Go from zero to a machine learning engineer in 12 months. This step-by-step roadmap covers the essential skills you must learn to become a machine learning engineer in 2024.

Download the FREE roadmap PDF here: https://mosh.link/machine-learning-roadmap

✋ Stay connected

- Complete courses: https://codewithmosh.com
- Twitter: https://twitter.com/moshhamedani
- Facebook: https://www.facebook.com/programmingwithmosh/
- Instagram: https://www.instagram.com/codewithmosh.official/
- LinkedIn: https://www.linkedin.com/school/codewithmosh/

🔗 Other roadmaps

https://youtu.be/Tef1e9FiSR0?si=QpVnZ_o9-DAXzT71
https://youtu.be/OeEHJgzqS1k?si=qd0ZIqAzUpZQn6BX

📚 Tutorials

https://youtu.be/_uQrJ0TkZlc?si=ZhlCrQs1SkaPNVa8
https://youtu.be/8JJ101D3knE?si=OGTuS35LQqSunuhh
https://youtu.be/BBpAmxU_NQo?si=dm-ZCPxVBYWS1Qhn
https://youtu.be/7S_tz1z_5bA?si=QL7s_M2Ao90RDwG8

📖 Chapters

00:00 - Introduction
00:20 - Programming Languages
00:42 - Version Control
01:03 - Data Structures & Algorithms
01:35 - SQL
01:55 - The Complete Roadmap PDF
02:19 - Mathematics & Statistics
02:40 - Data Handling
03:15 - Machine Learning Fundamentals
03:57 - Advanced Topics
04:28 - Model Deployment

#machinelearning #ai #datascience #coding #programming

Transformer Neural Networks are the heart of pretty much everything exciting in AI right now. ChatGPT, Google Translate and many other cool things, are based on Transformers. This StatQuest cuts through all the hype and shows you how a Transformer works, one-step-at-a time.

NOTE: If you're interested in learning more about Backpropagation, check out these 'Quests:
The Chain Rule: https://youtu.be/wl1myxrtQHQ
Gradient Descent: https://youtu.be/sDv4f4s2SB8
Backpropagation Main Ideas: https://youtu.be/IN2XmBhILt4
Backpropagation Details Part 1: https://youtu.be/iyn2zdALii8
Backpropagation Details Part 2: https://youtu.be/GKZoOHXGcLo

If you're interested in learning more about the SoftMax function, check out:
https://youtu.be/KpKog-L9veg

If you're interested in learning more about Word Embedding, check out: https://youtu.be/viZrOnJclY0

If you'd like to learn more about calculating similarities in the context of neural networks and the Dot Product, check out:
Cosine Similarity: https://youtu.be/e9U0QAFbfLI
Attention: https://youtu.be/PSs6nxngL6k

If you'd like to support StatQuest, please consider...
Patreon: https://www.patreon.com/statquest
...or...
YouTube Membership: https://www.youtube.com/channe....l/UCtYLUTtgS3k1Fg4y5

...buying my book, a study guide, a t-shirt or hoodie, or a song from the StatQuest store...
https://statquest.org/statquest-store/

...or just donating to StatQuest!
paypal: https://www.paypal.me/statquest
venmo: @JoshStarmer

Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter:
https://twitter.com/joshuastarmer

0:00 Awesome song and introduction
1:26 Word Embedding
7:30 Positional Encoding
12:53 Self-Attention
23:37 Encoder and Decoder defined
23:53 Decoder Word Embedding
25:08 Decoder Positional Encoding
25:50 Transformers were designed for parallel computing
27:13 Decoder Self-Attention
27:59 Encoder-Decoder Attention
31:19 Decoding numbers into words
32:23 Decoding the second token
34:13 Extra stuff you can add to a Transformer

#StatQuest #Transformer #ChatGPT

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

A friendly explanation of how computers predict and generate sequences, based on Recurrent Neural Networks.
For a brush up on Neural Networks, check out this video: https://www.youtube.com/watch?v=BR9h47Jtqyw

0:00 A friendly introduction to Recurrent Neural Networks
1:38 A friendly introduction to Deep Learning and Neural Networks
2:11 Vectors
5:22 Perfect Roommate
7:13 Simple Neural Network
7:54 Simple (Recurrent) Neural Network
10:03 Cooking Schedule
11:47 More Complicated RNN
12:06 Food
13:31 Weather
14:38 Add
16:02 Merge
20:53 Start with random weights
21:05 Use Gradient Descent
21:41 New Error Function

Read about this in more detail in my latest blog post: https://www.builder.io/blog/train-ai

#ai #developer #javascript #react

Thanks to Microsoft for sponsoring this video! Submit your #CodingWithCopilot story for a chance to be featured in an upcoming video!

In this video, you'll learn how to build an AI agent
from scratch in Python. I'll walk you through everything step-by-step. This will be very beginner friendly,
and you'll learn how to make something quite interesting in Python
with some popular frameworks like LangChain. I'll show you how to use various
LLMs like Claude or GPT, how you can give the agent access to various tools, and how you can structure the output of the agent
so you can use it in your code.

🎞 Video Resources 🎞
Code in this video: https://github.com/techwithtim..../PythonAIAgentFromSc
Learn Python Virtual Environments: https://www.youtube.com/watch?v=Y21OR1OPC9A
Get Your Anthropic API Key: https://console.anthropic.com/settings/keys
Get Your OpenAI API Key: https://platform.openai.com/api-keys

⏳ Timestamps ⏳
00:00 | Overview
00:35 | Agent Demo
01:38 | Setup/Requirements
06:53 | Basic Code Setup/Imports
08:37 | Using Claude or GPT
10:00 | Getting Your API Key
11:32 | Basic LLM Functionality
12:43 | Structured Output/Models
15:42 | Prompt Templates
17:33 | Creating & Running the Agent
21:00 | Output Parsing
23:39 | Adding Prebuilt Tools
30:48 | Custom Tool Calling

Hashtags
#CodingWithCopilot #Sponsored

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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

Claude Code isn’t just another coding tool. t’s a game-changer for automation, DevOps, and productivity. Imagine combining the simplicity of Unix commands with the intelligence of an AI-powered assistant. That’s what Claude Code delivers.

From coding support to system maintenance, from workflow automation to custom commands, Claude Code is becoming an essential tool across industries.

🔥 Watch now and see how it can transform the way teams work.

#ClaudeCode #Anthropic #Claudeai #Coding #AITools #Automation #DevOpsCommunity #ProductivityTools #kodekloud

⚡Build Your AI Agency & Land Your First Client⚡
https://www.skool.com/chase-ai

🔥 FREE Skool community with all the templates!🔥
https://www.skool.com/chase-ai-community

💻 Need custom work? Book a consult💻
https://chaseai.io

Ever wanted to build your own web application but felt stopped by a lack of coding knowledge? This video is your ultimate starting guide. I'll show you how to use Claude Code, a powerful AI assistant that can write, plan, and execute code based on your plain English instructions. This tutorial is designed specifically for non-coders and absolute beginners, meaning we start from the very beginning with zero assumptions about your technical skills.

We'll walk through every single step together to get you up and running. You'll learn how to set up your complete development environment, including installing Node.js and Cursor. From there, we dive into the core mechanics of Claude Code, mastering the essential slash commands, building agents, and learning the workflow to turn your ideas into functional applications.

The best way to learn is by doing, so we'll build a real-world project: a complete front-end for an AI-powered chatbot. But we won't stop there. To make sure you have something real to show for your efforts, I'll guide you through the process of deploying your new application to the internet using GitHub and Vercel. By the end of this video, you won't just understand Claude Code—you'll have a live, shareable project on your very own domain.

TIMESTAMPS:
0:00 Intro
4:37 Installing Claude Code
5:51 Claude Code Basics
9:16 Building Our Frontend
11:20 Cursor AI
13:27 Local Testing
15:12 Claude Code Agents
18:46 n8n Integration
26:33 GitHub and Vercel Setup
29:04 Preparing for Prod
34:00 Troubleshooting the Deployment
37:24 The Mindset


RESOURCES FROM THIS VIDEO:
➡️ Get started with n8n: https://n8n.partnerlinks.io/bfumg8i8y8nc
➡️ Learn More About AI Agents: https://www.skool.com/chase-ai
➡️ My Website: https://www.chaseai.io

#ClaudeCode #AICoding #CodingForBeginners #NonCoders #AI #nocode #n8n #automation

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

October 17, 2025
This lecture covers:
• Pretraining
• Quantization
• Hardware optimization
• Supervised finetuning (SFT)
• Parameter-efficient finetuning (LoRA)

To follow along with the course schedule and syllabus, visit: https://cme295.stanford.edu/syllabus/

Chapters:
00:00:00 Introduction
00:07:19 Pretraining
00:13:26 FLOPs, FLOPS
00:16:34 Scaling laws, Chinchilla law
00:24:49 Training optimizations overview
00:31:09 Data parallelism with ZeRO
00:35:51 Model parallelism
00:38:26 Flash Attention
00:52:37 Quantization
00:56:00 Mixed precision training
01:02:31 Supervised finetuning
01:09:21 Instruction tuning
01:37:53 Parameter-efficient finetuning with LoRA
01:45:16 QLoRA

Afshine Amidi is an Adjunct Lecturer at Stanford University.

Shervine Amidi is an Adjunct Lecturer at Stanford University.

View the course playlist: https://www.youtube.com/playli....st?list=PLoROMvodv4r

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

January 9, 2026
This lecture covers:
• How the ongoing integration of LLMs into our social worlds is creating new risks and opportunities
• Best practices for designing rehabilitative bots

To follow along with the seminar schedule, visit: https://hci.stanford.edu/

Jeremy Foote is an Assistant Professor in the Brian Lamb School of Communication at Purdue University and a faculty member in the Community Data Science Collective.

Provides an intuitive explanation for what deep learning models are doing when they find an 'efficient feature representation'
Full course playlist here: https://www.youtube.com/playli....st?list=PLnrO0TOwDbu

BỂ THỦY SINH NGHỆ THUẬT #thuysinh #aquarium

THỦY SINH NHA TRANG - THUỶ SINH MANG TÀI LỘC ĐẾN NHÀ BẠN

Thủy Sinh Nha Trang cung cấp sỉ lẻ các loại sản phẩm thủy sinh như cây thủy sinh, hồ thủy sinh, tôm cảnh, cá cảnh, tép cảnh, ốc cảnh, hồ cá thủy sinh, bể cá, phụ kiện thủy sinh, đèn thủy sinh và các thiết bị khác với giá hấp dẫn tại khu vực miền Trung. Chúng tôi chuyên về Thiết kế cảnh quan, Trang trí nội thất, Tiểu cảnh.

Phone: (+84) 0777-525-328
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In this video on large language models, we’ll take a look at Large Language Models (LLMs) and understand what they are all about. First, we’ll explain what LLMs are and why they’re becoming so important in the world of technology. You’ll learn what these models do and how they’re used in areas like healthcare, finance, and customer service. We’ll also talk about how large language models are trained. These models use huge amounts of data to learn and create human-like text. We’ll break down how they work, from understanding language to predicting what comes next in a sentence.
Lastly, we’ll explore where LLMs are used today, such as in chatbots, content creation, and translations. These models are helping businesses work smarter and faster.

00:00 Introduction to LLMs
02:17 What are large language models?
03:22 How LLMs are trained?
05:05 How LLMsWork?
05:48 Applications Of LLMs

What is a large language model?
A large language model (LLM) is a type of artificial intelligence (AI) program that can recognize and generate text, among other tasks. LLMs are trained on huge sets of data — hence the name "large." LLMs are built on machine learning: specifically, a type of neural network called a transformer model.

Is ChatGPT a large language model?
The reason is that Large Language Models like ChatGPT are actually trained in phases. Phases of LLM training: (1) Pre-Training, (2) Instruction Fine-Tuning, (3) Reinforcement from Human Feedback (RLHF).

What are LLM examples?
For example, LLMs could be used to create personalized education or healthcare plans, leading to better patient and student outcomes. LLMs can be used to help businesses and governments make better decisions by analyzing large amounts of data and generating insights.

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

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PyCharm Python Tutorial. PyCharm for Data Science.
https://github.com/datamites/p....ublic_datasets/tree/
https://github.com/datamites/b....an_house_price_pycha

00:00 Why PyCharm?
02:32 PyCharm Installation.
03:50 PyCharm Quick Overview
27:33 Data Science Project with PyCharm
34:43 Data Science Project src/feature_processing.py
45:18 Data Science Project Labelencoding
58:22 Data Science Project src/train.py
1:21:04 PyCharm integration with GitHub Repository

#Why is #PyCharm?
#How To do Data Science #Project with PyCharm?

Trainer: Mr.Ashok Veda - https://in.linkedin.com/in/ashokveda

Watch above video to understand Why PyCharm? and How To do Data Science Projects with PyCharm?

DataMites is providing global valid data science courses along with certifications. Join Certified Data Scientist Course and Get Certified in data science and machine learning.
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In this Data Science Course video, you'll learn the basics of data science, from statistics and machine learning to advanced topics like natural language processing and deep learning. You'll learn to collect, clean, and analyze data, build machine-learning models, and communicate your findings to stakeholders.

🔵 Here is a breakdown of the topics covered in the video:

00:00:00 - Introduction
00:02:43 - How to Master Data Science
00:14:50 - Demand for Data Science Professionals
00:28:24 - Data Science Learning Path
00:35:52 - Statistics & Probability
00:57:26 - What is Statistics?
01:39:19 - What is probability?
01:55:06 - Bayesian tree
02:52:13 - Sampling distribution
03:23:25 - Systematic Sampling
04:01:18 - Introduction to Numpy Array
04:37:32 - Numpy Indexing and Slicing
04:43:15 - Array Manipulation
05:04:33 - Introduction to Pandas
06:13:50 - Introduction to Matplotlib
06:17:09 - Types of Plots in Python
06:29:59 - Line plots
06:45:51 - Bar charts
06:50:18 - Scatter plots
06:54:21 - Histogram
07:26:51 - Pie charts
08:20:02 - Introduction to Machine Learning
08:21:13 - Types of machine learning
08:36:41 - Logistic Regression
08:44:34 - Data Visualization
10:08:52 - Data Science Interview Questions & Answers

This video on Data Science Courses For Beginners is perfect for anyone who wants to learn the skills they need to become a data scientist. Whether you're a student, a working professional, or just someone who's interested in learning more about data science, this video is for you.

Enroll today and start your journey to becoming a Data Scientist!

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Intellipaat is a global online professional training provider. We offer some of the most updated, industry-designed certification training programs, including courses in Big Data, Data Science, Artificial Intelligence, and 150 other top-trending technologies.
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