

Lukas Biewald - Okay, and final question, since this is our user conference and you are such a high profile user of OpenAI, I'm curious if you could say a little bit about how OpenAI uses Weights & Biases and if you have a favorite feature or part of Weights & Biases, we'd love to know about that.

Peter Welinder - Yeah, I mean, we use it for pretty much all of our model training. So just tracking them, I think there's a lot of just sharing of the fact that you can easily share training runs and stuff like that. It's a super-used feature.

But I think one thing that these days I do way, way less of that sort of work. So one of the features I really like is the ability to have reports and so on where people... So we use that quite heavily.

It depends a little bit on the team, but a number of teams are using that quite heavily to kind of really have a clear hypothesis.

Here's the hypothesis.
Here are the experiments that were run to kind of validate or invalidate that hypothesis.
Here's the conclusion.

You have all these mini scientific papers, essentially, on all of the stuff that's happening at OpenAI, which is incredibly interesting to kind of follow along with.

Lukas Biewald - Fantastic. That sounds very interesting.

Learn how to earn a free C# certification! freeCodeCamp has teamed up with Microsoft up to bring you a new free professional certification: the Foundational C# Certification. The course is a text based course, not a video course. This professional certification includes 35 hours of text-based training and interactive coding challenges all from Microsoft, and also an online certification exam by freeCodeCamp. In this video, freeCodeCamp team member @GavinLon will preview the course, along with telling you everything you need to do to take this free course.

💻 Access the course and certification here: https://www.freecodecamp.org/l....earn/foundational-c-

🔗 .NET Home Page: https://dotnet.microsoft.com/
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🔗 Beginner Videos: https://dotnet.microsoft.com/learn/videos
🔗 MS Learn for .NET: https://learn.microsoft.com/training/dotnet/
🔗 Docs: https://learn.microsoft.com/dotnet/

⭐️ Contents ⭐️
⌨️ (0:00:00) Introduction
⌨️ (0:05:09) Overview of how to acquire the Foundational C# with Microsoft Developer Certificate
⌨️ (0:09:34) In depth guide through Part1, Module1 (Write your first C# code)
⌨️ (0:48:42) Configure and Install Visual Studio Code and Install .NET 7 SDK
⌨️ (1:04:08) Code Example - if, else, else if statements in C#
⌨️ (1:10:43) Code Example -switch-case construct in C#
⌨️ (1:17:52) Code Example - write your first C# method
⌨️ (1:25:40) Gavin Lon takes the Foundational C# with Microsoft Developer Certification exam
⌨️ (1:27:46) Recommended content to continue your C# and .NET education upon finishing the certification
⌨️ (1:29:29) Conclusion

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In this comprehensive exploration of the field of deep learning with Professor Simon Prince who has just authored an entire text book on Deep Learning, we investigate the technical underpinnings that contribute to the field's unexpected success and confront the enduring conundrums that still perplex AI researchers.

Understanding Deep Learning - Prof. SIMON PRINCE [STAFF FAVOURITE]

Watch behind the scenes, get early access and join private Discord by supporting us on Patreon:
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https://twitter.com/MLStreetTalk

Key points discussed include the surprising efficiency of deep learning models, where high-dimensional loss functions are optimized in ways which defy traditional statistical expectations. Professor Prince provides an exposition on the choice of activation functions, architecture design considerations, and overparameterization. We scrutinize the generalization capabilities of neural networks, addressing the seeming paradox of well-performing overparameterized models. Professor Prince challenges popular misconceptions, shedding light on the manifold hypothesis and the role of data geometry in informing the training process. Professor Prince speaks about how layers within neural networks collaborate, recursively reconfiguring instance representations that contribute to both the stability of learning and the emergence of hierarchical feature representations. In addition to the primary discussion on technical elements and learning dynamics, the conversation briefly diverts to audit the implications of AI advancements with ethical concerns.

Pod version (with no music or sound effects): https://podcasters.spotify.com..../pod/show/machinelea

Follow Prof. Prince:
https://twitter.com/SimonPrinceAI
https://www.linkedin.com/in/si....mon-prince-615bb9165

Get the book now!
https://mitpress.mit.edu/97802....62048644/understandi
https://udlbook.github.io/udlbook/

Panel: Dr. Tim Scarfe -
https://www.linkedin.com/in/ecsquizor/
https://twitter.com/ecsquendor

TOC:
[00:00:00] Introduction
[00:11:03] General Book Discussion
[00:15:30] The Neural Metaphor
[00:17:56] Back to Book Discussion
[00:18:33] Emergence and the Mind
[00:29:10] Computation in Transformers
[00:31:12] Studio Interview with Prof. Simon Prince
[00:31:46] Why Deep Neural Networks Work: Spline Theory
[00:40:29] Overparameterization in Deep Learning
[00:43:42] Inductive Priors and the Manifold Hypothesis
[00:49:31] Universal Function Approximation and Deep Networks
[00:59:25] Training vs Inference: Model Bias
[01:03:43] Model Generalization Challenges
[01:11:47] Purple Segment: Unknown Topic
[01:12:45] Visualizations in Deep Learning
[01:18:03] Deep Learning Theories Overview
[01:24:29] Tricks in Neural Networks
[01:30:37] Critiques of ChatGPT
[01:42:45] Ethical Considerations in AI

References:

#61: Prof. YANN LECUN: Interpolation, Extrapolation and Linearisation (w/ Dr. Randall Balestriero)
https://youtube.com/watch?v=86ib0sfdFtw

Scaling down Deep Learning [Sam Greydanus]
https://arxiv.org/abs/2011.14439

"Broken Code" a book about Facebook's internal engineering and algorithmic governance [Jeff Horwitz]
https://www.penguinrandomhouse.....com/books/712678/br

Literature on neural tangent kernels as a lens into the training dynamics of neural networks.
https://en.wikipedia.org/wiki/....Neural_tangent_kerne

Zhang, C. et al. "Understanding deep learning requires rethinking generalization." ICLR, 2017.
https://arxiv.org/abs/1611.03530

Computer Vision: Models, Learning, and Inference, by Simon J.D. Prince
https://www.amazon.co.uk/Compu....ter-Vision-Models-Le

Deep Learning Book, by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
https://www.deeplearningbook.org/

Predicting the Future of AI with AI: High-quality link prediction in an exponentially growing knowledge network
https://arxiv.org/abs/2210.00881

Computer Vision: Algorithms and Applications, 2nd ed. [Szeliski]
https://szeliski.org/Book/

A Spline Theory of Deep Networks [Randall Balestriero]
https://proceedings.mlr.press/....v80/balestriero18b/b

DEEP NEURAL NETWORKS AS GAUSSIAN PROCESSES [Jaehoon Lee]
https://arxiv.org/abs/1711.00165

Do Transformer Modifications Transfer Across Implementations and Applications [Narang]
https://arxiv.org/abs/2102.11972

ConvNets Match Vision Transformers at Scale [Smith]
https://arxiv.org/abs/2310.16764

Dr Travis LaCroix (Wrote Ethics chapter with Simon)
https://travislacroix.github.io/

CIFAR DLRL 2020 Summer School