20+ AI Online Courses From Top Universities

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In Today’s Newsletter

1. 13 Free AI Courses From Top Universities

2. SQL #3 - GROUP BY & HAVING + FREE PDF

3. AI & Tech News

20 Free AI Courses From Google, IBM, & Deeplearning AI

• Machine Learning by Stanford University (Andrew Ng)
  Learn the fundamentals of machine learning—including supervised and unsupervised techniques—from one of the field’s pioneers.
  

• IBM Machine Learning Professional Certificate
  Develop practical machine learning skills and work on real-world projects through a series of hands-on courses by IBM.
  

• Deep Learning Specialization
  Dive deep into deep learning concepts such as neural networks, convolutional networks, and sequence models across a multi-course specialization.
  

• AI For Everyone by Andrew Ng
  A non-technical introduction to AI, this course explains what AI can—and cannot—do for your organization and career.
  

• Generative AI for Everyone
  Explore the basics of generative AI, its applications, and its ethical implications in an accessible format for a broad audience.
  

• Computer Vision Basics by the University at Buffalo
  Get introduced to the core concepts of computer vision and image processing with practical, hands-on examples.
  

• Generative Adversarial Networks (GANs) Specialization 
  Understand and build GANs, a key deep learning model for generating realistic images and media, through a series of focused courses.
  

• Applied Data Science with Python Specialization by the University of Michigan
  Broaden your data science skills with Python, covering topics like data visualization, machine learning, and applied statistics.
  

• Machine Learning with Python by IBM
  Learn to implement and apply machine learning techniques using Python in this practical, project-focused course.
  

More courses related to AI

1. Neural networks | Machine Learning | Google for Developers​

2. Introduction to Computer Vision and Image Processing | Coursera​

3. Machine Learning Crash Course | Google for Developers​

4. Intro to PyTorch and Neural Networks | Codecademy​

5. Machine Learning Specialization | Coursera​

6. Explore Text to Speech Using IBM watson® | IBM SkillsBuild​

7. Applied Machine Learning in Python | Coursera​

8. Deep Learning Lecture Series 2020 (DeepMind and UCL)​

9. Summarizing Data Using IBM Granite | IBM SkillsBuild​

10. Build Your First Chatbot ​

11. Use Generative AI for Software Development Using IBM watsonx™ | IBM SkillsBuild​

12. IBM Granite Models for Software Development | IBM SkillsBuild​

SQL - Part #3 (GROUP BY & HAVING)

GROUP BY and HAVING Clause in SQL (Quick Learn ⤵️ )

The GROUP BY clause groups identical data into sets. It works with aggregate functions like SUM(), COUNT(), AVG(), MAX(), and MIN() to perform calculations on grouped data.

Syntax:

SELECT column_name, aggregate_function(column_name)
FROM table_name
GROUP BY column_name;

✅ Key Points:
Groups data based on one or more columns.
Must be used with aggregate functions.
Columns in SELECT must either be in GROUP BY or used in an aggregate function.

HAVING vs WHERE
WHERE filters before grouping.
HAVING filters after aggregation.

Syntax:

SELECT column_name, aggregate_function(column_name)
FROM table_name
GROUP BY column_name
HAVING condition;

✅ When to Use GROUP BY?

• When aggregating data (totals, averages, counts, etc.).

• When summarizing large datasets into meaningful groups.

• When filtering aggregate results using HAVING.
  

🔥 Advanced Use Cases

• Find top-selling products

• Customers who placed more than 10 orders

• Total revenue per year
  

💡 Pro Tips

• Always include non-aggregated columns in GROUP BY.

• Use WHERE for filtering before aggregation and HAVING for filtering after.

• Index grouped columns for better performance.

• Use ROLLUP for subtotals and CUBE for multi-level grouping.

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AI & Tech News

#1. Nvidia Uses AI to Solve Grid Problems—Caused by AI

Nvidia is partnering with EPRI and major utilities to use AI in tackling electrical grid issues—ironically driven by AI’s rising power demands. The Open Power AI Consortium will develop open-source AI models to help the energy industry handle increasing electricity needs.

With U.S. electricity demand growing 4% annually, companies like Microsoft and Oracle are securing renewable energy sources. However, AI-driven demand management—like shifting tasks during off-peak hours—could free up 76 GW of capacity, about 10% of peak U.S. demand.

#2. Google AI Solves 10-Year Science Problem in Two Days (Source)

Google’s new AI tool solved a decade-old scientific mystery in just two days, a breakthrough researchers call “game-changing” for disease prevention.

Scientists at Imperial College London tested the AI on how superbugs gain antibiotic resistance. The AI analyzed data, designed experiments, and proposed the correct hypothesis—a process that took human researchers years.

Experts believe this AI “co-scientist” will speed up discoveries by eliminating dead-end experiments and focusing on key breakthroughs. While human testing is still needed, the AI could revolutionize biomedical research, starting with antimicrobial resistance (AMR).

#3. The Singularity Could Arrive Sooner Than Expected – Experts Predict Within 12 Months (Source)

The rise of artificial general intelligence (AGI) is closer than we think. Some researchers believe AGI and the singularity—the point where AI surpasses human intelligence—could arrive by 2026, while others predict it may take a few more decades.

A new macro analysis of over 8,500 expert predictions shows that opinions have shifted dramatically since the rise of large language models (LLMs) like ChatGPT.

Before LLMs, scientists predicted AGI would emerge around 2060—now, estimates range from 2030 to as early as next year.

AI capabilities double every 18 months, and if classical computing hits a limit, quantum computing could push AGI forward. Some argue AGI can never fully replicate human intelligence, which includes emotions, creativity, and social reasoning. While AI enhances research, human intuition remains crucial for breakthroughs.

Why You Should Ignore 99% of AI Tools (and the 5 You Actually Need)

The AI industry moves at lightning speed, with new tools launching every second. Keeping up is overwhelming—but the truth is, most AI tools aren’t worth your time.

Instead of chasing trends, focus on solving real problems. Identify tedious tasks in your workflow, test a few AI tools, and stick with what actually saves time.

The 5 AI Tools Worth Using Daily:

✅ ChatGPT – A creative assistant for brainstorming and generating ideas.
✅ Perplexity – A powerful research tool that often replaces Google.
✅ Grammarly – A writing assistant for grammar, spelling, and editing.
✅ Canva – A go-to for quick and easy design work.
✅ InVideo – A simple yet effective AI-powered video creation tool. Turn your thoughts into Faceless videos, text - to -video. It even add the background music, subtitles and also prepare a complete video from a script or a simple prompt.

Thanks for your time and consideration!

Shailesh & NextCareerStep team