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/
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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3. Build & Deploy Like a Pro🚀
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Join our session led by Satvik, the founder and an AI expert who has trained over 10,000 people!
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🎓 Build Your Career In Data Analytics! 📊
🌟 2000+ Students Placed
💰 7.4 LPA Average Package
🚀 41 LPA Highest Package
🤝 500+ Hiring Partners
Registration link: https://tracking.acciojob.com/g/PUfdDxgHR
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🌟 2000+ Students Placed
💰 7.4 LPA Average Package
🚀 41 LPA Highest Package
🤝 500+ Hiring Partners
Registration link: https://tracking.acciojob.com/g/PUfdDxgHR
Limited Seats, Register Now! ✨
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Top 7 Open-Source LLMs in 2025
1️⃣ DeepSeek R1
An open-source reasoning model excelling in logic, math, and decision-making.
2️⃣ Qwen2.5-72B
Alibaba’s 72B LLM optimized for coding, multilingual tasks, and structured data.
3️⃣ Llama 3.3
Meta’s multilingual LLM with strong dialogue, reasoning, and 128K context.
4️⃣ Mistral-Large
A 123B model excelling in reasoning, coding, and high factual accuracy.
5️⃣ Llama-3.1-70B
A robust instruction-tuned model for research, reasoning, and enterprise use.
6️⃣ Phi-4
Microsoft’s efficient small-scale model for programming and logical reasoning.
7️⃣ Gemma-2-9b-it
Google’s lightweight LLM for reasoning, summarization, and Q&A.
#llm
1️⃣ DeepSeek R1
An open-source reasoning model excelling in logic, math, and decision-making.
2️⃣ Qwen2.5-72B
Alibaba’s 72B LLM optimized for coding, multilingual tasks, and structured data.
3️⃣ Llama 3.3
Meta’s multilingual LLM with strong dialogue, reasoning, and 128K context.
4️⃣ Mistral-Large
A 123B model excelling in reasoning, coding, and high factual accuracy.
5️⃣ Llama-3.1-70B
A robust instruction-tuned model for research, reasoning, and enterprise use.
6️⃣ Phi-4
Microsoft’s efficient small-scale model for programming and logical reasoning.
7️⃣ Gemma-2-9b-it
Google’s lightweight LLM for reasoning, summarization, and Q&A.
#llm
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🗂 A collection of the good Gen AI free courses
🔹 Generative artificial intelligence
1️⃣ Generative AI for Beginners course : building generative artificial intelligence apps.
2️⃣ Generative AI Fundamentals course : getting to know the basic principles of generative artificial intelligence.
3️⃣ Intro to Gen AI course : from learning large language models to understanding the principles of responsible artificial intelligence.
4️⃣ Generative AI with LLMs course : Learn business applications of artificial intelligence with AWS experts in a practical way.
5️⃣ Generative AI for Everyone course : This course tells you what generative artificial intelligence is, how it works, and what uses and limitations it has.
🔹 Generative artificial intelligence
1️⃣ Generative AI for Beginners course : building generative artificial intelligence apps.
2️⃣ Generative AI Fundamentals course : getting to know the basic principles of generative artificial intelligence.
3️⃣ Intro to Gen AI course : from learning large language models to understanding the principles of responsible artificial intelligence.
4️⃣ Generative AI with LLMs course : Learn business applications of artificial intelligence with AWS experts in a practical way.
5️⃣ Generative AI for Everyone course : This course tells you what generative artificial intelligence is, how it works, and what uses and limitations it has.
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⏺️ 7 Free AI Courses for High-Paying Careers
🔹 Build Your First Chatbot Using IBM :- Course Link Click Here
Create AI chatbots with IBM watsonx and NLP basics.
🔹 DeepMind x UCL | Deep Learning :- Course Link Click Here
Learn Deep Learning fundamentals from DeepMind experts.
🔹 Machine Learning Crash Course :- Course Link Click Here
Google's hands-on intro to machine learning.
🔹 Neural networks:- Course Link Click Here
Understand neural networks and their AI applications.
🔹 Applied Machine Learning in Python:- Course Link Click Here
Practical ML techniques using scikit-learn.
🔹 Machine Learning Specialization:- Course Link Click Here
Stanford ML fundamentals course.
🔹 Computer Vision and Image Processing:- Course Link Click Here
Hands-on computer vision with Python & OpenCV.
🎁 Bonus: 🔴 Build an AI Agent in NEXT.JS 15!
Learn to integrate LangChain, Clerk, Convex, TS & IBM in AI-powered apps. - Video Link
🔹 Build Your First Chatbot Using IBM :- Course Link Click Here
Create AI chatbots with IBM watsonx and NLP basics.
🔹 DeepMind x UCL | Deep Learning :- Course Link Click Here
Learn Deep Learning fundamentals from DeepMind experts.
🔹 Machine Learning Crash Course :- Course Link Click Here
Google's hands-on intro to machine learning.
🔹 Neural networks:- Course Link Click Here
Understand neural networks and their AI applications.
🔹 Applied Machine Learning in Python:- Course Link Click Here
Practical ML techniques using scikit-learn.
🔹 Machine Learning Specialization:- Course Link Click Here
Stanford ML fundamentals course.
🔹 Computer Vision and Image Processing:- Course Link Click Here
Hands-on computer vision with Python & OpenCV.
🎁 Bonus: 🔴 Build an AI Agent in NEXT.JS 15!
Learn to integrate LangChain, Clerk, Convex, TS & IBM in AI-powered apps. - Video Link
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10 Things you need to become an AI/ML engineer:
1. Framing machine learning problems
2. Weak supervision and active learning
3. Processing, training, deploying, inference pipelines
4. Offline evaluation and testing in production
5. Performing error analysis. Where to work next
6. Distributed training. Data and model parallelism
7. Pruning, quantization, and knowledge distillation
8. Serving predictions. Online and batch inference
9. Monitoring models and data distribution shifts
10. Automatic retraining and evaluation of models
1. Framing machine learning problems
2. Weak supervision and active learning
3. Processing, training, deploying, inference pipelines
4. Offline evaluation and testing in production
5. Performing error analysis. Where to work next
6. Distributed training. Data and model parallelism
7. Pruning, quantization, and knowledge distillation
8. Serving predictions. Online and batch inference
9. Monitoring models and data distribution shifts
10. Automatic retraining and evaluation of models
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Data science is a multidisciplinary field that combines techniques from statistics, computer science, and domain-specific knowledge to extract insights and knowledge from data. Here are some essential concepts in data science:
1. Data Collection: The process of gathering data from various sources, such as databases, files, sensors, and APIs.
2. Data Cleaning: The process of identifying and correcting errors, missing values, and inconsistencies in the data.
3. Data Exploration: The process of summarizing and visualizing the data to understand its characteristics and relationships.
4. Data Preprocessing: The process of transforming and preparing the data for analysis, including feature selection, normalization, and encoding.
5. Machine Learning: A subset of artificial intelligence that uses algorithms to learn patterns and make predictions from data.
6. Statistical Analysis: The use of statistical methods to analyze and interpret data, including hypothesis testing, regression analysis, and clustering.
7. Data Visualization: The graphical representation of data to communicate insights and findings effectively.
8. Model Evaluation: The process of assessing the performance of a predictive model using metrics such as accuracy, precision, recall, and F1 score.
9. Feature Engineering: The process of creating new features or transforming existing features to improve the performance of machine learning models.
10. Big Data: The term used to describe large and complex datasets that require specialized tools and techniques for analysis.
These concepts are foundational to the practice of data science and are essential for extracting valuable insights from data.
Join for more: https://t.me/datasciencefun
ENJOY LEARNING 👍👍
1. Data Collection: The process of gathering data from various sources, such as databases, files, sensors, and APIs.
2. Data Cleaning: The process of identifying and correcting errors, missing values, and inconsistencies in the data.
3. Data Exploration: The process of summarizing and visualizing the data to understand its characteristics and relationships.
4. Data Preprocessing: The process of transforming and preparing the data for analysis, including feature selection, normalization, and encoding.
5. Machine Learning: A subset of artificial intelligence that uses algorithms to learn patterns and make predictions from data.
6. Statistical Analysis: The use of statistical methods to analyze and interpret data, including hypothesis testing, regression analysis, and clustering.
7. Data Visualization: The graphical representation of data to communicate insights and findings effectively.
8. Model Evaluation: The process of assessing the performance of a predictive model using metrics such as accuracy, precision, recall, and F1 score.
9. Feature Engineering: The process of creating new features or transforming existing features to improve the performance of machine learning models.
10. Big Data: The term used to describe large and complex datasets that require specialized tools and techniques for analysis.
These concepts are foundational to the practice of data science and are essential for extracting valuable insights from data.
Join for more: https://t.me/datasciencefun
ENJOY LEARNING 👍👍
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🔗 Mistral AI releases Mistral Small 3.1
It outperforms comparable models like Gemma 3 and GPT-4o mini
- It builds upon Mistral Small 3 with improved text performance
- Supports multimodal understanding capabilities
- It has an expanded context window of up to 128k tokens
- Achieves inference speeds of 150 tokens per second
It is described as the first open-source model to surpass leading small proprietary models in text, multimodal, multilingual processing, long-context handling, low latency, and cost efficiency.
It outperforms comparable models like Gemma 3 and GPT-4o mini
- It builds upon Mistral Small 3 with improved text performance
- Supports multimodal understanding capabilities
- It has an expanded context window of up to 128k tokens
- Achieves inference speeds of 150 tokens per second
It is described as the first open-source model to surpass leading small proprietary models in text, multimodal, multilingual processing, long-context handling, low latency, and cost efficiency.
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