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://news.1rj.ru/str/machinelearning_deeplearning
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://news.1rj.ru/str/machinelearning_deeplearning
👍12
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://news.1rj.ru/str/generativeai_gpt/7
- Computer vision
- Natural language processing (NLP)
If you're into NLP, here are 5 fundamental concepts you should know:
👇👇
https://news.1rj.ru/str/generativeai_gpt/7
👍1
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://news.1rj.ru/str/dsabooks
❯ SQL
http://online.stanford.edu/courses/soe-ydatabases0005-databases-relational-databases-and-sql
https://news.1rj.ru/str/sqlanalyst
❯ DSA
http://techdevguide.withgoogle.com/paths/data-structures-and-algorithms/
https://news.1rj.ru/str/crackingthecodinginterview/290
❯ Java
http://learn.microsoft.com/shows/java-for-beginners/
https://news.1rj.ru/str/Java_Programming_Notes
❯ JavaScript
http://learn.microsoft.com/training/paths/web-development-101/
https://news.1rj.ru/str/javanoscript_courses
❯ TypeScript
http://learn.microsoft.com/training/paths/build-javanoscript-applications-typenoscript/
❯ 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://news.1rj.ru/str/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-javanoscript/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://news.1rj.ru/str/free4unow_backup
ENJOY LEARNING 👍👍
- 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://news.1rj.ru/str/dsabooks
❯ SQL
http://online.stanford.edu/courses/soe-ydatabases0005-databases-relational-databases-and-sql
https://news.1rj.ru/str/sqlanalyst
❯ DSA
http://techdevguide.withgoogle.com/paths/data-structures-and-algorithms/
https://news.1rj.ru/str/crackingthecodinginterview/290
❯ Java
http://learn.microsoft.com/shows/java-for-beginners/
https://news.1rj.ru/str/Java_Programming_Notes
❯ JavaScript
http://learn.microsoft.com/training/paths/web-development-101/
https://news.1rj.ru/str/javanoscript_courses
❯ TypeScript
http://learn.microsoft.com/training/paths/build-javanoscript-applications-typenoscript/
❯ 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://news.1rj.ru/str/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-javanoscript/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://news.1rj.ru/str/free4unow_backup
ENJOY LEARNING 👍👍
👍13❤5
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://news.1rj.ru/str/machinelearning_deeplearning
👉Telegram Link: https://news.1rj.ru/str/addlist/ID95piZJZa0wYzk5
Like for more ❤️
All the best 👍👍
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://news.1rj.ru/str/machinelearning_deeplearning
👉Telegram Link: https://news.1rj.ru/str/addlist/ID95piZJZa0wYzk5
Like for more ❤️
All the best 👍👍
👍8❤3
Best Resource to Learn Artificial Intelligence (AI) For Free
👇👇
https://imp.i115008.net/qn27PL
https://i.am.ai/roadmap
https://bit.ly/3h97QpE
https://news.1rj.ru/str/datasciencefun/1375
http://microsoft.github.io/AI-For-Beginners
https://ai.google/education/
Share with credits: https://news.1rj.ru/str/free4unow_backup
ENJOY LEARNING 👍👍
👇👇
https://imp.i115008.net/qn27PL
https://i.am.ai/roadmap
https://bit.ly/3h97QpE
https://news.1rj.ru/str/datasciencefun/1375
http://microsoft.github.io/AI-For-Beginners
https://ai.google/education/
Share with credits: https://news.1rj.ru/str/free4unow_backup
ENJOY LEARNING 👍👍
👍4
Machine Code for Beginners on the Amstrad 1984.pdf
85.1 MB
Machine Code for Beginners on the Amstrad
Steve Kramer, 1984
Steve Kramer, 1984
👍3❤1🔥1
Applied Generative AI for Beginners.pdf
7.9 MB
Applied Generative AI for Beginners
Akshay Kulkarni, 2023
Akshay Kulkarni, 2023
👍3🔥1
Et_Tu_Code_Building,_Training_and_Hardware_for_LLM_AI_A_Comprehensive.pdf
59.3 MB
LLM Building Training Hardware
Et Tu Code, 2023
Et Tu Code, 2023
👍7🔥1
HAI_AI-Index-Report-2024.pdf
42.3 MB
Artificial Intelligence Index Report 2024
Ray Perrault, 2024
Ray Perrault, 2024
👍7🔥1
Applied Causal Inference Powered by ML and AI, 2024.pdf
15.3 MB
Applied Causal Inference Powered by ML and AI
Victor Chernozhukov, 2024
Victor Chernozhukov, 2024
👍9🔥1
Artificial Intelligence and Chatbots 101 (2024).pdf
1.3 MB
📚 Title: Artificial Intelligence and Chatbots 101 (2024)
Join for more: https://news.1rj.ru/str/machinelearning_deeplearning
Join for more: https://news.1rj.ru/str/machinelearning_deeplearning
👍9❤1🔥1