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This video on Machine Learning Full Course video by Simplilearn, will help begin with the basics of Artificial intelligence and machine learning. This Machine Learning Full Course begins with an Introduction to Machine Learning, that helps you with a complete guide to mastering machine learning concepts and applications. It begins with an explanation of what machine learning is and offers insights into how to learn AI and ML effectively. The course includes a deep learning tutorial for beginners and introduces the best AI coding tools to boost productivity. Practical guidance on how to get a job in AI and the basics of machine learning sets the foundation for career advancement. Advanced topics like Q-learning, reinforcement learning, and LSTM (Long Short-Term Memory) are also explored. You’ll delve into linear algebra for machine learning, LLM benchmarking, and learn how to run Llama privately. Additional highlights include a stable diffusion tutorial, exploratory data analysis using Python, and tools like Meta’s new Llama 3.2. The course concludes with an in-depth look at the confusion matrix in ML, equipping you with both theoretical and practical knowledge for machine learning projects.

Following are the topics covered in this Machine Learning Full Course:
00:00:00 Introduction to Machine Learning Full Course
00:56:31 What is Machine Learning
00:58:05 How To Learn Artificial Intelligence and Machine Learning
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01:31:16 How to get a job in AI
04:28:15 Machine Learning Basics
04:29:05 What is Q Learning
05:19:32 Linear Algebra for Machine Learning
05:28:25 LLM Benchmarking
05:37:37 Run Lllama Privately
05:48:12 Stable diffusion tutorial
06:22:15 Exploratory Data Analysis Using Python
10:33:36 Reinforcement Learning
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11:00:05 Confusion Matrix ML
11:06:17 What is LSTM

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Designed to boost your career as an AI and ML professional, this program showcases Caltech CTME's excellence and IBM's industry prowess. The artificial intelligence course covers key concepts like Statistics, Data Science with Python, Machine Learning, Deep Learning, NLP, and Reinforcement Learning through an interactive learning model with live sessions.

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In this video on Excel Tutorials for Beginners, you'll learn the fundamentals of working with Microsoft Excel. It covers the Excel interface, allowing you to get familiar with the ribbon, worksheets, and cells. Data entry techniques like typing values directly and using the AutoFill feature for repetitive data are demonstrated. The video guides you through basic calculations using formulas and functions for addition, subtraction, multiplication, and division. Formatting is also covered, showing you ways to enhance your data's appearance by changing font styles, colors, and number formats. Additionally, you'll learn how to transform your data into visuals like charts and graphs for better analysis and presentation. By the end, you'll have a solid understanding of essential Excel skills for organizing, calculating, and presenting data effectively, making it an ideal resource for Excel beginners.

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In this comprehensive data analysis full course, you will learn everything you need to know to become a data analyst, from the basics of data analysis to the most advanced techniques. This course is designed for beginners with no prior experience in data analysis.

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

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🔥Machine Learning Training with Python: https://www.edureka.co/machine....-learning-certificat

This Edureka video on Random Forest in Machine Learning explains the concept of the Random Forest algorithm in Python and how is it used.

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How it Works?
1. This is a 5 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work
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 be working on a real time project for which we will provide you a Grade and a Verifiable Certificate!

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

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.
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Got a question on the topic? Please share it in the comment section below and our experts will answer it for you.

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El Deep Learning ha cambiado el mundo en sólo una década y hoy os contaré cómo esta rama de la informática podría seguir evolucionando. Y lo haremos desde el comienzo, con las redes neuronales más sencillas hasta Google Gemini, la futura promesa de Google DeepMind, pasando eso sí por los enormes modelos fundacionales como ChatGPT. ¡Bienvenidos a la nueva temporada de DotCSV!

📹 EDICIÓN: Carlos Santana y Diego Gonzalez (Diocho)

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00:00:00 Introduction
00:01:58 What is Prompt Engineering?
00:16:11 What are Large Language Models?
00:37:10 LLM vs SLM
00:42:52 What is Generative AI?
00:57:02 Prompt Engineering For Code Generation
01:06:43 GitHub Copilot Tutorial
01:23:51 Building a Chatbot with Prompt Engineering
01:39:20 What is LangChain?
01:56:35 What is RAG?
02:19:32 LLMOps: The Future of AI Development
02:25:55 What is Agentic AI?
02:36:47 Agentic AI vs Generative AI
02:57:58 What Is Multimodal AI?
03:14:58 Prompt Engineering Roadmap
03:22:38 Top 15 Popular Gen AI Tools You Must Know
03:40:38 Generative AI Interview Questions

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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.

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This "Data engineer roadmap" Video By Simplilearn is a guide that outlines the essential skills and knowledge needed to become a successful data engineer. This tutorial covers a range of topics, starting with core concepts such as databases, SQL, and data modeling. From there, it progresses to data integration and ETL processes, including batch processing and real-time data pipelines. The roadmap also includes learning about distributed systems and technologies like Apache Hadoop and Apache Spark, as well as cloud platforms like AWS, Azure, and GCP. Overall, the data engineer roadmap provides a comprehensive path for acquiring the necessary expertise in data engineering. By following this roadmap, aspiring or experienced data engineers can develop the skills needed to design, construct, and maintain data infrastructure and systems, and contribute to building robust and scalable data solutions.

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In this What Are Transformers In Artificial Intelligence tutorial, we'll delve into the world of Transformers in AI. We'll unravel the intricate workings of these powerful models, from their foundational architecture to their applications in natural language processing, computer vision, and beyond. By the end, you'll grasp not only how Transformers operate but also why they've become a cornerstone in modern AI research and development. So, buckle up as we embark on this enlightening exploration through the realm of Transformers in AI.

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This AI ML course is designed to enhance your career in AI and ML by demystifying concepts like machine learning, deep learning, NLP, computer vision, reinforcement learning, and more. You'll also have access to 4 live sessions, led by industry experts, covering the latest advancements in AI such as generative modeling, ChatGPT, OpenAI, and chatbots.

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A complete guide to the mathematics behind neural networks and backpropagation.

In this lecture, I aim to explain the mathematical phenomena, a combination of linear algebra and optimization, that underlie the most important algorithm in data science today: the feed forward neural network.

Through a plethora of examples, geometrical intuitions, and not-too-tedious proofs, I will guide you from understanding how backpropagation works in single neurons to entire networks, and why we need backpropagation anyways.

It's a long lecture, so I encourage you to segment out your learning time - get a notebook and take some notes, and see if you can prove the theorems yourself.

As for me: I'm Adam Dhalla, a high school student from Vancouver, BC. I'm interested in how we can use algorithms from computer science to gain intuition about natural systems and environments.

My website: adamdhalla.com
I write here a lot: adamdhalla.medium.com
Contact me: [email protected]

Two good sources I recommend to supplement this lecture:

Terence Parr and Jeremy Howard's The Matrix Calculus You Need for Deep Learning: https://arxiv.org/abs/1802.01528

Michael Nielsen's Online Book Neural Networks and Deep Learning, specifically the chapter on backpropagation http://neuralnetworksanddeeple....arning.com/chap2.htm

ERRATA----
I'm pretty sure the Jacobians part plays twice - skip it when you feel like stuff is repeating, and stop when you get to the part about the "Scalar Chain Rule" (00:24:00).

And, here are the timestamps for each chapter mentioned in the syllabus present at the beginning of the course.

PART I - Introduction
--------------------------------------------------------------
00:00:52 1.1 Prerequisites
00:02:47 1.2 Agenda
00:04:59 1.3 Notation
00:07:00 1.4 Big Picture
00:10:34 1.5 Matrix Calculus Review
00:10:34 1.5.1 Gradients
00:14:10 1.5.2 Jacobians
00:24:00 1.5.3 New Way of Seeing the Scalar Chain Rule
00:27:12 1.5.4 Jacobian Chain Rule

PART II - Forward Propagation
--------------------------------------------------------------
00:37:21 2.1 The Neuron Function
00:44:36 2.2 Weight and Bias Indexing
00:50:57 2.3 A Layer of Neurons

PART III - Derivatives of Neural Networks and Gradient Descent
--------------------------------------------------------------
01:10:36 3.1 Motivation & Cost Function
01:15:17 3.2 Differentiating a Neuron's Operations
01:15:20 3.2.1 Derivative of a Binary Elementwise Function
01:31:50 3.2.2 Derivative of a Hadamard Product
01:37:20 3.2.3 Derivative of a Scalar Expansion
01:47:47 3.2.4 Derivative of a Sum
01:54:44 3.3 Derivative of a Neuron's Activation
02:10:37 3.4 Derivative of the Cost for a Simple Network (w.r.t weights)
02:33:14 3.5 Understanding the Derivative of the Cost (w.r.t weights)
02:45:38 3.6 Differentiating w.r.t the Bias
02:56:54 3.7 Gradient Descent Intuition
03:08:55 3.8 Gradient Descent Algorithm and SGD
03:25:02 3.9 Finding Derivatives of an Entire Layer (and why it doesn't work well)

PART IV - Backpropagation
--------------------------------------------------------------
03:32:47 4.1 The Error of a Node
03:39:09 4.2 The Four Equations of Backpropagation
03:39:12 4.2.1 Equation 1: The Error of the last Layer
03:46:41 4.2.2 Equation 2: The Error of any layer
04:03:23 4.2.3 Equation 3: The Derivative of the Cost w.r.t any bias
04:10:55 4.2.4 Equation 4: The Derivative of the Cost w.r.t any weight
04:18:25 4.2.5 Vectorizing Equation 4
04:35:24 4.3 Tying Part III and Part IV together
04:44:18 4.4 The Backpropagation Algorithm
04:58:03 4.5 Looking Forward

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00:00:00 - Introduction to Artificial Intelligence Course
00:03:29 - What is Artificial Intelligence
00:07:17 - Why Artificial Intelligence
00:57:05 - Machine Learning Types
01:07:35 - Introduction to Deep Learning
02:45:16 - Benefits of Using Artificial Neural Network
02:50:26 - Deep Learning Frameworks
03:02:57 - Data Handling with NumPy
03:20:28 - Introduction to TensorFlow
05:23:00 - Understanding Epoch
05:50:38 - Introduction to Keras
06:43:35 - Predefined Neural Network Layers
08:43:11 - Problem With a Fully Connected Network
08:52:56 - Convolutional Neural Network
10:13:25 - Rectified Linear Units
10:45:45 - Artificial Intelligence Interview Questions

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✅ Programming Essentials For Artificial Intelligence
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✅ Machine Learning
✅ Foundations of Deep Learning
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This Edureka video on R vs Python provides you with a short and crisp description of the top two languages used in Data Science and Data Analytics i.e. Python and R (Blog:http://bit.ly/2ClaowR). You will also see the head-to-head comparison between the two on various parameters and learn why one is preferred over the other in certain aspects.

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

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Q. Why Python programming is important?
Python is a highly popular object-oriented language that is fast to learn and easy to deploy. It can run on various systems like Windows, Linux, and Mac; this makes it highly coveted for the Data Analytics domain. Python’s design and libraries provide 10 times productivity compared to C, C++, or Java. A Senior Python Developer in the United States can earn $102,000 – Indeed

Q. Why should you opt for a Python career?
If you want to fast-track your career then you should strongly consider Python. The reason for this is that it is one of the fastest-growing and widely used programming languages. There is a huge demand for Python programmers. The salaries for Python programmers are very good. There is a huge growth opportunity in this domain as well. Hence this Intellipaat Python programming session is your stepping stone to a successful career!
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🔥 Post Graduate Diploma in Artificial Intelligence by E&ICT Academy
NIT Warangal: https://www.edureka.co/executi....ve-programs/machine-
This video on the Sentiment Analysis in Python is a quick guide for the one who is getting started with Sentiment Analysis.
Second Part: https://youtu.be/27P268Q7pE0

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----------𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐏𝐲𝐭𝐡𝐨𝐧 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠𝐬-----------

🔵Python Programming Certification: http://bit.ly/37rEsnA
🔵Python Certification Training for Data Science: http://bit.ly/2Gj6fux

----------𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐌𝐚𝐬𝐭𝐞𝐫𝐬 𝐏𝐫𝐨𝐠𝐫𝐚𝐦----------

🔵Data Scientist Masters Program: http://bit.ly/2t1snGM
🔵Machine Learning Engineer Masters Program: https://bit.ly/3Hi1sXN

-----------𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐔𝐧𝐢𝐯𝐞𝐫𝐬𝐢𝐭𝐲 𝐏𝐫𝐨𝐠𝐫𝐚𝐦----------

🌕Post Graduate Diploma in Artificial Intelligence Course offered by E&ICT Academy
NIT Warangal: https://bit.ly/3qdRRdw

How it Works?

1. This is a 5 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work
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 be working on a real time project for which we will provide you a Grade and a Verifiable Certificate!

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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 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.

After completing this Machine Learning Certification Training using Python, you should be able to:
Gain insight into the 'Roles' played by a Machine Learning Engineer
Automate data analysis using python
Describe Machine Learning
Work with real-time data
Learn tools and techniques for predictive modeling
Discuss Machine Learning algorithms and their implementation
Validate Machine Learning algorithms
Explain Time Series and it’s related concepts
Gain expertise to handle business in future, living the present

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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.

Got a question on the topic? Please share it in the comment section below and our experts will answer it for you.
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