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This Edureka SQL Full Course video will cover all the topics of Structured Query Language (SQL) starting from scratch. This SQL tutorial for beginners is great for beginners who want to learn SQL and for professionals who want to brush up on their SQL skills. The following topics are covered in this SQL Full Course Tutorial:
00:00:00 Introduction
00:02:23 Agenda
00:02:35 What is SQL
00:16:32 Data & Database
00:23:58 Basic SQL Queries
00:50:51 Normalization in SQL
01:01:44 Triggers in SQL
01:11:23 Advantages & Disadvantages of Triggers
01:12:33 Joins in SQL
01:33:28 Functions in SQL
02:18:19 Stored procedure
02:43:02 User-Defined Functions
03:02:13 SQL Vs MySQL
03:07:36 SQL Vs NoSQL
03:39:41 SQL Interview Question & Answers
04:31:17 SQL For DataScience
05:06:27 PostgreSQL
05:17:00 SQL Command Catogories
05:24:31 ER Diagram
05:43:34 Keys in Database
05:46:53 Constraints in Database
05:53:44 Normalization
05:59:26 DML COmmands
06:18:36 Operators
06:47:54 Joins
06:51:52 Views
07:06:19 DCL Commands
07:15:23 SQL Server
07:21:28 Features of SQL Server
07:36:50 DDL Commands
08:30:07 Operators
08:55:50 Exception handling
08:59:55 SQL Server Interview Question & Answer

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🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬

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🌕 Professional Certificate Program in DevOps with Purdue University: https://bit.ly/3Ov52lT

🔵 Advanced Certificate Program in Data Science with E&ICT Academy, IIT Guwahati: http://bit.ly/3V7ffrh

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📢📢 𝐓𝐨𝐩 𝟏𝟎 𝐓𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐭𝐨 𝐋𝐞𝐚𝐫𝐧 𝐢𝐧 2023 𝐒𝐞𝐫𝐢𝐞𝐬 📢📢
⏩ NEW Top 10 Technologies To Learn In 2023 - https://youtu.be/udD_GQVDt5g

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



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🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐀𝐖𝐒 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐂𝐨𝐮𝐫𝐬𝐞: https://www.edureka.co/aws-certification-training (Use code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎")
This Edureka video on AWS Solutions Architect Interview Questions will discuss some of the most frequently asked questions based on AWS during interviews. It will help you understand the different topics you need to know about.
Topics Covered:
00:00:00 Introduction
00:00:56 Basic level
00:13:12 Intermediate level questions
00:30:00 Advance level

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📝Feel free to share your list of the top 10 trending technologies to learn in 2023 in the comments below.📝

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🔵 DevOps Engineer Masters Program: http://bit.ly/3Oud9PC
🌕 Cloud Architect Masters Program: http://bit.ly/3OvueZy
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🌕 Professional Certificate Program in DevOps with Purdue University: https://bit.ly/3Ov52lT

🔵 Advanced Certificate Program in Data Science with E&ICT Academy, IIT Guwahati: http://bit.ly/3V7ffrh

🌕 Artificial and Machine Learning PGD with E&ICT Academy
NIT Warangal: http://bit.ly/3OuZ3xs

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⏩ NEW Top 10 Technologies To Learn In 2023 - https://youtu.be/udD_GQVDt5g

📌𝐓𝐞𝐥𝐞𝐠𝐫𝐚𝐦: https://t.me/edurekaupdates
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📌𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲: https://www.edureka.co/community/

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INTRODUCTION TO DEEP LEARNING
0:00:00 Welcome
0:05:31 What is a Neural Network
0:12:48 Supervised Learning with Neural Network
0:21:17 Why is Deep Learing taking off
0:31:38 About this course
0:34:06 Geoffrey Hinton Interview

NEURAL NETWORKS BASICS
1:14:28 Binary Classification
1:22:52 Logistic Regression
1:28:51 Logistic Regression Cost Function
1:37:03 Gradient Descent
1:48:26 Derivatives
1:55:37 More Derivative Example
2:06:04 Computation Graph
2:09:38 Derivatives With a Computation Graph
2:24:12 Logistic Regression Gradient Descent
2:30:54 Gradient Descent on m Examples
2:38:55 Vectorization
2:46:59 More Vectorization Examples
2:53:18 Vectorization Logistic Regression
3:00:50 Vectorization Logistic Regression Gradient Output
3:10:28 Broadcasting in PYthon
3:21:34 A Note on Python Numpy Vectors
3:28:23 Quick tour of jupyter iPython Notebooks
3:32:06 Explanation of Logistics Regression Cost Function (optional)
3:39:21 Pieter Abbeel Interview

SHALLOW NEURAL NETWORKS
3:55:25 Neural Network Overview
3:59:51 Neural Network Representation
4:05:05 computing a Neural Network's output
4:15:03 Vectorizing Across Multiple Examples
4:24:09 Explantion for Vectorized Implementation
4:31:46 Activation Functions
4:42:43 Why do you need Non-Linear Activation Functions
4:48:19 Derivatives of Activation Functions
4:56:16 Gradient Descent for Neural Networks
5:06:14 Backpropagation Intuition (Optional)
5:22:02 Random Initialization
5:30:00 Ian Goodfellow Interview

DEEP NEURAL NETWORKS
5:44:56 Deep L-layer Neural Network
5:50:47 Forward Propagation in a Deep Network
5:58:02 Getting your Matrix Dimentions Right
6:09:12 Why Deep Representations
6:19:46 Building Blocks of Deep Neural Networks
6:28:19 Forward and Backward propagation
6:38:49 Parameters vs Hyperparameters
6:46:06 What does this have to do with brain


By the end, you will be familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network’s architecture; and apply deep learning to your own applications.

⭐ Important Notes ⭐
⌨️ The creator of this course is Deeplearning.ai (Andrew Ng)

Note: If you have any copyright issue with the content used in our channel or you find something that belongs to you, before you claim it to Youtube, SEND US A MESSAGE and the respective content will be DELETED right away. Thanks for understanding.

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How is a beginner supposed to get started learning machine learning? I'm going to describe a free 3 month curriculum to help you go from beginner to well-versed in modern machine learning. Transformers, Diffusers, Large Language Models, and dozens of new ML Ops tools have been released the past few years! The field of ML has changed so much and there are so many new tools to learn. Using the help of AI services, we can learn a lot faster than usual. This learning plan is something I'd create for myself if I were to get started today, but I'm going to open source it for you guys. This curriculum will cover all the math concepts, machine learning, deep learning, ML Ops, and tons of cool projects to get you up to speed with the field as fast as possible. If anyone asks how to best get started with machine learning, direct them to this video!

Curriculum from this video:
https://github.com/llSourcell/LearnML

Subscribe! And like. And comment. That's what keeps me going.

Want more education? Connect with me here:
Twitter: https://twitter.com/sirajraval
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Curriculum Links
-----------------------------------------------------
Month 1: Machine Learning

Week 1: Python Fundamentals - https://allendowney.github.io/DSIRP/index.html

Week 2: Math of Machine Learning -
http://www.xaktly.com/XMathMain.html

Week 3: Data Analysis -
https://www.kaggle.com/learn

Week 4: Machine Learning Techniques -
https://github.com/ipython-books/cookbook-2nd

Month 2: Deep Learning

Week 1: Neural Networks
http://d2l.ai/

Week 2: Transformers
https://huggingface.co/course/chapter1/1

Week 3: Diffusers
https://www.fast.ai/posts/part2-2022-preview.html

Week 4: Deep Reinforcement Learning
https://simoninithomas.github.io/deep-rl-course/

Month 3: Machine Learning Operations

Week 1: Design -
https://madewithml.com/

Week 2: Development -
https://fullstackdeeplearning.com/course/2022/

Week 3: Production -
https://github.com/DataTalksClub/mlops-zoomcamp

Week 4: Data Engineering -
https://github.com/DataTalksCl....ub/data-engineering-

All in one place: the best AI course you’ve ever watched!

You can now enjoy all 6.5 hours of Google’s legendary AI course designed to enlighten AI beginners, grow technology leaders, inform better citizens, and amuse AI experts!

This video is the feast version. If you prefer to learn in bite-sized nibbles, you can find the individual chapters as short videos on the http://bit.ly/mf-ml playlist. The episode guide is in the top right hand corner of every video.

Don't forget to hit subscribe+notify! If you found this video useful or enjoyable, the best way to say thank you is by sharing it.

Looking for hands-on ML/AI tutorials? Here are some of my favorite 10 minute walkthroughs:

AutoML - https://console.cloud.google.c....om/?walkthrough_id=a
Vertex AI - https://bit.ly/kozvertex
AI notebooks - https://bit.ly/kozvertexnotebooks
ML for tabular data - https://bit.ly/kozvertextables
Text classification - https://bit.ly/kozvertextext
Image classification - https://bit.ly/kozverteximage
Video classification - https://bit.ly/kozvertexvideo

So how do you get started with machine learning and AI? What should you learn first? Well in this video I will be discussing the exact things you need to learn to get started with machine learning. I'll be talking about which language to learn, how much math you need and what ML algorithms to learn first.

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Tags:
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- Get started with Machine Learning
- Machine learning getting started
- Get started with AI

#AI #MachineLearning

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Many developers have concern about how and where to learn AI.

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In this video we will see
- How to learn Artificial intelligence for free
- Three things most important: petience , commmitment and time
- Prerequisite to learn Artificial intelligence
- Programming language
- Maths
- Time or duration to learn
- Efforts
- Where to learn from
- Some of the suggestions: Edx, Coursera, Udacity
- Courses on Artificial intelligence

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More Learning :

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JavaScript Tutorial for Beginners :- https://bit.ly/3NGR3ba
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Spring Framework with Maven Tutorial :- https://bit.ly/3LWByL3
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Blockchain Tutorial for Beginners :- https://bit.ly/3x33f0z
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NoSQL Tutorial for Beginners :- https://bit.ly/3m03Ycq
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Mysql Tutorial :- https://bit.ly/3M4C6OK

Data Structures using Java :- https://bit.ly/3t5HPNY
Data Structures using C :- https://bit.ly/3GBLoAE
Kotlin Tutorial:- https://bit.ly/3x1kOfV
Git Tutorial for Beginners:- https://bit.ly/3NMbmUw
C Programming Tutorial :- https://bit.ly/3x4bA45

You should take a look at the Machine Learning Crash Course from Google. You might just like it!
https://developers.google.com/....machine-learning/cra

Another video:
Should you really do 10000 steps a day? https://youtu.be/gY8dom3TwMY

3 Data Science Learning Platforms I would recommend

1. Data Quest - https://bit.ly/3yClqbZ (my favourite)
2 Codeacademy - https://www.pntra.com/t/SENKTE....5HS0pDSEpJT0pLQ0hHSU
3 365 Data Science - https://bit.ly/3hbUHdd

I have created 2 Python Courses you might like

1. Exploratory Data Analysis with Python and Pandas - https://bit.ly/2QXMpxJ
2. Complete Python Programmer Bootcamp - http://bit.ly/2OwUA09

(These contain affiliate links, which means I receive a percentage of any sales made. There is no additional cost for anybody clicking on them)


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Machine Learning is an absolutely fantastic skill to pick up. Here are some of my favorite Machine Learning courses that I have taken on Coursera.

LINKS:
Machine Learning by Andrew Ng - https://coursera.pxf.io/yRx0dD
Mathematics for Machine Learning - https://coursera.pxf.io/qnGJRn
IBM Applied AI Professional Certificate - https://coursera.pxf.io/jWjkEn
DeepLearning.AI TensorFlow Developer Professional Certificate - https://coursera.pxf.io/AoY9zR
Data Engineering, Big Data, and Machine Learning on GCP Specialization - https://coursera.pxf.io/VyjErR

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RESOURCES:

Coursera Courses:
Google Data Analyst Certification: https://coursera.pxf.io/5bBd62
Data Analysis with Python - https://coursera.pxf.io/BXY3Wy
IBM Data Analysis Specialization - https://coursera.pxf.io/AoYOdR
Tableau Data Visualization - https://coursera.pxf.io/MXYqaN

Udemy Courses:
Python for Data Analysis and Visualization- https://bit.ly/3hhX4LX
Statistics for Data Science - https://bit.ly/37jqDbq
SQL for Data Analysts (SSMS) - https://bit.ly/3fkqEij
Tableau A-Z - http://bit.ly/385lYvN

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*All opinions or statements in this video are my own and do not reflect the opinion of the company I work for or have ever worked for*

Reality behind data science jobs. Is machine learning really cool?

Do you want to learn technology from me? Check https://codebasics.io/ for my affordable video courses.

🌎 My Website For Video Courses: https://codebasics.io/?utm_source=description&utm_medium=yt&utm_campaign=description&utm_id=description

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❗❗ DISCLAIMER: All opinions expressed in this video are of my own and not that of my employers'.

In this course we implement the most popular Machine Learning algorithms from scratch using only Python and NumPy.

Get my Free NumPy Handbook:
https://www.python-engineer.com/numpybook

✅ Write cleaner code with Sourcery, instant refactoring suggestions in VS Code & PyCharm: https://sourcery.ai/?utm_source=youtube&utm_campaign=pythonengineer *

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Code:
https://github.com/patrickloeber/MLfromscratch

Timeline:
00:00​ - Introduction
00:56​ - 1 KNN
23:00​ - 2 Linear Regression
43:38​ - 3 Logistic Regression
1:00:50​ - 4 Regression Refactoring
1:08:25​ - 5 Naive Bayes
1:29:11​ - 6 Perceptron
1:47:01​ - 7 SVM
2:06:33​ - 8 Decision Tree Part 1
2:17:12​ - 9 Decision Tree Part 2
2:48:00​ - 10 Random Forest
3:01:22​ - 11 PCA
3:18:43​ - 12 K-Means
3:48:15​ - 13 AdaBoost
4:15:53​ - 14 LDA
4:38:10​ - 15 Load Data From CSV

You can find me here:
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#Python #MachineLearning

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METACENT $10,000 GIVEAWAY:
https://gleam.io/tGeMT/metacent-giveaway

Metacent links:
https://www.metacent.io

Machine Learning Roadmap 2022 Website:
https://bit.ly/LearnML2022

Join me on #100DaysOfML and follow along to learn machine learning!

Start from day 0: https://youtube.com/playlist?l....ist=PLVBat3Ko2nN9z2L

Machine Learning is one of the best skills that you can learn in 2022, especially if you are already in tech. This video will show you exactly how to get started and also I've created a dedicated website so that you guys can easily follow along! There are many stages involved when it comes to learning ML but it doesn't have to be daunting, so it really helps to have a plan. I've broken down the process into 6 different stages.

Stage 1: Math, Linear Algebra
Stage 2: Python
Stage 3: Basic Machine Learning
Stage 4: Advanced Machine Learning
Stage 5: Deep Learning
Stage 6: Projects

This entire process can take on average up to 6-12 months and this is an excellent way to learn while building a really strong foundation!

------------------------------------------------------------------------------
Discord Link for School Of Machine Learning:
------------------------------------------------------------------------------
https://discord.gg/ZzUwYDjFwm


-------------------------------------------------------------------------
LINKS:
--------------------------------------------------------------------------
🖊 Machine Learning Roadmap 2022:
https://bit.ly/LearnML2022

--------------------------------------------------------------------------
MORE VIDEOS:
--------------------------------------------------------------------------
📌I'm Starting My Machine Learning Company (Day 1)
https://youtu.be/lh_wyUrjS9k

📌Top Machine Learning Certifications For 2021
https://youtu.be/YhXzUZGKhIY

📌Why You Should NOT Learn Machine Learning!
https://youtu.be/reY50t2hbuM

📌How I Learnt Machine Learning In 6 Steps (3 months)
https://youtu.be/OuC3wgp1Fnw

📌How To Learn Machine Learning For Free
https://youtu.be/QNKYKzTGerA
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Ready to explore machine learning and artificial intelligence in python? This python machine learning and AI mega course contains 4 different series designed to teach you the ins and outs of ML and AI. It talks about fundamental ML algorithms, neural networks, creating AI chat bots and finally developing an AI that can play the game of Flappy Bird.

⭐️ Thanks to Kite for sponsoring this video! Download the best AI automcolplete for python programming for free: https://kite.com/download/?utm_medium=referral&utm_source=youtube&utm_campaign=techwithtim&utm_content=python-ml-ai-mega-course

⭐ RESOURCES ⭐
IMPORTANT: The text-based guides will have download links for files or datasets needed.

1⃣ Machine Learning for Beginners
💻 Text-Based Guide: https://techwithtim.net/tutori....als/machine-learning
💾 UCI Student Data Set: https://archive.ics.uci.edu/ml..../datasets/Student+Pe
💾 UCI Car Evaluation Data Set: http://techwithtim.net/wp-cont....ent/uploads/2019/01/

2⃣ Neural Networks
💻 Text-Based Guide: https://techwithtim.net/tutori....als/python-neural-ne

3⃣ Simple AI Chat Bot
💻 Text-Based Guide: https://techwithtim.net/tutorials/ai-chatbot/
💾 JSON-File Download: https://techwithtim.net/wp-con....tent/uploads/2019/05

4⃣ Flappy Bird AI
💻 GitHub/Code: https://github.com/techwithtim/NEAT-Flappy-Bird
💾 Images: https://techwithtim.net/wp-con....tent/uploads/2019/08

⭐ SOFTWARE DOWNLOADS ⭐
🔗 Anaconda Download: https://www.anaconda.com/download/
🔗 Pycharm Download: https://www.jetbrains.com/pych....arm/download/#sectio

⭐ TIMESTAMPS ⭐
🎥 Course 1: Machine Learning for Beginners 🎥
⌨️ (0:00) Course Introduction
⌨️ (00:02:30) Introduction to Machine Learning & Environment Setup
⌨️ (00:12:24) Linear Regression Part 1 – Data Loading and Analysis
⌨️ (00:26:28) Linear Regression Part 2 – Implementation and Algorithm Explanation
⌨️ (00:42:50) Saving Models and Visualizing Data
⌨️(00:56:05) K-Nearest Neighbors Part 1 – Irregular Data
⌨️ (01:08:16) K-Nearest Neighbors Part 2 – Algorithm Explanation
⌨️ (01:21:33) K-Nearest Neighbors Part 3 – Implementation
⌨️ (01:31:54) Support Vector Machines Part 1 - SkLearn Datasets and Analysis
⌨️ (01:38:34) Support Vector Machines Part 2 – Algorithm Explanation
⌨️ (01:52:21) Support Vector Machines Part 3 – Implementation
⌨️(02:01:57) K-Means Clustering – Algorithm Explanation
⌨️ (02:15:11) K-Means Clustering - Implementation

🎥 Course 2: Neural Networks 🎥
⌨️ (02:27:07) Introduction to Neural Networks
⌨️ (02:53:47) Loading & Looking at Data
⌨️ (03:06:50) Creating a Model
⌨️ (03:24:05) Using and Testing Our Model
⌨️ (03:33:56) Text Classification Part 1 – Data Analysis and Model Architecture
⌨️ (03:55:23) Text Classification Part 2 – Embedding Layers
⌨️ (04:09:43) Text Classification Part 3 – Training the Model
⌨️ (04:19:49) Text Classification Part 4 – Saving and Loading Models

🎥 Course 3: AI Chat Bot 🎥
⌨️ (04:34:35) Part 1
⌨️ (04:50:28) Part 2
⌨️ (05:02:39) Part 3
⌨️ (05:14:32) Part 4
⌨️ (05:30:34) Part 5

🎥 Course 4: Neuroevolutionary Algorithm Plays Flappy Bird 🎥
⌨️ (05:39:16) Creating the Bird
⌨️ (05:51:36) Moving the Bird
⌨️ (06:10:04) Pixel Perfect Collision
⌨️ (06:29:22) Finishing the Graphics
⌨️ (06:41:16) NEAT Introduction and Configuration File
⌨️ (07:01:36) Implementing NEAT and Fitness Functions
⌨️ (07:16:32) Testing and Saving Models

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Tags:
- Tech With Tim
- Python Tutorials
- Machine Learning Course
- AI Course Python
- Python Machine Learning
- Python Machine Learning Course
- Machine Learning Python

#Python #MachineLearning #AI

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Machine Learning Full Course for Beginners | Machine Learning Tutorial | Machine Learning Course
Hello and welcome to the Machine Learning Full Course for Beginners using python. In this video, you will learn from basics to advanced machine learning concepts from Great Learning’s top faculties, including professor Mukesh Rao, Bharani Akella & many other leading industry experts. If you are an enthusiast who wants to start with machine learning from scratch, this machine learning beginner video is the best to start with.
#machinelearningfullcourse #machinelearning #machinelearningbasics

Agenda:
• Python for Machine Learning
• Role of Statistics in Machine Learning
• Introduction to Machine Learning and its types
• How does a Machine learning model learn?
• Supervised and Unsupervised learning algorithms
• Principal component analysis for dimensionality reduction
• Application of Machine Learning

Topics Covered:
00:01:09 – What Is Machine learning? (Introduction to Machine Learning)
00:03:00 – Why Machine Learning?
00:04:22 – Road Map to Machine Learning
00:01:09 – How to Use Kaggle (www.kaggle.com)
Machine Learning with Python (Python Libraries for Machine Learning)
00:11:25 - NumPy Python Tutorial (How to Create NumPy Array)
00:14:58 - How to Initialize NumPy Array
00:22:19 - How to check the shape of NumPy arrays
00:24:42 - How to Join NumPy Arrays
00:28:15 - NumPy Intersection & Difference
00:31:50 - NumPy Array Mathematics
00:39:15 - NumPy Matrix
00:42:28 - How to Transpose NumPy Matrix
00:43:21 - NumPy Matrix Multiplication
00:45:45 - NumPy Save & Load
00:47:44 - Python Pandas Tutorial
00:48:09 - Pandas Series Object
00:58:44 - Pandas Dataframe
01:12:00 - Matplotlib Python Tutorial
01:12:12 - Line plot
01:26:32 - Bar plot
01:32:37 - Scatter Plot
01:40:35 - Histogram
01:46:16 - Box Plot
01:51:03 - Violin Plot
01:51:57 - Pie Chart
01:56:39 - DoughNut Chart
01:59:04 - SeaBorn Line Plot
02:07:27 - SeaBorn Bar Plot
02:15:15 - SeaBorn ScatterPlot
02:20:25 - SeaBorn Histogram/Distplot
02:26:52 - SeaBorn JointPlot
02:30:23 - SeaBorn BoxPlot
02:38:59 – Role of Mathematics in Data Science
02:40:23 – What is data?
02:42:34 – What is Information?
02:43:21 – What is Statistics?
02:43:58 – What is Population?
02:46:48 – What is Sample?
02:47:33 – What are Parameters?
02:47:55 – Measures of Central Tendency
02:51:10 – Understanding Empirical Rule
02:53:16 – What is Mean, median, and mode?
02:57:04 – Measures of Spread (Understanding Range, Inter Quartile Range & Box-plot)
03:12:56 – Types of Machine Learning (Supervised, Unsupervised & Reinforcement Learning)
03:27:43 – How does a Machine Learning Model Learn?
03:35:31 – Supervised Machine Learning (Mukesh Rao)
04:34:51 – Python for Machine Learning
04:46:40 – Linear Regression Algorithm (Hands-on)
05:21:13 – What is Logistic Regression
05:29:39 – Linear Regression vs Logistic Regression
05:40:15 – Naïve Bayes Algorithm
05:49:32 – Diabetes Prediction using Naïve Bayes
06:15:18 – Decision Tree and Random Forest Algorithm
07:55:01 – Introduction to Support Vector Machines (SVMs)
08:07:08 – Kernel Functions
08:11:56 – Advantages & Disadvantages of SVMs
08:31:37 – K-NN Algorithm (K-Nearest Neighbour Algorithm)
08:40:13 – Introduction to Unsupervised Learning - Clustering
08:48:35 – Introduction to Principal Component Analysis
09:09:39 – PCA for Dimensionality Reduction
09:15:27 – Introduction to Hierarchical Clustering
09:28:38 – Types of Hierarchical Clustering
09:34:02 – How does Agglomerative hierarchical clustering work
09:42:32 – Euclidean Distance
09:45:10 – Manhattan Distance
09:48:01 – Minkowski Distance
09:50:02 – Jaccard Similarity Coefficient/Jaccard Index
09:54:02 – Cosine Similarity
09:58:18 – How to find an optimal number for clustering
10:03:02 – Applications Machine Learning

Enroll now for Machine Learning Certification Course from Reputed Universities:
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For more updates on courses and tips, follow us on:
Telegram: https://t.me/GreatLearningAcademy
Facebook: https://www.facebook.com/GreatLearningOfficial/
LinkedIn: https://www.linkedin.com/schoo....l/great-learning/myc
Follow our Blog: https://glacad.me/GL_Blog

Introduction to Statistical Learning: https://statlearning.com

Roadmap to Become a Data Scientist / Machine Learning Engineer in 2022: https://youtu.be/lHKlCqx94OY

Roadmap to Become a Data Analyst in 2022: https://youtu.be/024KMhTKlVs

Roadmap to Become a Data Engineer in 2022: https://youtu.be/ztGB-__Yg7w


Here's my favourite resources:

Best Courses for Analytics:
---------------------------------------------------------------------------------------------------------
+ IBM Data Science (Python): https://bit.ly/3Rn00ZA
+ Google Analytics (R): https://bit.ly/3cPikLQ
+ SQL Basics: https://bit.ly/3Bd9nFu


Best Courses for Programming:
---------------------------------------------------------------------------------------------------------
+ Data Science in R: https://bit.ly/3RhvfFp
+ Python for Everybody: https://bit.ly/3ARQ1Ei
+ Data Structures & Algorithms: https://bit.ly/3CYR6wR


Best Courses for Machine Learning:
---------------------------------------------------------------------------------------------------------
+ Math Prerequisites: https://bit.ly/3ASUtTi
+ Machine Learning: https://bit.ly/3d1QATT
+ Deep Learning: https://bit.ly/3KPfint
+ ML Ops: https://bit.ly/3AWRrxE


Best Courses for Statistics:
---------------------------------------------------------------------------------------------------------
+ Introduction to Statistics: https://bit.ly/3QkEgvM
+ Statistics with Python: https://bit.ly/3BfwejF
+ Statistics with R: https://bit.ly/3QkicBJ


Best Courses for Big Data:
---------------------------------------------------------------------------------------------------------
+ Google Cloud Data Engineering: https://bit.ly/3RjHJw6
+ AWS Data Science: https://bit.ly/3TKnoBS
+ Big Data Specialization: https://bit.ly/3ANqSut


More Courses:
---------------------------------------------------------------------------------------------------------
+ Tableau: https://bit.ly/3q966AN
+ Excel: https://bit.ly/3RBxind

+ Computer Vision: https://bit.ly/3esxVS5
+ Natural Language Processing: https://bit.ly/3edXAgW

+ IBM Dev Ops: https://bit.ly/3RlVKt2
+ IBM Full Stack Cloud: https://bit.ly/3x0pOm6
+ Object Oriented Programming (Java): https://bit.ly/3Bfjn0K

+ TensorFlow Advanced Techniques: https://bit.ly/3BePQV2
+ TensorFlow Data and Deployment: https://bit.ly/3BbC5Xb
+ Generative Adversarial Networks / GANs (PyTorch): https://bit.ly/3RHQiRj


Become a Member of the Channel! https://bit.ly/3oOMrVH
Follow me on LinkedIn! https://www.linkedin.com/in/greghogg/

#machinelearning #datascience #statistics #datascience