🔥 Edureka Machine Learning Certification training (𝐔𝐬𝐞 𝐂𝐨𝐝𝐞: 𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎) : https://www.edureka.co/masters....-program/machine-lea
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.
Topics covered in this video:

00:00:00 Introduction
00:01:17 Agenda
00:01:54 What is Machine Learning?
00:05:38 What is Support Vector Machine?
00:06:54 How does SVM work?
00:09:07 SVM Kernel
00:14:18 SVM Use case
00:14:35 How to implement SVM?




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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.
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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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🔥Edureka’s Generative AI Master course: https://www.edureka.co/masters....-program/generative-
Explore the fundamentals and applications of Generative AI in this comprehensive Generative AI Full course, featuring tutorials on essential concepts such as Text Classification, Autoencoders, GANs, and models like ChatGPT. Delve into the basics of Generative AI, Artificial Intelligence, Machine Learning, and Deep Learning while exploring popular tools like TensorFlow and Keras. Gain insights into various types of AI and learn how to become an AI Engineer.


📢📢 𝐓𝐨𝐩 𝟏𝟎 𝐓𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐭𝐨 𝐋𝐞𝐚𝐫𝐧 𝐢𝐧 𝟐𝟎𝟐𝟒 𝐒𝐞𝐫𝐢𝐞𝐬 📢📢
⏩ NEW Top 10 Technologies To Learn In 2024 - https://www.youtube.com/watch?v=vaLXPv0ewHU

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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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🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐂𝐡𝐚𝐭𝐆𝐏𝐓 𝐂𝐨𝐮𝐫𝐬𝐞 - 𝐁𝐞𝐠𝐢𝐧𝐧𝐞𝐫𝐬 𝐭𝐨 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝: https://www.edureka.co/openai-....chatgpt-training-cou
This Edureka video on 𝐂𝐡𝐚𝐭𝐆𝐏𝐓 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 will talk about multiple use cases where you can use ChatGPT. There are several day to day applications where you can leverage ChatGPT to do the job for you. Below are some important ChatGPT applications covered in this ChatGPT tutorial:
00:00:00 Introduction
00:00:50 ChatGPT for Writing Code
00:02:25 ChatGPT for Code Debugging
00:03:37 ChatGPT for Content Writing
00:05:03 ChatGPT for SEO
00:06:18 ChatGPT for Language Translation
00:07:01 ChatGPT for Writing Legal Documents
00:08:11 ChatGPT for College Assignments
00:08:37 ChatGPT to make Workout Schedule

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Prepare for a job interview about deep learning. This course covers 50 common interview questions related to deep learning and gives detailed explanations.

✏️ Course created by Tatev Karen Aslanyan.

✏️ Expanded course with 100 questions: https://academy.lunartech.ai/p....roduct/deep-learning

⭐️ Contents ⭐️
⌨️ 0:00:00 Introduction
⌨️ 0:08:20 Question 1: What is Deep Learning?
⌨️ 0:11:45 Question 2: How does Deep Learning differ from traditional Machine Learning?
⌨️ 0:15:25 Question 3: What is a Neural Network?
⌨️ 0:21:40 Question 4: Explain the concept of a neuron in Deep Learning
⌨️ 0:24:35 Question 5: Explain architecture of Neural Networks in simple way
⌨️ 0:31:45 Question 6: What is an activation function in a Neural Network?
⌨️ 0:35:00 Question 7: Name few popular activation functions and describe them
⌨️ 0:47:40 Question 8: What happens if you do not use any activation functions in a neural network?
⌨️ 0:48:20 Question 9: Describe how training of basic Neural Networks works
⌨️ 0:53:45 Question 10: What is Gradient Descent?
⌨️ 1:03:50 Question 11: What is the function of an optimizer in Deep Learning?
⌨️ 1:09:25 Question 12: What is backpropagation, and why is it important in Deep Learning?
⌨️ 1:17:25 Question 13: How is backpropagation different from gradient descent?
⌨️ 1:19:55 Question 14: Describe what Vanishing Gradient Problem is and it’s impact on NN
⌨️ 1:25:55 Question 15: Describe what Exploding Gradients Problem is and it’s impact on NN
⌨️ 1:33:55 Question 16: There is a neuron in the hidden layer that always results in an error. What could be the reason?
⌨️ 1:37:50 Question 17: What do you understand by a computational graph?
⌨️ 1:43:28 Question 18: What is Loss Function and what are various Loss functions used in Deep Learning?
⌨️ 1:47:15 Question 19: What is Cross Entropy loss function and how is it called in industry?
⌨️ 1:50:18 Question 20: Why is Cross-entropy preferred as the cost function for multi-class classification problems?
⌨️ 1:53:10 Question 21: What is SGD and why it’s used in training Neural Networks?
⌨️ 1:58:24 Question 22: Why does stochastic gradient descent oscillate towards local minima?
⌨️ 2:03:38 Question 23: How is GD different from SGD?
⌨️ 2:08:19 Question 24: How can optimization methods like gradient descent be improved? What is the role of the momentum term?
⌨️ 2:14:22 Question 25: Compare batch gradient descent, minibatch gradient descent, and stochastic gradient descent.
⌨️ 2:19:12 Question 26: How to decide batch size in deep learning (considering both too small and too large sizes)?
⌨️ 2:26:01 Question 27: Batch Size vs Model Performance: How does the batch size impact the performance of a deep learning model?
⌨️ 2:29:33 Question 28: What is Hessian, and how can it be used for faster training? What are its disadvantages?
⌨️ 2:34:12 Question 29: What is RMSProp and how does it work?
⌨️ 2:38:43 Question 30: Discuss the concept of an adaptive learning rate. Describe adaptive learning methods
⌨️ 2:43:34 Question 31: What is Adam and why is it used most of the time in NNs?
⌨️ 2:49:59 Question 32: What is AdamW and why it’s preferred over Adam?
⌨️ 2:54:50 Question 33: What is Batch Normalization and why it’s used in NN?
⌨️ 3:03:19 Question 34: What is Layer Normalization, and why it’s used in NN?
⌨️ 3:06:20 Question 35: What are Residual Connections and their function in NN?
⌨️ 3:15:05 Question 36: What is Gradient clipping and their impact on NN?
⌨️ 3:18:09 Question 37: What is Xavier Initialization and why it’s used in NN?
⌨️ 3:22:13 Question 38: What are different ways to solve Vanishing gradients?
⌨️ 3:25:25 Question 39: What are ways to solve Exploding Gradients?
⌨️ 3:26:42 Question 40: What happens if the Neural Network is suffering from Overfitting relate to large weights?
⌨️ 3:29:18 Question 41: What is Dropout and how does it work?
⌨️ 3:33:59 Question 42: How does Dropout prevent overfitting in NN?
⌨️ 3:35:06 Question 43: Is Dropout like Random Forest?
⌨️ 3:39:21 Question 44: What is the impact of Drop Out on the training vs testing?
⌨️ 3:41:20 Question 45: What are L2/L1 Regularizations and how do they prevent overfitting in NN?
⌨️ 3:44:39 Question 46: What is the difference between L1 and L2 regularisations in NN?
⌨️ 3:48:43 Question 47: How do L1 vs L2 Regularization impact the Weights in a NN?
⌨️ 3:51:56 Question 48: What is the curse of dimensionality in ML or AI?
⌨️ 3:53:04 Question 49: How deep learning models tackle the curse of dimensionality?
⌨️ 3:56:47 Question 50: What are Generative Models, give examples?

Announcement: New Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML
40% Discount code: serranoyt

Welcome! I believe that math concepts can be learned through simple explanations, analogies and easy-to-understand visualizations. I am passionate about teaching math concepts in relatable, friendly and simple ways. My videos are designed so that beginners can clearly learn new concepts while experts can see them under a new light. I hope you enjoy the channel and please drop me a line if you have any comments or suggestions. Twitter: @luis_likes_math.

In this video you'll learn about ways to tell probability distributions apart. method for finding zeros of a polynomial.
This is part of the Math for ML Specialization with @Deeplearningai Check it out here!
https://bit.ly/4imAtNz

Other samples of the M4ML Specialization:
Linear Algebra: Discrete Dynamical Systems: https://www.youtube.com/watch?v=7SfocUa8gis
Calculus: Newton's method https://www.youtube.com/watch?v=TEsJpHTGURo
Probability/Statistics: (this one)

00:24 Average
04:31 Variance
10:24 Skewness
16:32 Kurtosis

Covariance matrix video: https://youtu.be/WBlnwvjfMtQ
Clustering video: https://youtu.be/QXOkPvFM6NU

A friendly description of Gaussian mixture models, a very useful soft clustering method.

Announcement: New Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML
40% discount code: serranoyt

0:00 Introduction
0:13 Clustering applications
1:56 Hard clustering - soft clustering
3:36 Step 1: Colouring points
6:10 Step 2: Fitting a Gaussian
10:33 Gaussian Mixture Models (GMM)

This is Week 01 Live Session of BeSA Batch 09 focused on Agentic AI on AWS.
🔴 LIVE SESSION: Foundation of Agentic AI

Join us for a live deep-dive into the Foundation of Agentic AI — the next evolution of AI systems that can plan, reason, and take actions autonomously.

In this live session, we will break down the core concepts behind Agentic AI in a practical and beginner-friendly way. You’ll understand how AI is shifting from passive assistants to autonomous agents capable of goal-driven execution and intelligent decision-making.

What you’ll learn in this LIVE session:
- What is Agentic AI (vs Generative AI)
- Core building blocks of AI Agents
- Planning, reasoning, and tool usage
- Memory and orchestration in agentic systems
- Single-agent vs Multi-agent architectures
- Real-world use cases and architecture patterns

How Agentic AI fits into modern cloud and AI solutions

This session is ideal for Solutions Architects, Developers, AI Engineers, and anyone looking to understand how next-generation AI systems are designed and implemented.

📌 Live Q&A included — bring your questions!
📌 Beginner-friendly + architecture-focused
📌 Practical insights for real-world AI systems

Don’t miss this session if you want to build a strong foundation in AI Agents and Agentic AI.

#AgenticAI #LiveSession #AIonAWS #AIAgents #ArtificialIntelligence #AIArchitecture #GenerativeAI

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New Claude Code Update is INSANE! (AI Coding Revolution)

Anthropic just released a massive update to Claude Code Desktop that transforms your computer into an autonomous AI coding machine. Discover how it handles everything from live app previews to automated PR merges while streamlining your entire development workflow. Learn why this update is a game-changer for developers and how to start using it today.

00:00 - Intro: The Massive Claude Update
01:04 - Live App Previews & Auto-Fixing
02:07 - Automated Local Code Reviews
02:47 - CI Failure Handling & Auto-Fix
03:26 - PR Monitoring & Auto-Merge
04:21 - Session Mobility Explained
04:48 - The Future of AI Development
06:59 - How to Get Started with Claude Code

MIT 22.01 Introduction to Nuclear Engineering and Ionizing Radiation, Fall 2016
Instructor: Michael Short
View the complete course: https://ocw.mit.edu/22-01F16
YouTube Playlist: https://www.youtube.com/playli....st?list=PLUl4u3cNGP6

A brief summary of the discovery of forms of ionizing radiation up to the 1932 discovery of the neutron. We introduce mass-energy equivalence for the first time and explain how these cutting-edge experiments (for their time) conclusively proved the existence of high-energy, ionizing radiation.

License: Creative Commons BY-NC-SA
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MIT 18.06 Linear Algebra, Spring 2005
Instructor: Gilbert Strang
View the complete course: http://ocw.mit.edu/18-06S05
YouTube Playlist: https://www.youtube.com/playli....st?list=PLE7DDD91010

1. The Geometry of Linear Equations

License: Creative Commons BY-NC-SA
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MIT 15.S21 Nuts and Bolts of Business Plans, IAP 2014
View the complete course: http://ocw.mit.edu/15-S21IAP14
Instructor: Joe Hadzima

What is it, why do I need it and what is it used for? Practical do's and don'ts in preparing a Business Plan. Things to keep in mind in writing a Business Plan which will improve your chances of obtaining funding and running a successful business.

License: Creative Commons BY-NC-SA
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More courses at http://ocw.mit.edu

MIT 6.622 Power Electronics, Spring 2023
Instructor: David Perreault

View the complete course (or resource): https://ocw.mit.edu/courses/6-....622-power-electronic
YouTube Playlist: https://www.youtube.com/playli....st?list=PLUl4u3cNGP6

In this lecture, we first review linear vs. switching regulators. We then introduce definitions and methods for analyzing switching circuits, including the Method of Assumed States and Periodic Steady State.

License: Creative Commons BY-NC-SA
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More courses at https://ocw.mit.edu

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The imagedeep.io series serves to bridge the knowledge gap between medical imaging and AI education.

It was co-founded and is co-instructed by:

Mazen Zawaideh, MD. Chief Radiology Resident and imagedeep.io co-instructor.

David Haynor, MD, Ph.D. Professor of Neuroradiology and imagedeep.io co-instructor.

Nathan Cross, MD. Assistant Professor of Neuroradiology and imagedeep.io co-instructor.

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