🔥 Post Graduate Diploma in Artificial Intelligence by E&ICT Academy
NIT Warangal: https://www.edureka.co/executi....ve-programs/machine-
This Edureka video is a part of the Machine Learning Tutorial that will provide you with a list of the Top Machine Learning tools and Frameworks that one must know about. Machine Learning Tools help us in developing algorithmic applications of artificial intelligence which can enable the ability to learn and improve machine processing without much human input. Below is the list of Best Machine Learning Tools and Frameworks covered in this Machine Learning tutorial:
00:00 Introduction
01:01 Machine Learning Tutorial
01:24 Top Machine Learning Tools
01:29 Scikit Learn
02:16 Knime
03:08 Tensorflow
04:14 Weka
05:02 PyTorch
05:47 Rapidminer
06:43 Google's AutoML
07:41 Azure ML Studio
08:21 Accord.NET
09:28 Google Collab
10:24 NLTK

Check out our Machine Learning Tutorial playlist for more videos: http://bit.ly/2taym8X

🔴Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV

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

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🔵Machine Learning Engineer Masters Program: https://bit.ly/3Hi1sXN

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

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#Edureka #MachineLearningEdureka #MachineLearningTools #MachineLearningUsingPython #MachineLearningTraining #MachineLearningTutorial

------------------------------
About the ML Master's Program

Edureka’s Machine Learning Masters Program makes you proficient in techniques like Supervised Learning, Unsupervised Learning and Natural Language Processing. It includes training on the latest advancements and technical approaches in Artificial Intelligence & Machine Learning such as Deep Learning, Graphical Models and Reinforcement Learning.

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

Why Learn Machine Learning?

Machine Learning is a concept which allows the machine to learn from examples and experience, and that too without being explicitly programmed. So instead of you writing the code, what you do is you feed data to the generic algorithm, and the algorithm/machine builds the logic based on the given data.
-------------------------------------

Edureka's Machine Learning Masters Program Curriculum

🟠 Live Courses 🟠
Python Programming Certification Course: https://www.edureka.co/python-....programming-certific
Machine Learning Certification Training using Python: https://www.edureka.co/machine....-learning-certificat
Graphical Models Certification Training: https://www.edureka.co/graphical-modelling-course
Reinforcement Learning: https://www.edureka.co/reinfor....cement-learning-cour
Natural Language Processing with Python Certification: https://www.edureka.co/python-....natural-language-pro
Deep Learning with TensorFlow 2.0 Certification Training: https://www.edureka.co/ai-deep....-learning-with-tenso
Python Spark Certification Training using PySpark: https://www.edureka.co/pyspark....-certification-train

🟠 Elective Course 🟠
Python Scripting Certification Training: https://www.edureka.co/python-scripting

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

Prerequisites

There are no prerequisites for enrollment to the Masters Program. However, as a goodwill gesture, Edureka offers a complimentary self-paced course in your LMS on SQL Essentials to brush up on your SQL Skills. This program is designed and developed for an aspirant planning to build a career in Machine Learning or an experienced professional working in the IT industry.

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

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

🔥AI Engineer Masters Program (Discount Code - YTBE15): https://www.simplilearn.com/masters-in-artificial-intelligence?utm_campaign=24JunUSPriority&utm_medium=DescriptionFF&utm_source=youtube
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This Machine Learning with Python tutorial gives an introduction to Machine Learning and how to implement Machine Learning algorithms in Python. By the end of this video, you will be able to understand Machine Learning workflow, steps to download Anaconda, types of Machine Learning and hands-on in Python for Linear Regression and K-Means clustering algorithms.


Below are the topics covered in this Machine Learning tutorial:
1. Why Machine Learning? ( 01:09 )
2. Applications of Machine Learning ( 01:50 )
3. How does Machine Learning work? ( 03:33 )
4. Machine Learning Workflow ( 04:53 )
5. Steps to download Anaconda ( 06:13 )
6. Types of Machine Learning ( 09:53 )
7. Linear Regression Demo ( 13:51 )
8. K-Means Clustering Demo ( 26:02 )
9. Use Case - Weather Analysis ( 39:27 )

What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.

Subscribe to our channel for more Machine Learning Tutorials: https://www.youtube.com/user/S....implilearn?sub_confi

Download the Machine Learning Career Guide to explore and step into the exciting world of Machine Learning, and follow the path towards your dream career- https://www.simplilearn.com/machine-learning-career-guide-pdf?utm_campaign=What-is-Machine-Learning-7JhjINPwfYQ&utm_medium=Tutorials&utm_source=youtube

You can also go through the Slides here: https://goo.gl/AMDVtD

Machine Learning Articles: https://www.simplilearn.com/what-is-artificial-intelligence-and-why-ai-certification-article?utm_campaign=What-is-Machine-Learning-7JhjINPwfYQ&utm_medium=Tutorials&utm_source=youtube

#MachineLearningAlgorithms #Datasciencecourse #DataScience #SimplilearnMachineLearning #MachineLearningCourse

➡️ 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:




🔥Free Machine Learning Course: https://www.simplilearn.com/learn-machine-learning-basics-skillup?utm_campaign=MachineLearningwithPython&utm_medium=Description&utm_source=youtube

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

Dr. Paul Lessard and his collaborators have written a paper on "Categorical Deep Learning and Algebraic Theory of Architectures". They aim to make neural networks more interpretable, composable and amenable to formal reasoning. The key is mathematical abstraction, as exemplified by category theory - using monads to develop a more principled, algebraic approach to structuring neural networks.

We also discussed the limitations of current neural network architectures in terms of their ability to generalise and reason in a human-like way. In particular, the inability of neural networks to do unbounded computation equivalent to a Turing machine. Paul expressed optimism that this is not a fundamental limitation, but an artefact of current architectures and training procedures.

The power of abstraction - allowing us to focus on the essential structure while ignoring extraneous details. This can make certain problems more tractable to reason about. Paul sees category theory as providing a powerful "Lego set" for productively thinking about many practical problems.

Towards the end, Paul gave an accessible introduction to some core concepts in category theory like categories, morphisms, functors, monads etc. We explained how these abstract constructs can capture essential patterns that arise across different domains of mathematics.

Paul is optimistic about the potential of category theory and related mathematical abstractions to put AI and neural networks on a more robust conceptual foundation to enable interpretability and reasoning. However, significant theoretical and engineering challenges remain in realising this vision.

Please support us on Patreon. We are entirely funded from Patreon donations right now.
https://patreon.com/mlst
If you would like to sponsor us, so we can tell your story - reach out on mlstreettalk at gmail

Links:
Categorical Deep Learning: An Algebraic Theory of Architectures
Bruno Gavranović, Paul Lessard, Andrew Dudzik,
Tamara von Glehn, João G. M. Araújo, Petar Veličković
Paper: https://categoricaldeeplearning.com/

Symbolica:
https://twitter.com/symbolica
https://www.symbolica.ai/

Dr. Paul Lessard (Principal Scientist - Symbolica)
https://www.linkedin.com/in/paul-roy-lessard/

Neural Networks and the Chomsky Hierarchy (Grégoire Delétang et al)
https://arxiv.org/abs/2207.02098

Interviewer: Dr. Tim Scarfe

Pod: https://podcasters.spotify.com..../pod/show/machinelea

Transcript:
https://docs.google.com/docume....nt/d/1NiHJKTkeqYdpcg

More info about NNs not being recursive/TMs:
https://www.youtube.com/watch?v=4KIQH1VEwBI

Geometric Deep Learning blueprint:
https://www.youtube.com/watch?v=bIZB1hIJ4u8

TOC:
00:00:00 - Intro
00:05:07 - What is the category paper all about
00:07:19 - Composition
00:10:42 - Abstract Algebra
00:23:01 - DSLs for machine learning
00:24:10 - Inscrutability
00:29:04 - Limitations with current NNs
00:30:41 - Generative code / NNs don't recurse
00:34:34 - NNs are not Turing machines (special edition)
00:53:09 - Abstraction
00:55:11 - Category theory objects
00:58:06 - Cat theory vs number theory
00:59:43 - Data and Code are one and the same
01:08:05 - Syntax and semantics
01:14:32 - Category DL elevator pitch
01:17:05 - Abstraction again
01:20:25 - Lego set for the universe
01:23:04 - Reasoning
01:28:05 - Category theory 101
01:37:42 - Monads
01:45:59 - Where to learn more cat theory

🔥 PMP Certification Training Course: https://www.edureka.co/pmp-cer....tification-exam-trai
🔥 PRINCE2® Foundation & Practitioner Certification Course: https://www.edureka.co/prince2....-foundation-and-prac

In this video, on *PMP vs PRINCE2* , we will dive deep into the project management certifications by comparing two of the most sought-after credentials: *PMP* (Project Management Professional) and *PRINCE2* (Projects IN Controlled Environments). If you're considering advancing your career in project management, understanding the differences between *PMP vs PRINCE2* is essential. We’ll explore the key differences between these certifications, including their frameworks, benefits, and how they fit into different project management methodologies. Additionally, we will cover the exam details and eligibility criteria for both certifications. Whether you’re a seasoned project manager or just starting your career, this video will help you understand which certification is right for you.

✅ 00:00 - PMP vs PRINCE2
✅ 01:41 - What is a PMP Certification?
✅ 02:10 - What is a PRINCE2 Certification?
✅ 02:39 - Key Differences Between PMP vs. PRINCE2 Certifications
✅ 07:52 - What is the cost of PMP Certification?
✅ 08:15 - What is the cost of PRINCE2 Certification?
✅ 08:38 - Purchase Option for Preparing for the PRINCE2 Exam in India
✅ 09:39 - Benefits of PMP Certification
✅ 10:37 - Benefits of PRINCE2 Certification

✅Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV

📝Feel free to share your comments below.📝

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📌𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲: https://www.edureka.co/community/

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

What is a PMP certification?

A PMP certification, provided by the Project Management Institute (PMI), validates and elevates project managers' expertise and abilities. It showcases mastery in project management principles, top practices, and methodologies and is worldwide recognized as a testament to proficiency in the industry.

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

Who should go for this course?

Project managers aim to enhance their skills.
Professionals aspiring to become project managers.
Individuals were seeking validation of their project management expertise.

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

The concise prerequisites for the PMP Course:

Complete 35 hours of formal project management education through online training.
Obtain PMP certification by demonstrating either 4,500 hours of project leadership (with a four-year degree) or 7,500 hours (with a secondary diploma).
This ensures a mix of education and practical experience for certification.

For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US & Others: +18885487823 (toll-free)

#pmpvsprince2 #pmp #prince2 #projectmanagement #projectmanagementcertification #projectmanagementtraining #BestCertifications

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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?
07:05 What are Large Language Models?

*RESOURCES I MENTION IN THE VIDEO*
Google’s full course: https://www.cloudskillsboost.g....oogle/course_templat
Grab my free Workspace Toolkit: https://academy.jeffsu.org/workspace-toolkit?utm_source=youtube&utm_medium=video&utm_campaign=146

*MY FAVORITE GEAR*
🎬 My YouTube Gear - https://www.jeffsu.org/yt-gear/
🎒 Everyday Carry - https://www.jeffsu.org/my-edc/

*MY TOP 3 FAVORITE SOFTWARE*
❎ CleanShot X - https://geni.us/cleanshotx
✍️ Skillshare - https://geni.us/skillshare-jeff
📖 Readwise - https://readwise.io/jeffsu/

*BE MY FRIEND:*
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*👨🏻‍💻 WHO AM I:*
I'm Jeff, a tech professional trying to figure life out. What I do end up figuring out, I share!

_PS: Some of the links in this description are affiliate links I get a kickback from and my opinions are my own and may not reflect that of my employer_ 😇

🔥Data Analyst Masters Program (Discount Code - YTBE15) - https://www.simplilearn.com/data-analyst-masters-certification-training-course?utm_campaign=DoANDQMAmIg&utm_medium=DescriptionFirstFold&utm_source=Youtube
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This Excel Full course video by simplilearn will help you learn Microsoft excel from basics to advanced concepts. This Introduction to Excel Full Course provides a detailed guide to learning Excel and related data analytics tools. Starting with what Microsoft Excel is, the course offers an Excel tutorial for beginners, teaching you the basics. You'll learn advanced features like Microsoft Power Query and how to use Copilot in Excel for enhanced productivity. The course covers 10 essential Excel formulas, explains the concept of DBMS, and explores data analytics using AI. It also compares Data Science vs. Data Analytics and outlines a roadmap to becoming a data analyst. Additionally, it introduces key BI terms every data analyst should know and demonstrates how to use ChatGPT to build an Excel dashboard. To solidify your skills, it concludes with the top 10 data analyst projects for your portfolio, providing practical insights to excel in your career.

00:00:00 Introduction to Excel Full Course
00:03:55 What is Microsoft Excel
05:42:00 Excel Tutorial For Beginners
06:03:24 How to use Microsoft power query
06:12:45 Copilot In EXCEL
06:36:31 10 important excel formulas
06:37:39 What is DBMS
06:48:32 Data Analytics using AI
07:12:59 Data Science Vs Data Analyst
07:31:40 Data Analyst Roadmap
07:32:29 Top BI Terms every data analyst know
08:09:23 How to use ChatGPT to built an Excel Dashboard
08:21:58 Top 10 Data Analyst Projects for your portfolio

⏩ Check out the Excel tutorial videos: https://www.youtube.com/watch?v=nPkmWE4JCfE&list=PLEiEAq2VkUUKf8aLrspLg3zuyJ5S-5K5S

#excelfullcourse #excelcourse #excel #exceldataanalytics #exceltraining #excelformulasandfunctions #exceldatavisualization #simplilearn #2024

➡️ 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.

Key Features
✅ Post Graduate Program certificate and Alumni Association membership
✅ Exclusive hackathons and Ask me Anything sessions by IBM
✅ 8X higher live interaction in live online classes by industry experts
✅ Capstone from 3 domains and 14+ Data Analytics Projects with Industry datasets from Google PlayStore, Lyft, World Bank etc.
✅ Master Classes delivered by Purdue faculty and IBM experts
✅ Simplilearn's JobAssist helps you get noticed by top hiring companies
✅ Resume preparation and LinkedIn profile building
✅ 1:1 mock interview
✅ Career accelerator webinars

Skills Covered
✅ Data Analytics
✅ Statistical Analysis using Excel
✅ Data Analysis Python and R
✅ Data Visualization Tableau and Power BI
✅ Linear and logistic regression modules
✅ Clustering using kmeans
✅ Supervised Learning

👉 Learn More at: https://www.simplilearn.com/pgp-data-analytics-certification-training-course?utm_campaign=DoANDQMAmIg&utm_medium=Description&utm_source=youtube

#anuvjain #guitar #guitarcover #independentartist

This video is intended for entertainment purposes only. I do not own the rights to any of the songs featured in this jukebox compilation. All audio and visual content used in this video are the property of their respective copyright holders.

Fair Use Notice: This video is not intended to infringe upon any copyright, but to provide a transformative experience for viewers who wish to enjoy the artist's music in a compiled format. This use is believed to fall under fair use as it is nonprofit, and intended for review and commentary purposes.

If you are the copyright owner and would like this video to be taken down, please contact me directly. I will promptly remove the content as requested.

Thank you for your understanding and support!

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26. Olivia Newton-John - I Honestly Love You
27. Eric Clapton - I Shot the Sheriff
28. Carpenters - I Won't Last a Day Without You
29. Terry Jacks - If You Go Away
30. Olivia Newton-John - If You Love Me (Let Me Know)
31. Jim Croce - I'll Have to Say I Love You in a Song
32. Albert Hammond - I'm a Train
33. Alvin Stardust - Jealous Mind
34. Paul McCartney & Wings - Jet
35. Dolly Parton - Jolene
36. The Rubettes - Jukebox Jive
37. Kool and the Gang - Jungle Boogie
38. Paul McCartney & Wings - Junior's Farm
39. Queen - Killer Queen
40. The Drifters - Kissin' in the Back Row
41. Carl Douglas - Kung Fu Fighting
42. Neil Sedaka - Laughter in the Rain
43. Bachman–Turner Overdrive - Let It Ride
44. Mud - Lonely This Christmas
45. Neil Diamond - Long Fellow Serenade
46. The Osmonds - Love Me for a Reason
47. The Love Unlimited Orchestra - Love's Theme
48. Elton John - Lucy in the Sky with Diamonds
49. Electric Light Orchestra - Ma-Ma-Ma-Belle
50. Maria Muldaur - Midnight at the Oasis
51. Bee Gees - Mr. Natural
52. Gloria Gaynor - Never Can Say Goodbye
53. Billy Preston - Nothing from Nothing
54. Cat Stevens - Oh Very Young
55. Billy Joel - Piano Man
56. Golden Earring - Radar Love
57. David Bowie - Rebel Rebel
58. Steely Dan - Rikki Don't Lose That Number
59. The Righteous Brothers - Rock and Roll Heaven
60. Andy Kim - Rock Me Gently
61. The Hues Corporation - Rock the Boat
62. George McCrae - Rock Your Baby
63. Sweet Sensation - Sad Sweet Dreamer
64. Terry Jacks - Seasons in the Sun
65. Queen - Seven Seas of Rhye
66. Bay City Rollers - Shang-a-Lang
67. Charles Aznavour - She
68. Al Wilson - Show and Tell
69. Blue Magic - Sideshow
70. Ralph McTell - Streets of London
71. The Rubettes - Sugar Baby Love
72. Bay City Rollers - Summerlove Sensation
73. Gordon Lightfoot - Sundown
74. John Denver - Sunshine on My Shoulders
75. Lynyrd Skynyrd - Sweet Home Alabama
76. The Sweet - Teenage Rampage
77. The Hollies - The Air That I Breathe
78. Slade - The Bangin' Man
79. Elton John - The Bitch Is Back
80. Mud - The Cat Crept In
81. Billy Joel - The Entertainer
82. Steve Miller Band - The Joker
83. Grand Funk - The Loco-Motion
84. Paper Lace - The Night Chicago Died
85. Leo Sayer / Three Dog Night - The Show Must Go On
86. The Sweet - The Sixteens
87. Dionne Warwick & The Spinners - Then Came You
88. Sparks - This Town Ain't Big Enough for Both of Us
89. Mud - Tiger Feet
90. America - Tin Man
91. The Rubettes - Tonight
92. MFSB - TSOP (The Sound of Philadelphia)
93. Mike Oldfield - Tubular Bells
94. Aretha Franklin - Until You Come Back to Me
95. ABBA - Waterloo
96. Three Degrees - When Will I See You Again
97. Bachman–Turner Overdrive - You Ain't Seen Nothing Yet
98. Stevie Wonder - You Haven't Done Nothin'
99. The Stylistics - You Make Me Feel Brand New
100. Barry White - You're the First, The Last, My Everything

Based on the charts of the US and Europe.
The songs are in alphabetical order.
Song snippets are kept short due to copyright reasons.

Discover how startups can benefit from adopting or creating Small Language Models and the role they have in enabling agentic AI. Learn from world-class experts Julien Simon, Chief Evangelist at Arcee, and Nicolas David, Sr. Startup Architect at AWS

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Timestamps ⏰
00:00 AI agents course!
01:13 Basics of AI Agents - What is an AI Agent?
02:58 What tasks are agents good for?
05:47 Spectrum of Autonomy
06:45 Context Engineering
07:13 Task Decomposition
07:59 Demo: No-Code Agent System
09:22 Intermediate AI Agents - Measuring Performance
10:39 Memory
11:42 Guardrails
12:50 Reflection
13:56 Tool Use
15:51 Designing Good Tools
16:56 Planning
19:05 Multi-Agent Collaboration
20:38 Multi-Agent System Design - Roles
21:25 Multi-Agent System Design - Communication Patterns
23:09 Multi-Agent System Design - Communication Pitfalls
23:44 Multi-Agent System Design - Best Practices
25:02 Demo: Multi-Agent System in Python
26:14 Advanced AI Agents - Advanced Task Decomposition
29:02 Improving Performance
30:26 Reducing Latency
31:16 Reducing Cost
32:39 Observability and Monitoring
35:10 Security
37:17 Bonus Resource!
----------------------------------------
Agentic System Design:
https://gerred.github.io/build....ing-an-agentic-syste

The slides and examples were adapted from a few different courses, including:
https://www.deeplearning.ai/co....urses/design-develop
https://learn.deeplearning.ai/....courses/agentic-ai/i
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🎥 Other videos you might like:

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AI Engineering: A *Realistic* Roadmap for Beginners
https://www.youtube.com/watch?v=dbUIjFXIpis

4 *Real* Machine Learning Projects That Get You Hired - No More Tutorials!
https://www.youtube.com/watch?v=MFSFcPsMsuE

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#AI #AIEngineering #AIAgents

AI can feel overwhelming, but this hands-on course makes it practical, approachable, and fun. You will start with AI fundamentals and model mechanics like transformers and tokens, before moving into the architecture of tools, workflows, and true agents. You will build and experiment with four distinct agents, seeing firsthand how single agents evolve into a multi-agent system. Finally, you will learn how OpenClaw woks to gain the confidence needed to design, build, test, and deploy real-world AI agents.

Course resources: https://kode.wiki/3SrN8GO

Course created by @KodeKloud

❤️ Support for this channel comes from our friends at Scrimba – the coding platform that's reinvented interactive learning: https://scrimba.com/freecodecamp

Chapters
Section 1 & 2: LLM & Core Concepts
- 00:00:00 Course Welcome & Architecture Fundamentals
- 00:07:54 Hallucinations, Model Evolution, & Competitors
- 00:15:23 Developer Fundamentals & Working with APIs
- 00:17:54 Token Economics & Managing Context Windows
- 00:31:13 API Message Roles & Prompt Engineering Nuances

Section 3 & 4: Code Setup & Tool Architecture
- 00:38:29 Hands-On Lab: Practice Labs & First API Call
- 00:45:01 Multi-Turn History, API Costs, & Trade-offs
- 00:51:08 Architecture Overview: Tool Schemas & Execution Loops
- 00:58:20 Predefined AI Workflows vs. Autonomous Agents

Section 5: Building Agents from Scratch
- 01:08:16 The Core Loop: Perceive, Reason, Act
- 01:13:15 Hands-On Lab: Engineering the Standard `While` Loop
- 01:21:13 Planning Limits, Brittle Logic, & Mitigation Guardrails
- 01:26:19 Memory Managers: Sliding Windows & Vector DBs
- 01:33:56 Multi-Agent Frameworks & Inter-Agent Communication
- 01:43:08 Deconstructing Production Failures & Error Handling
- 01:52:20 Grounding Data & Prepackaged Frameworks (LangChain, Mastra)
- 02:00:25 Hands-On Lab: Building a Production Personal Assistant

Section 6: Deep Case Study (OpenClaw)
- 02:09:25 OpenClaw Codebase Tour & File Architecture
- 02:14:39 Custom Layouts, Terminal Interfaces, & Concurrency Locks
- 02:30:44 Testing Non-Deterministic Agents & LLM-as-a-Judge Evals
- 02:39:30 Observability, Tracking Performance Metrics, & Optimization Levers
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Build modern AI agents with Stanford's graduate-level curriculum, adapted for working professionals.

Taught by Stanford Adjunct Professor Chowdhery and Assistant Professor Mirhoseini, this Agentic AI program takes you beyond basic LLM prompting to agents that reason, plan, use tools, and improve themselves through interaction with their environment.

What you'll learn:
- Design agentic systems for complex, multi-step real-world tasks
- Apply test-time scaling to boost LLM performance
- Use self-improvement methods: verifiers, feedback loops, RL, and search
- Build agents that use tools and take actions effectively
- Add retrieval and long-term memory to LLMs
- Develop planning and multi-step reasoning
- Evaluate agent performance with robust frameworks

Drawing on Stanford research and the latest advances in the field.

About the Instructors
Stanford Engineering Adjunct Professor Dr. Aakanksha Chowdhery led end-to-end training of the 540B PaLM model (the largest densely trained language model in the world at the time) and drove pre-training and scaling of Gemini's mixture-of-experts models. Stanford Assistant Professor Azalia Mirhoseini co-developed the MoE architectures now used in nearly every frontier model, created AlphaChip (the RL method behind Google's TPU designs), and pioneered LLM test-time scaling; she worked on both Claude and Gemini.

Welcome to a tutorial on sockets with Python 3. We have a lot to cover, so let's just jump right in. The socket library is a part of the standard library, so you already have it.

Source code and text-based tutorial: https://pythonprogramming.net/....sockets-tutorial-pyt

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#sockets #python #tutorial