🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐀𝐖𝐒 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐂𝐨𝐮𝐫𝐬𝐞 (Use code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎") : https://www.edureka.co/aws-certification-training
This "Cloud Computing Tutorial for Beginners" video will give you an introduction to the cloud computing world. We will also look at why cloud computing is better along with the advantages of cloud computing. Later, we will look at the types of cloud computing models, and how the lifecycle for developing a cloud based solution occurs. At the very end, we have a demo in which we will use AWS services to help a normal business transition to cloud. This video is ideal for those who want to become an AWS Certified Solutions Architect.

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

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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.

🔥Edureka's Data Science Training: https://www.edureka.co/data-sc....ience-python-certifi
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 session:
00:00:00 Introduction
00:00:25 Agenda
00:01:35 What is Machine Learning
00:13:47 Introduction to Classification
00:17:28 Classification Algorithms
00:18:30 What is Naïve Bayes
00:31:36 Mathematical Working of Naïve Bayes
00:39:24 Example

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🔹Blog Series: https://bit.ly/2PX5lIp

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-------------Edureka Online Training and Certification-----------

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🟣 Selenium Online Training: https://bit.ly/3dIrh43
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-------Edureka Masters Programs----------

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🟣RPA Developer Masters Program: https://bit.ly/3eHwPNf

-----------Edureka PGP Courses----------

🔵Artificial and Machine Learning PGP: https://bit.ly/2Ziy7b1

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#edureka #datascienceedureka #SupportVectorMachine #SVM #DataScience #MachineLearning #MachineLearningTraining #learnMachineLearning #withme

----------------------------------------
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).

( ** Deep Learning Training: https://goo.gl/4it6DE ** )
This Edureka PyTorch Tutorial video (Blog: https://goo.gl/4zxMfU) will help you in understanding various important basics of PyTorch. It also includes a use-case in which we will create an image classifier that will predict the accuracy of an image data-set using PyTorch.

Below are the topics covered in this tutorial:

1. What is Deep Learning?
2. What are Neural Networks?
3. Libraries available in Python
4. What is PyTorch?
5. Use-Case of PyTorch
6. Summary

Check our complete Deep Learning playlist: https://goo.gl/cck4hE

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#PyTorchTutorial #Python #Edureka

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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.

Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world.

For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free).

🔥AI Engineer Specialist: https://l.linklyhq.com/l/1yhn3
🔥 Purdue Post Graduate Program In AI And Machine Learning: https://www.simplilearn.com/pgp-ai-machine-learning-certification-training-course?utm_campaign=MachineLearning-6dEUTmoXz0w&utm_medium=Descriptionff&utm_source=youtube
🔥Professional Certificate Course In AI And Machine Learning by IIT Kanpur (India Only): https://www.simplilearn.com/iitk-professional-certificate-course-ai-machine-learning?utm_campaign=23AugustTubebuddyExpPCPAIandML&utm_medium=DescriptionFF&utm_source=youtube
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In this video on Machine Learning with R, you will understand machine learning, applications of machine learning, and learn R programming. In this machine learning full course, you will also understand machine learning algorithms, such as linear regression, logistic regression, decision tree, and random forest to name a few.

Dataset Link - https://drive.google.com/drive..../folders/1DeoJO7NdoZ

The topics covered in this Machine Learning Tutorial video are:
00:00:00 Introduction
00:00:57 Machine Learning Basics
00:31:50 Machine Learning Applications
00:13:41 R programming
05:51:08 Linear Regression in R
06:20:14 Logistic Regression in R
06:38:20 Decision Tree in R
07:22:28 Random Forest in R
07:47:36 Support Vector Machine in R
08:24:41 Hierarchical Clustering in R
08:48:33 Time Series Analysis in R

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

⏩ Check out the Machine Learning tutorial videos: https://bit.ly/3fFR4f4

#MachineLearningWithR #MachineLearningFullCourse #MachineLearningTutorial #MachineLearning #SimplilearnMachineLearning #MachineLearningCourse

Machine learning is a core sub-area of artificial intelligence; it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. So, put simply, the iterative aspect of machine learning is the ability to adapt to new data independently. This is possible as programs learn from previous computations and use “pattern recognition” to produce reliable results.

R is a widely popular programming language in the field of machine learning. R is an open-source programming language used for statistical computing. It is one of the most popular programming languages today. R was inspired by S+, which is similar to the S programming language. R has various data structures and operators. It can be integrated with other programming languages like C, C++, Java, and Python.


➡️ About Post Graduate Program In AI And Machine Learning

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.

✅ Key Features

- Post Graduate Program certificate and Alumni Association membership
- Exclusive hackathons and Ask me Anything sessions by IBM
- 3 Capstones and 25+ Projects with industry data sets from Twitter, Uber, Mercedes Benz, and many more
- Master Classes delivered by Purdue faculty and IBM experts
- Simplilearn's JobAssist helps you get noticed by top hiring companies
- Gain access to 4 live online sessions on latest AI trends such as ChatGPT, generative AI, explainable AI, and more
- Learn about the applications of ChatGPT, OpenAI, Dall-E, Midjourney & other prominent tools

✅ Skills Covered

- ChatGPT
- Generative AI
- Explainable AI
- Generative Modeling
- Statistics
- Python
- Supervised Learning
- Unsupervised Learning
- NLP
- Neural Networks
- Computer Vision
- And Many More…

👉 Learn More At: 🔥 Purdue Post Graduate Program In AI And Machine Learning: https://www.simplilearn.com/pgp-ai-machine-learning-certification-training-course?utm_campaign=MachineLearning-6dEUTmoXz0w&utm_medium=Description&utm_source=youtube


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

🔥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).

Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
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🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐀𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞 (𝐔𝐬𝐞 𝐂𝐨𝐝𝐞: 𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎) : https://www.edureka.co/advance....d-artificial-intelli
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.
00:00:00 Introduction
00:00:36 Agenda
00:00:47 Introduction to NLP
00:01:46 Ways to Process Text Data
00:02:57 Recurrent Neural Networks
00:22:02 Long Short-term Memory
00:51:46 LSTM Use Cases
00:53:45 Real Time Applications of LSTM

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🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬

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🌕 CEHv12 Online Training: http://bit.ly/3Vhq8Hj
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📌𝐅𝐚𝐜𝐞𝐛𝐨𝐨𝐤: https://www.facebook.com/edurekaIN/
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📌𝐂𝐚𝐬𝐭𝐛𝐨𝐱: https://castbox.fm/networks/505?country=IN
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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.

This deep learning course is designed to take you from beginner to proficient in deep learning. You will learn the fundamental concepts, architectures, and applications of deep learning in a clear and practical way. So get ready to build, train, and deploy models that can tackle real-world problems across various industries.

Course created by @AyushSinghSh

GitHub: https://github.com/ayush714/co....re-deep-learning-cou

⭐️ Contents ⭐️
0:00:00 Intro
0:03:07 Getting started
0:05:07 Vectors
0:21:51 Operation on vectors
0:38:52 Matrices
0:52:02 Operation on Matrices
0:52:27 Matrix Scalar Multiplication
0:55:47 Addition of Matrices
0:59:27 Properties of Matrix addition
1:03:07 Matrix Multiplication
1:08:02 Properties of Matrix Multiplication
1:18:32 Linear Combination Concept
1:36:20 Span
1:50:57 Linear Transformation
2:05:30 Transpose
2:14:02 Properties of Transpose
2:19:52 Dot Product
2:25:22 Geometric Meaning of Dot Product
2:34:32 Types of Matrices
3:04:22 Determinant
3:11:17 Geometric Meaning of Determinant
3:15:42 Calculating Determinant
3:23:37 Properties of Determinant
3:27:22 Rule of Sarus
3:48:42 Minor
3:56:49 Cofactor of a Matrix
4:00:42 Steps to calculate Cofactor of a Matrix
4:03:17 Adjoint of a Matrix
4:18:47 Trace of a Matrix
4:17:22 Properties of Trace
4:38:17 System of Equations
5:03:07 Example
5:17:42 Determinant
5:57:47 Single Variable Calculus
6:02:48 What is Calculus?
6:11:07 Ideas in Calculus
6:11:33 Differentiation
6:18:38 Integration
6:22:07 Precalculus Functions
6:43:52 Single Variable Calculus (Trigonometry Review)
6:45:02 Trigonometry functions
7:12:02 Unit Circle
7:24:32 Limit Concept
7:51:47 Definition of a limit
7:53:27 Continuity
8:00:17 Evaluating Limits
8:17:12 Sandwich Theorem
8:21:12 Differentiation
8:45:42 Differentiation as rate of Change
8:52:37 Differentiation in terms of Limit
9:04:51 Example
9:09:54 Important Differentiation Rules
9:53:12 Rule Chain Rule
10:17:27 What is Deep Learning
10:18:27 What is Machine Learning
10:36:37 Definition of Deep Learning
10:43:07 Applications
10:47:19 Introduction to Neural Networks
10:51:17 Artificial Neural Networks
11:08:31 The Perceptron
11:19:57 Linear Neural Network
11:21:32 Intuition Behind Activation function and Backpropagation Algorithm



🎉 Thanks to our Champion and Sponsor supporters:
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MIT Introduction to Deep Learning 6.S191: Lecture 2
Recurrent Neural Networks
Lecturer: Ava Amini
** New 2024 Edition **

For all lectures, slides, and lab materials: http://introtodeeplearning.com

Lecture Outline
0:00​ - Introduction
3:42​ - Sequence modeling
5:30​ - Neurons with recurrence
12:20 - Recurrent neural networks
14:08 - RNN intuition
17:14​ - Unfolding RNNs
19:54 - RNNs from scratch
22:41 - Design criteria for sequential modeling
24:24 - Word prediction example
31:50​ - Backpropagation through time
33:40 - Gradient issues
37:15​ - Long short term memory (LSTM)
40:00​ - RNN applications
44:00- Attention fundamentals
46:46 - Intuition of attention
49:13 - Attention and search relationship
51:22 - Learning attention with neural networks
57:45 - Scaling attention and applications
1:00:08 - Summary
Subscribe to stay up to date with new deep learning lectures at MIT, or follow us @MITDeepLearning on Twitter and Instagram to stay fully-connected!!

🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐰𝐢𝐭𝐡 𝐏𝐲𝐭𝐡𝐨𝐧 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞 : https://www.edureka.co/data-sc....ience-python-certifi (𝐔𝐬𝐞 𝐂𝐨𝐝𝐞: 𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎)
This Edureka Data Science course video (Data Science Blog Series: https://goo.gl/yGjZfs) will take you through the need of data science, what is data science, data science use cases for business, BI vs data science, data analytics tools, data science lifecycle along with a demo. This Data Science tutorial video is ideal for beginners to learn data science and machine learning basics. You can read the blog here: https://goo.gl/lYb5Lb

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📌𝐓𝐞𝐥𝐞𝐠𝐫𝐚𝐦: https://t.me/edurekaupdates
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📌𝐂𝐚𝐬𝐭𝐛𝐨𝐱: https://castbox.fm/networks/505?country=IN
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📌𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲: https://www.edureka.co/community/



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🔥Edureka Business Intelligence Masters Training: https://www.edureka.co/masters....-program/business-in
This Edureka Business Intelligence Full Course video will help you understand and learn the fundamentals concepts of Tableau and Power BI. This Business Intelligence training video is ideal for both beginners as well as professionals who want to master the fundamentals of Tableau and Power BI. Topics covered
what is BI? - 00:02:44
Quiz - 06:54:28
How to Become a Business Intelligence Analyst? - 00:07:23
Data Warehousing and BI - 00:22:30
Quiz - 01:50:29
Data Warehouse Concepts - 01:50:51
Excel for Beginners - 02:28:25
Quiz - 02:44:23
Excel Formulas and Functions - 02:44:39
Quiz - 03:12:40
VLOOKUP in Excel - 03:12:59
Quiz - 03:31:53
Excel Pivot Tables - 03:32:07
Quiz - 03:47:42
Advanced Excel - 03:48:05
Quiz - 04:18:21
Power BI for Beginners - 04:18:44
How to Build Power BI Dashboards - 05:19:34
Power Query - 05:45:16
Data Visualization Using Power BI Desktop - 06:16:21
Introduction to Tableau - 06:30:48
Getting Started with Tableau - 06:34:09
Quiz - 06:44:09
Tableau vs QlikView - 06:44:30
What is Tableau Server? - 06:57:41
Tableau Online - 07:07:57
Top 5 BI Tools - 07:58:17
Power BI vs MSBI - 08:04:38
Data Warehouse Interview Questions and Answers - 08:13:57
BI Analyst Interview Questions & Answers - 09:33:37

🔴 𝐋𝐞𝐚𝐫𝐧 𝐓𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐅𝐨𝐫 𝐅𝐫𝐞𝐞! 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐘𝐨𝐮𝐓𝐮𝐛𝐞 𝐂𝐡𝐚𝐧𝐧𝐞𝐥: https://edrk.in/DKQQ4Py
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⏩ NEW Top 10 Technologies To Learn In 2024 - https://www.youtube.com/watch?v=vaLXPv0ewHU

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

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📌𝐓𝐞𝐥𝐞𝐠𝐫𝐚𝐦: https://t.me/edurekaupdates
📌𝐓𝐰𝐢𝐭𝐭𝐞𝐫: https://twitter.com/edurekain
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📌𝐅𝐚𝐜𝐞𝐛𝐨𝐨𝐤: https://www.facebook.com/edurekaIN/
📌𝐒𝐥𝐢𝐝𝐞𝐒𝐡𝐚𝐫𝐞: https://www.slideshare.net/EdurekaIN
📌𝐂𝐚𝐬𝐭𝐛𝐨𝐱: https://castbox.fm/networks/505?country=IN
📌𝐌𝐞𝐞𝐭𝐮𝐩: https://www.meetup.com/edureka/
📌𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲: https://www.edureka.co/community/


Please write back to us at [email protected] or call us at IND: 9606058406 / US: +18338555775 (toll-free) for more information.

𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐏𝐲𝐭𝐡𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞 (Use Code𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎"): https://www.edureka.co/python-....programming-certific
This "Python Full Course" video provides a comprehensive overview of Python programming. This Python tutorial for beginners has foundational concepts such as variables and data types to advanced topics like functions, modules, and object-oriented programming. This Python for Beginners video offers a step-by-step approach with explanations, examples, and interactive exercises. It's the perfect starting point for individuals eager to dive into Python programming from scratch.

Below are the topics covered in this Python Full Course :
00:00:00 Introduction
00:01:50 Python Basics
00:03:56 Python Installation
00:09:43 Quiz Question
00:10:10 Basics of Programming
01:18:53 Quiz Question
01:19:22 Comments in Python
01:28:42 Quiz question
01:28:58 Operators in Python
01:52:03 Quiz Question
01:52:18 Tuple in Python
02:05:19 Quiz Question
02:05:37 Sets in Python
02:19:45 Quiz Question
02:20:03 Dictionary in Python
02:24:36 Quiz Question
02:34:54 Loops in Python
03:06:19 Quiz Question
03:06:39 Python Libraries - Numpy
03:20:51 Quiz Question
03:38:27 Python Pandas
04:40:47 Quiz Question
04:41:13 Python Matplotlib
05:08:14 Quiz Question
05:08:46 Python Functions
05:45:37 Quiz Question
05:46:06 Python Lamba Functions
06:05:52 Quiz Question
06:06:13 Arrays in Python
06:31:37 Quiz Question
06:32:01 Inheritance in Python
06:45:59 Quiz Question
06:46:20 Exception Handling
07:05:07 Quiz Question
07:05:30 Request in Python
07:20:06 Quiz Question
07:20:34 Date and time in Python
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🔍 In this video, we unravel the layers of AI, Machine Learning, Deep Learning, and their applications in tools like #ChatGPT and Google #Bard

We first go through how AI is a broad field of study that encompasses #MachineLearning as a sub-field.

We then break down Machine Learning into supervised and unsupervised models, using real-world examples to illustrate their functions and differences.

We move deeper into Deep Learning: Learn about artificial neural networks and the power of semi-supervised learning in applications like fraud detection in banking.

Then we delve into Generative AI, differentiating it from discriminative models and demonstrating its capabilities in creating new, innovative outputs.

Finally we walk through Large Language Models (LLMs) and uncover the significance of LLMs in AI, their pre-training processes, and their customization for specific industry applications

*TIMESTAMPS*
00:00 Google’s AI Course in 10 Minutes
00:38 What is Artificial Intelligence?
01:27 What is Machine Learning?
03:28 What is Deep Learning?
05:15 What is Generative AI?
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This video on PMP Full Course by simplilearn will help you learn the basics of PMP. The Project Management Professional Full Course is a complete guide to Project Management fundamentals and certification. The Full course Starts with What is Project Management and Agile Project Management, and get a deep dive into the PMP Certification with detailed exam guides and PMP 6th Edition Training. Followed by a Project Management Roadmap for beginners and learn essential skills like Prioritization Techniques and Product Lifecycle Management. Then we will proceed with key concepts such as Product Market Fit and Project Management in Software Engineering. proceeding ahead, we will prepare for career success with tips on How to Become a Project Manager and answering Product Management Interview Questions.

Following are the topics covered in PMP FC

00:00:00 Introduction to PMP FC
00:21:28 What is project management
00:29:37 What is agile project management
00:30:34 Detailed Information on PMP Certification
00:52:14 Project Management Roadmap for beginners
01:02:20 How to be Project Manager
06:12:27 Project Management
06:14:38 Prioritization Techniques
06:34:26 Product Lifecycle Management
06:54:02 What is product market fit
08:11:25 PMP 6th edition training
08:19:01 PMP Exam Guide
08:32:38 Projct Management in Software Engineering
08:34:07 How to answerProduct Management Interview Questions
09:09:03 Build Website with AI

Q) What is a roadmap in product management?
Answer: A product roadmap is a shared source of truth that outlines the vision, direction, priorities, and progress of a product over time. It's a plan of action that aligns the organization around short and long-term goals for the product or project, and how they will be achieved.

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The KL divergence of distributions P and Q is a measure of how similar P and Q are.
However, the KL Divergence of P and Q is not the same as the KL Divergence of Q and P.
Why?
Learn the intuition behind this in this friendly video.

More about the KL Divergence formula:
https://www.youtube.com/watch?v=sjgZxuCm_8Q

Correction: At 30:42 I write "X = Y". They're not equal, what I meant to say is "X and Y are identically distributed".

The variance is a measure of how spread out a distribution is. In order to estimate the variance, one takes a sample of n points from the distribution, and calculate the average square deviation from the mean.
However, this doesn't give a good estimate of the variance of the distribution. The best estimate, however, is obtained when dividing by n-1 instead of n.
WHY!?!?!?!?!?!?!?
In this video, we dig deeper into why the variance calculation should be divided by n-1 instead of by n. For this, we use an alternate definition of the variance, which doesn't use the mean in its calculation.

*[0:00] Introduction and Bessel's Correction*
- Introducing Bessel's Correction and why we divide by \( n-1 \) instead of \( n \) to estimate variance.

*[0:12] Introduction to Variance Calculation*
- Explaining the premise of calculating variance and introducing the concept of estimating variance using a sample instead of the entire population.

*[1:01] Definition of Variance*
- Defining variance as a measure of how much values deviate from the mean and outlining the basic steps of variance calculation.

*[1:52] Introduction to Bessel's Correction*
- Discussing why we divide by \( n-1 \) when calculating variance and introducing Bessel's Correction.

*[2:35] Challenges of Bessel's Correction*
- Sharing personal challenges in understanding the rationale behind Bessel's Correction and discussing my research process on the topic.

*[3:20] Alternative Definition of Variance*
- Presenting an alternative definition of variance to aid in understanding Bessel's Correction and expressing curiosity about its presence in the literature.

*[4:45] Quick Recap of Mean and Variance*
- Briefly revisiting the concepts of mean and variance, demonstrating how they are calculated with examples, and explaining how variance reflects different distributions.

*[7:05] Sample Mean and Variance Estimation*
- Explaining the challenges of estimating the mean and variance of a distribution using a sample and discussing why sample variance is not a good estimate.

*[8:49] Bessel's Correction and Why \( n-1 \) is Used*
- Explaining how Bessel's Correction provides a better estimate of variance and why we divide by \( n-1 \) instead of \( n \). Emphasizing the importance of making a correct variance estimate.

*[10:51] Why Better Estimation Matters?*
- Discussing why the original estimate is poor and why making a better estimate is crucial. Explaining the significance of sample mean as a good estimate.

*[13:02] Issues with Variance Estimation*
- Illustrating the problems with variance estimation and demonstrating with examples why using the correct mean is essential for accurate estimates. Explaining the accuracy of estimates made using \( n-1 \).

*[15:04] Introduction to Correcting the Estimate*
- Discussing the underestimated variance and the need for correction in estimation.

*[15:57] Adjusting the Variance Formula*
- Explaining the adjustment in the variance formula by changing the denominator from \( n \) to \( n - 1 \).

*[16:22] Calculation Illustration*
- Demonstrating the calculation process of variance with the adjusted formula using examples.

*[16:57] Better Estimate with Bessel's Correction*
- Discussing how the corrected estimate provides a more accurate variance estimation.

*[18:24] New Method for Variance Calculation*
- Introducing a new method for calculating variance without explicitly calculating the mean.

*[20:06] Understanding the Relation between Variance and Variance*
- Explaining the relationship between variance and variance, and how they are related mathematically.

*[21:52] Demonstrating a Bad Calculation*
- Illustrating a flawed method for calculating variance and explaining the need for correction.

*[23:37] The Role of Bessel's Correction*
- Explaining why removing unnecessary zeros in variance calculation leads to better estimates, equivalent to Bessel's Correction.

*[25:08] Summary of Estimation Methods*
- Summarizing the difference between the flawed and corrected estimation methods for variance.

*[26:02] Importance of Bessel's Correction*
- Emphasizing the significance of Bessel's Correction for accurate variance estimation, especially with smaller sample sizes.

*[30:19] Mathematical Proof of Variance Relationship*
- Providing two proofs of the relationship between variance and variance, highlighting their equivalence.

*[35:24] Acknowledgments and Conclusion*

Thanks @mkan543 for the summary!

A video about decision trees, and how to train them on a simple example.

Accompanying blog post: https://medium.com/@luis.serra....no/splitting-data-by

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

Helper videos:
- Gini index: https://www.youtube.com/watch?v=u4IxOk2ijSs
- Entropy and information gain: https://www.youtube.com/watch?v=9r7FIXEAGvs
- Machine learning error and metrics: https://www.youtube.com/watch?v=aDW44NPhNw0

Grokking Machine Learning book:
www.manning.com/books/grokking-machine-learning
40% discount code: serranoyt