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Admin: @PranavReal Free Resources for: 📌 Artificial Intelligence 📌 Machine Learning 📌 Deep Learning 📌 Data Science 📌 Python Programming
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Дописи каналу
| 2 | Mastering Numerical Computing With NumPy | 1 602 |
| 3 | The Art of Clean Code | 1 524 |
| 4 | The Secrets of AI Value Creation.pdf | 1 536 |
| 5 | Machine Learning for Everyone | 1 442 |
| 6 | Practical Guide to Matplotlib for Data Science.pdf | 1 316 |
| 7 | YouTube Playlists to Learn Machine Learning and Deep Learning.
YouTube Playlists by Krish Naik --->
Machine Learning
Deep Learning
YouTube Playlists by Codebasics --->
Machine Learning
Deep Learning
YouTube Playlists by Andrew Ng --->
Machine Learning
Deep Learning
YouTube Playlists by Sentdex --->
Machine Learning
Deep Learning
YouTube Playlists by Simplilearn --->
Machine Learning
Deep Learning
YouTube Playlists by edureka! --->
Machine Learning
Deep Learning
YouTube Playlists by FreeCodeCamp --->
ML+DL
👉 Join @Artificial_Intelligence_DS for More | 1 277 |
| 8 |  🔉 𝐌𝐮𝐬𝐭-𝐖𝐚𝐭𝐜𝐡 𝐀𝐈 𝐓𝐞𝐝 𝐓𝐚𝐥𝐤𝐬
⏩ The inside story of ChatGPT's astonishing potential by Greg Brockman. https://youtu.be/C_78DM8fG6E?si=kdGNA1PvO1lb7L8t
⏩ How AI could save (not destroy) education by Sal Khan
https://youtu.be/hJP5GqnTrNo?si=wlD-SOjr5ZxLQ0vQ
⏩ How to keep AI under control by Max Tegmark
https://youtu.be/xUNx_PxNHrY?si=e8JDz9up3IRYmBo5
⏩ How to think computationally about AI, the universe, and everything by Stephen Wolfram
https://youtu.be/fLMZAHyrpyo?si=5O1b63qgga89rEOb
⏩ The dark side of competition in AI by Liv Boeree
https://youtu.be/WX_vN1QYgmE?si=QDMlKkrxqrSCdFkr
⏩ How AI art could enhance humanity's collective memory by Refik Anadol
https://youtu.be/iz7diOuaTos?si=iyQOF20jZp78hfo2
⏩ Why AI is incredibly smart and shockingly stupid by Yejin Choil
https://youtu.be/SvBR0OGT5VI?si=rLhDzmohC_dPfrtM
⏩ Will superintelligent AI end the world by Eliezer Yudkowsky
https://youtu.be/Yd0yQ9yxSYY?si=JqN2yNgP0IOTnjN1
⏩ Kai-Fu Lee: How AI can save our humanity | TED Talk
https://bit.ly/44SC1ss | 1 363 |
| 9 | ❗Generative AI is EVERYWHERE right now.
So I've searched the internet for the best 10 FREE digital trainings available to really get started with #GenAI.
🔍 Beginner Level Courses:
1. Fundamentals of Generative AI for Beginners with Curtis Morton on Coursera - https://www.coursera.org/learn/aws-generative-ai-for-beginners
2. Introduction to Generative AI by Google Cloud - https://www.cloudskillsboost.google/course_templates/536
3. Generative AI for Everyone with Andrew Ng on Coursera - https://www.coursera.org/learn/generative-ai-for-everyone
4. Snowflake Data Academy for Generative AI on Snowflake - https://www.snowflake.com/data-cloud-academy-generative-ai-llm/
5. Foundations of Prompt Engineer on AWS SkillBuilder (FREE) - https://explore.skillbuilder.aws/learn/course/external/view/elearning/17763/foundations-of-prompt-engineering
6. Introduction to Large Language Models (LLMs)* with Google on Coursera - https://www.coursera.org/learn/introduction-to-large-language-models?action=enroll
🛑 Intermediate/Advanced Level Courses:
1. Encoder - Decoder Architecture with Google* on Coursera - https://www.coursera.org/learn/encoder-decoder-architecture
2. Attention Mechanisms with Google* on Coursera - https://www.coursera.org/learn/attention-mechanism
3. Probabalistic Deep Learning* with TensorFlow and Imperial College London on Coursera - https://www.coursera.org/learn/probabilistic-deep-learning-with-tensorflow2
4. Generative AI with Large Language Models* with DeepLearning.AI on Coursera - Introduction to Large Language Models (LLMs) with Google on Coursera - https://www.coursera.org/learn/introduction-to-large-language-models
*Any course I've marked with an * can be accessed as a part of a free trial - so please pick wisely! Anything without the * is free of charge and should be consumed!
Oftentimes, Coursera will allow you to take these courses FREE as an audit of the content.
♻ Did you find this post helpful? Don't gate keep - share it with your network!
Join the Ai India Community! (https://linktr.ee/AiCommunity) | 3 420 |
| 10 | 🖥 Roadmap of free courses for learning Python and Machine learning.
▪Data Science
▪ AI/ML
▪ Web Dev
1. Start with this https://kaggle.com/learn/python
2. Take any one of these
❯ https://openclassrooms.com/courses/6900856-learn-programming-with-python
❯ https://t.me/pythondevelopersindia/76
❯ https://simplilearn.com/learn-python-basics-free-course-skillup
3. Then take this
https://netacad.com/courses/programming/pcap-programming-essentials-python
4. Attempt for this certification
https://freecodecamp.org/learn/scientific-computing-with-python/
5. Take it to next level
❯ Data Scrapping, NumPy, Pandas
https://scaler.com/topics/course/python-for-data-science/
❯ Data Analysis
https://openclassrooms.com/courses/2304731-learn-python-basics-for-data-analysis
❯ Data Visualization
https://kaggle.com/learn/data-visualization
❯ Django
https://openclassrooms.com/courses/6967196-create-a-web-application-with-django
❯ Machine Learning
http://developers.google.com/machine-learning/crash-course
https://t.me/datasciencefun/290
❯ Deep Learning (TensorFlow)
http://kaggle.com/learn/intro-to-deep-learning | 4 581 |
| 11 | 🚦Top 10 Data Science Tools🚦
Here we will examine the top best Data Science tools that are utilized generally by data researchers and analysts. But prior to beginning let us discuss about what is Data Science.
🛰What is Data Science ?
Data science is a quickly developing field that includes the utilization of logical strategies, calculations, and frameworks to extract experiences and information from organized and unstructured data .
🗽Top Data Science Tools that are normally utilized :
1.) Jupyter Notebook : Jupyter Notebook is an open-source web application that permits clients to make and share archives that contain live code, conditions, representations, and narrative text .
2.) Keras : Keras is a famous open-source brain network library utilized in data science. It is known for its usability and adaptability.
Keras provides a range of tools and techniques for dealing with common data science problems, such as overfitting, underfitting, and regularization.
3.) PyTorch : PyTorch is one more famous open-source AI library utilized in information science. PyTorch also offers easy-to-use interfaces for various tasks such as data loading, model building, training, and deployment, making it accessible to beginners as well as experts in the field of machine learning.
4.) TensorFlow : TensorFlow allows data researchers to play out an extensive variety of AI errands, for example, image recognition , natural language processing , and deep learning.
5.) Spark : Spark allows data researchers to perform data processing tasks like data control, investigation, and machine learning , rapidly and effectively.
6.) Hadoop : Hadoop provides a distributed file system (HDFS) and a distributed processing framework (MapReduce) that permits data researchers to handle enormous datasets rapidly.
7.) Tableau : Tableau is a strong data representation tool that permits data researchers to make intuitive dashboards and perceptions. Tableau allows users to combine multiple charts.
8.) SQL : SQL (Structured Query Language) SQL permits data researchers to perform complex queries , join tables, and aggregate data, making it simple to extricate bits of knowledge from enormous datasets. It is a powerful tool for data management, especially for large datasets.
9.) Power BI : Power BI is a business examination tool that conveys experiences and permits clients to make intuitive representations and reports without any problem.
10.) Excel : Excel is a spreadsheet program that broadly utilized in data science. It is an amazing asset for information the board, examination, and visualization .Excel can be used to explore the data by creating pivot tables, histograms, scatterplots, and other types of visualizations. | 3 453 |
| 12 | How to build your career in the Ai (2).pdf | 2 645 |
| 13 |  How to Build Your Career in Artificial Intelligence by Andrew Ng!
This books includes:
✅ Steps to Career Growth.
✅ Learning Technical Skills for a Promising AI Career.
✅ Should You Learn Math to Get a Job in AI?
✅ Finding Projects that Complement
✅ Building a Portfolio of Projects that Shows Skill Progression.
✅ A Simple Framework for Starting Your AI Job Search.
✅ Finding the Right AI Job for You.
Good read for beginners 🚀 | 2 967 |
| 14 | Немає тексту... | 4 875 |
| 15 | +1 Machine Learning Notes - TutorialsDuniya.pdf | 5 292 |
| 16 | Machine Learning Handwritten Notes 👇 | 5 518 |
| 17 | Here are 300 hours of curated courses on Machine Learning for Free.
Topics Covered:
• Fundamentals of Machine Learning
• Feature Engineering
• Production Machine Learning Systems
• Computer Vision and Natural Language
• Recommendation Systems
• MLOps
• TensorFlow, Google Cloud, VertexAI
There are 15 courses.
From beginner to advanced.
From Google.
For free.
https://www.cloudskillsboost.google/paths/17 | 6 459 |
| 18 | Here are 300 hours of curated courses on Machine Learning for Free.
Topics Covered:
• Fundamentals of Machine Learning
• Feature Engineering
• Production Machine Learning Systems
• Computer Vision and Natural Language
• Recommendation Systems
• MLOps
• TensorFlow, Google Cloud, VertexAI
There are 15 courses.
From beginner to advanced.
From Google.
For free.
https://www.cloudskillsboost.google/paths/17 | 1 |
| 19 | Have you spent a lot of time taking online courses🧑🏼💻 but still find it hard to solve problems🤯 in the real world?
Want to create a rock-solid portfolio🏋️ of Data Science & Machine Learning projects?
We know that completing courses is a great starting point, but it's only half the battle🏆. That's why we've created something that bridges the gap between theoretical knowledge 📚and real-life projects👨💼.
🌟 Introducing TensorHulk's Machine Learning Megapack 🌟
A power-packed megapack including,
🚀 200+ Expertly Crafted Real-world projects,
🎓 Interview Preparation Kit(1200+ Interview Q&A asked in MAANG Companies),
📝 Hand-on-tutorial notebooks.
Benefits:
✴️ Up-to-date(2023 ready) and error-free projects
✴️ Dedicated email support for doubt-solving
✴️ Lifetime access to all the projects
Ace Your Next Data Science Interview With Confidence!!!
Visit : https://tensorhulk.in/machine-learning-megapack/ | 750 |
| 20 | Немає тексту... | 719 |
