Google, Harvard, and even OpenAI are offering FREE Generative AI courses (no payment required) 🎓

Here are 8 FREE courses to master AI in 2024:

1. Google AI Courses
5 courses covering generative AI from the ground up
https://www.cloudskillsboost.google/paths/118

2. Microsoft AI Course
Basics of AI, neural networks, and deep learning
https://microsoft.github.io/AI-For-Beginners/

3. Introduction to AI with Python (Harvard)
7-week course exploring AI concepts and algorithms
https://www.edx.org/learn/artificial-intelligence/harvard-university-cs50-s-introduction-to-artificial-intelligence-with-python

4. ChatGPT Prompt Engineering for Devs (OpenAI & DeepLearning)
Best practices and hands-on prompting experience
https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/

5. LLMOps (Google Cloud & DeepLearning)
Learn the LLMOps pipeline and deploy custom LLMs
https://www.deeplearning.ai/short-courses/llmops/

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🔗 Download this book

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Best Free SQL Courses to Get Started

1) Introduction to Databases and SQL
2) Advanced Database and SQL
3) Learn SQL 
4) SQL Tutorial

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Here are 20 essential VS Code shortcuts for beginners:

1. Ctrl + P: Open any file quickly 📂

2. Ctrl + /: Toggle line comment 📝

3. Alt + Up/Down: Move a line up or down ↕️

4. Ctrl + Shift + K: Delete the current line ❌

5. Ctrl + B: Show/hide the sidebar 📚

6. Ctrl + Space: Trigger IntelliSense for code suggestions 💡

7. Ctrl + Shift + F: Search across files 🔍

8. Ctrl + D: Select the next occurrence of the selected text 📑

9. Ctrl + Shift + L: Select all occurrences of the current selection 🔗

10. Ctrl + Shift + P: Open the Command Palette 📜

11. Ctrl + F2: Rename all occurrences of a variable ✏️

12. Ctrl + J: Show/hide the integrated terminal 💻

13. Ctrl + `: Open a new terminal 🔧

14. Ctrl + Shift + N: Open a new window 🖼️

15. Ctrl + W: Close the current editor tab 🗂️

16. Ctrl + Shift + E: Focus on the file explorer 🗃️

17. Ctrl + Shift + G: Open the Git view 🔄

18. Ctrl + Shift + M: Open the Problems panel 🚨

19. Alt + Shift + Up/Down: Copy the line up or down 📋

20. Ctrl + Alt + Arrow keys: Split the editor window ✂️


Master these and level up your coding speed! 🚀

SoftBank is on the verge of finalizing a *$40 billion investment in OpenAI,* pushing its valuation to $300 billion, according to sources. This deal will make SoftBank the largest backer of OpenAI, overtaking Microsoft, which had previously led investments in the AI powerhouse. The funding will be distributed over the next 12 to 24 months, with the first tranche expected as early as spring.

A significant portion of the investment is earmarked for Stargate, an ambitious AI infrastructure project backed by OpenAI, Oracle, and SoftBank, announced by Donald Trump earlier this year. The initiative aims to strengthen U.S. AI capabilities, injecting billions into AI research and deployment.

OpenAI, under the leadership of Sam Altman, remains at the forefront of the generative AI revolution, competing with industry giants such as Elon Musk’s xAI, Google, Amazon, Meta, and Anthropic. The company continues to expand its reach with strategic partnerships, this high-stakes investment signals a new era of AI dominance.

🖥 OpenAI will develop Al-specific hardware, CEO Sam Altman says

OpenAI CEO Sam Altman revealed plans to develop AI-specific hardware and custom semiconductors, marking a major shift in tech. The company aims to collaborate with former Apple design chief Jony Ive to create new AI-focused devices, believing AI will transform human-computer interaction.

OpenAI is also working on its own chips, joining tech giants like Apple, Google, and Amazon in optimizing AI performance. However, the first prototype is expected to take several years, with voice interaction being a key feature, similar to how the iPhone revolutionized user interfaces with touchscreens.

🚨 OpenAI introduces ChatGPT Gov

OpenAI has launched ChatGPT Gov, a version of its ChatGPT platform designed for U.S. government agencies to improve efficiency and productivity. This specialized platform allows agencies to securely access advanced models like GPT-4o within their Microsoft Azure environments, ensuring compliance with security and privacy standards.

Key features include the ability to save and share conversations, upload files, create custom GPTs, and manage IT through an administrative console. Since 2024, over 90,000 users from more than 3,500 government agencies have utilized ChatGPT for tasks such as enhancing access to resources and supporting AI education. Notable users include the Air Force Research Laboratory and Los Alamos National Laboratory.

OpenAI is also pursuing FedRAMP Moderate and High accreditations for ChatGPT Enterprise and is exploring expansion into Azure’s classified regions, emphasizing its commitment to responsible AI use in government.

Here are 25 most common ML interview screening questions for each category:


1. Machine Learning fundamentals:
- Explain the difference between supervised, unsupervised, and reinforcement learning. Provide an example for each.
- What is the bias-variance tradeoff? How does it affect model performance?
- Describe the process of cross-validation. Why is it important in model evaluation?
- What is overfitting, and how can you prevent it in your models?
- Explain the concept of ensemble learning. What are bagging and boosting?

2. Statistics and Probability:
- Explain the difference between frequentist and Bayesian approaches in statistics.
- What is the Central Limit Theorem, and why is it important in machine learning?
- Describe the concept of hypothesis testing and its application in A/B testing.
- What is maximum likelihood estimation? Provide an example of its use in machine learning.
- Explain the difference between correlation and causation. How does this impact model interpretation?

3. Model Evaluation and Deployment:
- What metrics would you use to evaluate a classification model? How do they differ for balanced vs. imbalanced datasets?
- Describe the process of deploying a machine learning model in a production environment.
- What is A/B testing in the context of machine learning models? How would you design an A/B test?
- Explain the concept of model drift. How can it be detected and mitigated?
- What are the key considerations when scaling a machine learning system to handle large amounts of data or traffic?

4. Python for Machine Learning:
- How would you handle missing data in a pandas DataFrame?
- Explain the difference between a list and a numpy array in Python. When would you use one over the other?
- What are lambda functions in Python? Provide an example of how they can be used in data processing.
- Describe the purpose of the scikit-learn library. How would you use it to implement a simple classification model?
- What is the difference between *args and **kwargs in Python? How might they be useful in creating flexible ML functions?

5. Data Preprocessing:
- What is feature scaling, and why is it important? Describe different methods of feature scaling.
- How do you handle categorical variables in machine learning models? Explain one-hot encoding and label encoding.
- What is dimensionality reduction? Describe PCA (Principal Component Analysis) and its applications.
- How do you deal with imbalanced datasets? Discuss various techniques to address this issue.
- What is feature selection? Describe a few methods for selecting the most important features for a model.

So accurate

This is a class from Harvard University:

"Introduction to Data Science with Python."

It's free. You should be familiar with Python to take this course.

The course is for beginners. It's for those who want to build a fundamental understanding of machine learning and artificial intelligence.

It covers some of these topics:

• Generalization and overfitting
• Model building, regularization, and evaluation
• Linear and logistic regression models
• k-Nearest Neighbor
• Scikit-Learn, NumPy, Pandas, and Matplotlib

Link: https://pll.harvard.edu/course/introduction-data-science-python

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🔥 Want to learn from one of the world’s top universities?

Now’s your chance!🔗

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Start Learning Now✅️

SQL Mindmap

Forget Midjourney, It costs $8/M to use.

Here are 5 alternatives to you use

1. Bing Image Creator :

Simply type your ideas into Bing which will create one-of-a-kind images from text.

Platforms: Web, Android, iOS

2. Lexica

It acts as a search engine for Stable Diffusion-generated images.

Features:

- Generate 100 images per month for free.

- Effortless to use

Platform: Web

3. Stable Diffusion

It is a free and open-source model.

- Powerful capabilities

- Trained on large dataset

Platforms: Web

4. Adobe Firefly

- In-house text-to-image model by Adobe.

- Has promising features like image expansion and sketch-to-image.

- 💯 Free

Platforms: Web

5. Playground AI

- Generate 1000 images per day for free.

- Stable Diffusion and DALL·E are available.

Platforms: Web

Join for more: https://news.1rj.ru/str/AI_Best_Tools

ENJOY LEARNING 👍👍