Understanding Langchain
Jeffery Owens, 2023

95% of Machine Learning solutions in the real world are for tabular data.

Not LLMs, not transformers, not agents, not fancy stuff.

Learning to do feature engineering and build tree-based models will open a ton of opportunities.

15 ways to start a conversation:
👇👇
https://t.me/englishlearnerspro/128

Artificial Intelligence with Python
Teik Toe Teoh, 2022

Machine Code for Beginners on the Amstrad
Steve Kramer, 1984

AI/ML roadmap

Topic: Mathematics

- Subtopic: Linear Algebra
- Vectors, Matrices, Eigenvalues and Eigenvectors
- Subtopic: Calculus
- Differentiation, Integration, Partial Derivatives
- Subtopic: Probability and Statistics
- Probability Theory, Random Variables, Statistical Inference

Topic: Programming

- Subtopic: Python
- Python Basics, Libraries like NumPy, Pandas, Matplotlib

Topic: Machine Learning

- Subtopic: Supervised Learning
- Linear Regression, Logistic Regression, Decision Trees
- Subtopic: Unsupervised Learning
- Clustering, Dimensionality Reduction[1](https://i.am.ai/roadmap)
- Subtopic: Neural Networks and Deep Learning
- Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks

Topic: Specializations

- Subtopic: Natural Language Processing
- Text Preprocessing, Topic Modeling, Word Embeddings
- Subtopic: Computer Vision
- Image Processing, Object Detection, Image Segmentation
- Subtopic: Reinforcement Learning
- Markov Decision Processes, Q-Learning, Policy Gradients

Join for more: https://t.me/machinelearning_deeplearning

If you're into deep learning, then you know that students usually one of the two paths:

- Computer vision
- Natural language processing (NLP)

If you're into NLP, here are 5 fundamental concepts you should know:
👇👇
https://t.me/generativeai_gpt/7

If I were to start Computer Science in 2023,

- Harvard - Stanford
- MIT - IBM - Telegram
- Microsoft - Google

❯ CS50 from Harvard
http://cs50.harvard.edu/x/2023/certificate/

❯ C/C++
http://ocw.mit.edu/courses/6-s096-effective-programming-in-c-and-c-january-iap-2014/

❯ Python
http://cs50.harvard.edu/python/2022/

https://t.me/dsabooks

❯ SQL
http://online.stanford.edu/courses/soe-ydatabases0005-databases-relational-databases-and-sql

https://t.me/sqlanalyst

❯ DSA
http://techdevguide.withgoogle.com/paths/data-structures-and-algorithms/

https://t.me/crackingthecodinginterview/290

❯ Java
http://learn.microsoft.com/shows/java-for-beginners/

https://t.me/Java_Programming_Notes

❯ JavaScript
http://learn.microsoft.com/training/paths/web-development-101/

https://t.me/javascript_courses

❯ TypeScript
http://learn.microsoft.com/training/paths/build-javascript-applications-typescript/

❯ C#
http://learn.microsoft.com/users/dotnet/collections/yz26f8y64n7k07

❯ Mathematics (incl. Statistics)
ocw.mit.edu/search/?d=Mathematics&s=department_course_numbers.sort_coursenum

❯ Data Science
cognitiveclass.ai/courses/data-science-101

https://t.me/datasciencefun/1141

❯ Machine Learning
http://developers.google.com/machine-learning/crash-course

❯ Deep Learning
introtodeeplearning.com

t.me/machinelearning_deeplearning/

❯ Full Stack Web (HTML/CSS)
pll.harvard.edu/course/cs50s-web-programming-python-and-javascript/2023-05

t.me/webdevcoursefree/594

❯ OS, Networking
ocw.mit.edu/courses/6-033-computer-system-engineering-spring-2018/

❯ Compiler Design
online.stanford.edu/courses/soe-ycscs1-compilers

Please give us credits while sharing: -> https://t.me/free4unow_backup

ENJOY LEARNING 👍👍

How do you start AI and ML ?

Where do you go to learn these skills? What courses are the best?

There’s no best answer🥺. Everyone’s path will be different. Some people learn better with books, others learn better through videos.

What’s more important than how you start is why you start.

Start with why.

Why do you want to learn these skills?
Do you want to make money?
Do you want to build things?
Do you want to make a difference?
Again, no right reason. All are valid in their own way.

Start with why because having a why is more important than how. Having a why means when it gets hard and it will get hard, you’ve got something to turn to. Something to remind you why you started.

Got a why? Good. Time for some hard skills.

I can only recommend what I’ve tried every week new course lauch better than others its difficult to recommend any course

I’ve completed courses from (in order):

Treehouse / youtube( free) - Introduction to Python

Udacity - Deep Learning & AI Nanodegree

Coursera - Deep Learning by Andrew Ng

fast.ai - Part 1and Part 2

They’re all world class. I’m a visual learner. I learn better seeing things being done/explained to me on. So all of these courses reflect that.

If you’re an absolute beginner, start with some introductory Python courses and when you’re a bit more confident, move into data science, machine learning and AI.

Join for more: https://t.me/machinelearning_deeplearning

👉Telegram Link: https://t.me/addlist/ID95piZJZa0wYzk5

Like for more ❤️

All the best 👍👍

Best Resource to Learn Artificial Intelligence (AI) For Free
👇👇

https://imp.i115008.net/qn27PL

https://i.am.ai/roadmap

https://bit.ly/3h97QpE

https://t.me/datasciencefun/1375

http://microsoft.github.io/AI-For-Beginners

https://ai.google/education/

Share with credits: https://t.me/free4unow_backup

ENJOY LEARNING 👍👍

Machine Code for Beginners on the Amstrad
Steve Kramer, 1984

LLM Building Training Hardware
Et Tu Code, 2023

Machine Learning: The Basics
Alexander Jung, 2023