๐ A collection of the good Gen AI free courses
๐น Generative artificial intelligence
1๏ธโฃ Generative AI for Beginners course : building generative artificial intelligence apps.
2๏ธโฃ Generative AI Fundamentals course : getting to know the basic principles of generative artificial intelligence.
3๏ธโฃ Intro to Gen AI course : from learning large language models to understanding the principles of responsible artificial intelligence.
4๏ธโฃ Generative AI with LLMs course : Learn business applications of artificial intelligence with AWS experts in a practical way.
5๏ธโฃ Generative AI for Everyone course : This course tells you what generative artificial intelligence is, how it works, and what uses and limitations it has.
๐น Generative artificial intelligence
1๏ธโฃ Generative AI for Beginners course : building generative artificial intelligence apps.
2๏ธโฃ Generative AI Fundamentals course : getting to know the basic principles of generative artificial intelligence.
3๏ธโฃ Intro to Gen AI course : from learning large language models to understanding the principles of responsible artificial intelligence.
4๏ธโฃ Generative AI with LLMs course : Learn business applications of artificial intelligence with AWS experts in a practical way.
5๏ธโฃ Generative AI for Everyone course : This course tells you what generative artificial intelligence is, how it works, and what uses and limitations it has.
Essential Skills to Master for Using Generative AI
1๏ธโฃ Prompt Engineering
โ๏ธ Learn how to craft clear, detailed prompts to get accurate AI-generated results.
2๏ธโฃ Data Literacy
๐ Understand data sources, biases, and how AI models process information.
3๏ธโฃ AI Ethics & Responsible Usage
โ๏ธ Know the ethical implications of AI, including bias, misinformation, and copyright issues.
4๏ธโฃ Creativity & Critical Thinking
๐ก AI enhances creativity, but human intuition is key for quality content.
5๏ธโฃ AI Tool Familiarity
๐ Get hands-on experience with tools like ChatGPT, DALLยทE, Midjourney, and Runway ML.
6๏ธโฃ Coding Basics (Optional)
๐ป Knowing Python, SQL, or APIs helps customize AI workflows and automation.
7๏ธโฃ Business & Marketing Awareness
๐ข Leverage AI for automation, branding, and customer engagement.
8๏ธโฃ Cybersecurity & Privacy Knowledge
๐ Learn how AI-generated data can be misused and ways to protect sensitive information.
9๏ธโฃ Adaptability & Continuous Learning
๐ AI evolves fastโstay updated with new trends, tools, and regulations.
Master these skills to make the most of AI in your personal and professional life! ๐ฅ
Free Generative AI Resources: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
1๏ธโฃ Prompt Engineering
โ๏ธ Learn how to craft clear, detailed prompts to get accurate AI-generated results.
2๏ธโฃ Data Literacy
๐ Understand data sources, biases, and how AI models process information.
3๏ธโฃ AI Ethics & Responsible Usage
โ๏ธ Know the ethical implications of AI, including bias, misinformation, and copyright issues.
4๏ธโฃ Creativity & Critical Thinking
๐ก AI enhances creativity, but human intuition is key for quality content.
5๏ธโฃ AI Tool Familiarity
๐ Get hands-on experience with tools like ChatGPT, DALLยทE, Midjourney, and Runway ML.
6๏ธโฃ Coding Basics (Optional)
๐ป Knowing Python, SQL, or APIs helps customize AI workflows and automation.
7๏ธโฃ Business & Marketing Awareness
๐ข Leverage AI for automation, branding, and customer engagement.
8๏ธโฃ Cybersecurity & Privacy Knowledge
๐ Learn how AI-generated data can be misused and ways to protect sensitive information.
9๏ธโฃ Adaptability & Continuous Learning
๐ AI evolves fastโstay updated with new trends, tools, and regulations.
Master these skills to make the most of AI in your personal and professional life! ๐ฅ
Free Generative AI Resources: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
๐๐จ๐ฐ ๐๐จ ๐๐๐ซ๐ ๐ ๐๐๐ง๐ ๐ฎ๐๐ ๐ ๐๐จ๐๐๐ฅ๐ฌ (๐๐๐๐ฌ) ๐๐จ๐ซ๐ค?
When I first worked with LLMs, they felt like magic. But once I learned how they really process language, it all started to make sense. Hereโs how it works -
1. Tokenization
- Why it matters: Before the model understands language, it needs to slice it into chunksโwords, subwords, even characters.
โข Use case: In a chatbot for a retail client, tokenization helped capture slang and misspellings from user queriesโso โgr8 dealsโ didnโt get lost in translation.
2. Embedding
- Why it's key: Those tokens turn into vectorsโnumbers that carry meaning and context.
โข Use case: While building a resume parser, embeddings helped the model understand โdeveloperโ and โprogrammerโ as similarโeven though the words were different.
3. Attention (Self-Attention)
- Why this stands out: This is where the model learns what to pay attention to. It looks across the entire sentence to make sense of context.
โข Use case: In a legal document assistant, attention mechanisms helped the model figure out that โheโ referred to โthe clientโ several sentences back.
4. Feed-Forward Layers
- Why it's helpful: It adds depth. These layers refine meaning and relationships even more.
โข Use case: While generating product descriptions, this helped the model balance between specs and toneโso it sounded natural, not robotic.
5. Normalization + Dropout
- Why it's needed: Keeps learning stable and prevents the model from overfitting to noise.
โข Use case: During fine-tuning for customer service tone, this made sure the model didnโt memorize one style too closelyโand stayed flexible.
6. Prediction (Next-Token Generation)
- Why it's powerful: Based on what it saw so far, the model predicts the next word.
โข Use case: In an AI assistant for internal reports, prediction steps helped craft bullet points from long texts, cutting writing time by 70%.
. .
But whatโs the most sensitive step?
- Attention. If it focuses wrong, hallucinations happenโconfusing facts or inventing things.
My learning?
- You donโt need to master it all at once. Stay curious. Build, break, repeat.
#llm
When I first worked with LLMs, they felt like magic. But once I learned how they really process language, it all started to make sense. Hereโs how it works -
1. Tokenization
- Why it matters: Before the model understands language, it needs to slice it into chunksโwords, subwords, even characters.
โข Use case: In a chatbot for a retail client, tokenization helped capture slang and misspellings from user queriesโso โgr8 dealsโ didnโt get lost in translation.
2. Embedding
- Why it's key: Those tokens turn into vectorsโnumbers that carry meaning and context.
โข Use case: While building a resume parser, embeddings helped the model understand โdeveloperโ and โprogrammerโ as similarโeven though the words were different.
3. Attention (Self-Attention)
- Why this stands out: This is where the model learns what to pay attention to. It looks across the entire sentence to make sense of context.
โข Use case: In a legal document assistant, attention mechanisms helped the model figure out that โheโ referred to โthe clientโ several sentences back.
4. Feed-Forward Layers
- Why it's helpful: It adds depth. These layers refine meaning and relationships even more.
โข Use case: While generating product descriptions, this helped the model balance between specs and toneโso it sounded natural, not robotic.
5. Normalization + Dropout
- Why it's needed: Keeps learning stable and prevents the model from overfitting to noise.
โข Use case: During fine-tuning for customer service tone, this made sure the model didnโt memorize one style too closelyโand stayed flexible.
6. Prediction (Next-Token Generation)
- Why it's powerful: Based on what it saw so far, the model predicts the next word.
โข Use case: In an AI assistant for internal reports, prediction steps helped craft bullet points from long texts, cutting writing time by 70%.
. .
But whatโs the most sensitive step?
- Attention. If it focuses wrong, hallucinations happenโconfusing facts or inventing things.
My learning?
- You donโt need to master it all at once. Stay curious. Build, break, repeat.
#llm
๐จ AI just cracked a 50-year-old physics problem in a few prompts.
Hereโs the story ๐
Back in the 1970s, physicists got stuck on the J1โJ2 Potts model โ a math-heavy puzzle used to understand frustrated magnets and atomic stacking.
It was only solved for the easiest case (q = 2).
Once it hit q = 3? Total chaos.
Until now.
Physicist Weiguo Yin teamed up with OpenAIโs o3-mini-high, a reasoning model.
Together, they shrunk a 9ร9 mathematical beast into a 2ร2 clean result โ and solved it exactly.
Why this matters:
๐งฒ Helps us understand complex materials
โก May unlock new superconductors
๐๏ธ Can improve how we design atomic-level tech
Physics problem: decades unsolved
AI + symmetry: exact solution
Real-world impact: massive
If AI can do this in physics... what else are we still sleeping on?
Hereโs the story ๐
Back in the 1970s, physicists got stuck on the J1โJ2 Potts model โ a math-heavy puzzle used to understand frustrated magnets and atomic stacking.
It was only solved for the easiest case (q = 2).
Once it hit q = 3? Total chaos.
Until now.
Physicist Weiguo Yin teamed up with OpenAIโs o3-mini-high, a reasoning model.
Together, they shrunk a 9ร9 mathematical beast into a 2ร2 clean result โ and solved it exactly.
Why this matters:
๐งฒ Helps us understand complex materials
โก May unlock new superconductors
๐๏ธ Can improve how we design atomic-level tech
Physics problem: decades unsolved
AI + symmetry: exact solution
Real-world impact: massive
If AI can do this in physics... what else are we still sleeping on?
Comprehensive Generative AI Learning Roadmap for 2025
Excited to share this detailed roadmap for anyone looking to dive into the world of Generative AI!
This visual guide breaks down the journey into 8 essential stages:
What is Generative AI - Understanding the fundamentals as a subset of ML that enables machines to learn from experience and create new content based on existing data
Important Concepts - Mastering the mathematical foundations: Probability, Linear Algebra, Calculus, and Statistics
Foundation Models - Familiarizing yourself with the key players: GPT, Llama, Gemini, Claude, and DeepSeek
GenAI Development Stack - Building with Python, Langchain, ChatGPT, Prompt Engineering, VectorDB, DeepSeek, MetaAI Llama, and Huggingface
Training a Foundation Model - The complete workflow from Dataset Collection โ Tokenization โ Configuration โ Training โ Evaluation โ Deployment
Building AI Agents - Understanding Human Control, Memory, Reactivity, Environment interactions, and how they enable Autonomous Actions
GenAI Models for Computer Vision - Exploring GAN, DALL-E, Flux, and Midjourney
GenAI Learning Resources - Leveraging DeepLearning AI, Kaggle, Google Labs, and Nvidia Learning
What I find most valuable about this roadmap is how it illustrates the interconnected nature of these concepts, from fundamental theory to practical implementation.
Whether you're a developer, researcher, or business leader, this framework provides a structured approach to understanding and leveraging generative AI technologies.
Excited to share this detailed roadmap for anyone looking to dive into the world of Generative AI!
This visual guide breaks down the journey into 8 essential stages:
What is Generative AI - Understanding the fundamentals as a subset of ML that enables machines to learn from experience and create new content based on existing data
Important Concepts - Mastering the mathematical foundations: Probability, Linear Algebra, Calculus, and Statistics
Foundation Models - Familiarizing yourself with the key players: GPT, Llama, Gemini, Claude, and DeepSeek
GenAI Development Stack - Building with Python, Langchain, ChatGPT, Prompt Engineering, VectorDB, DeepSeek, MetaAI Llama, and Huggingface
Training a Foundation Model - The complete workflow from Dataset Collection โ Tokenization โ Configuration โ Training โ Evaluation โ Deployment
Building AI Agents - Understanding Human Control, Memory, Reactivity, Environment interactions, and how they enable Autonomous Actions
GenAI Models for Computer Vision - Exploring GAN, DALL-E, Flux, and Midjourney
GenAI Learning Resources - Leveraging DeepLearning AI, Kaggle, Google Labs, and Nvidia Learning
What I find most valuable about this roadmap is how it illustrates the interconnected nature of these concepts, from fundamental theory to practical implementation.
Whether you're a developer, researcher, or business leader, this framework provides a structured approach to understanding and leveraging generative AI technologies.
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Qualcommโa global tech giant offering completely FREE courses that you can access anytime, anywhere.
โ 100% Free โ No hidden charges, subscriptions, or trials
โ Created by Industry Experts
โ Self-paced & Online โ Learn from anywhere, anytime
๐๐ข๐ง๐ค ๐:-
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Enroll Now & Get Certified ๐
7 Generative AI Projects You Can Build in 2025
โ Text-to-Image Generator โ Use models like DALLยทE or Stable Diffusion to generate art from text prompts
โ AI Music Composer โ Create original music using models like OpenAIโs Jukedeck or Magenta
โ Text-to-Video Generator โ Build a tool that generates short video clips from text descriptions
โ Deepfake Creation โ Develop realistic deepfake videos using GANs (Generative Adversarial Networks)
โ AI Content Writer โ Build a tool that generates human-like articles, blog posts, or social media updates
โ 3D Model Generator โ Create 3D objects and environments from text using AI like DreamFusion
โ AI Code Generator โ Use tools like GitHub Copilot to generate code snippets or even full programs from descriptions
Generative AI is changing the landscape of creativity and automation. These projects are perfect for experimenting with cutting-edge tech!
#generativeai
โ Text-to-Image Generator โ Use models like DALLยทE or Stable Diffusion to generate art from text prompts
โ AI Music Composer โ Create original music using models like OpenAIโs Jukedeck or Magenta
โ Text-to-Video Generator โ Build a tool that generates short video clips from text descriptions
โ Deepfake Creation โ Develop realistic deepfake videos using GANs (Generative Adversarial Networks)
โ AI Content Writer โ Build a tool that generates human-like articles, blog posts, or social media updates
โ 3D Model Generator โ Create 3D objects and environments from text using AI like DreamFusion
โ AI Code Generator โ Use tools like GitHub Copilot to generate code snippets or even full programs from descriptions
Generative AI is changing the landscape of creativity and automation. These projects are perfect for experimenting with cutting-edge tech!
#generativeai
Generative AI Career Paths You Can Explore in 2025
โ Generative AI Engineer โ Build and fine-tune models like GANs, VAEs, or diffusion models for images, video, and audio
โ Prompt Engineer โ Master the art of crafting effective prompts for large language and image models
โ AI Research Scientist โ Work on advancing the theory and capabilities of generative models
โ AI Product Manager โ Lead cross-functional teams to launch AI-powered creative tools
โ Creative Technologist โ Combine art and AI to build innovative experiences (e.g., AI in gaming, design, marketing)
โ Ethical AI Consultant โ Focus on the responsible use of generative models to prevent misuse
โ LLM Fine-Tuning Specialist โ Customize large language models for company-specific use cases and domains
Generative AI is a booming space โ blend creativity with code and ride the wave!
#generativeai
โ Generative AI Engineer โ Build and fine-tune models like GANs, VAEs, or diffusion models for images, video, and audio
โ Prompt Engineer โ Master the art of crafting effective prompts for large language and image models
โ AI Research Scientist โ Work on advancing the theory and capabilities of generative models
โ AI Product Manager โ Lead cross-functional teams to launch AI-powered creative tools
โ Creative Technologist โ Combine art and AI to build innovative experiences (e.g., AI in gaming, design, marketing)
โ Ethical AI Consultant โ Focus on the responsible use of generative models to prevent misuse
โ LLM Fine-Tuning Specialist โ Customize large language models for company-specific use cases and domains
Generative AI is a booming space โ blend creativity with code and ride the wave!
#generativeai
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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:
Before we start, What is NLP?
Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on the interaction between computers and humans through language.
It enables machines to understand, interpret, and respond to human language in a way that is both meaningful and useful.
Data scientists need NLP to analyze, process, and generate insights from large volumes of textual data, aiding in tasks ranging from sentiment analysis to automated summarization.
Tokenization
Tokenization involves breaking down text into smaller units, such as words or phrases. This is the first step in preprocessing textual data for further analysis or NLP applications.
Part-of-Speech Tagging:
This process involves identifying the part of speech for each word in a sentence (e.g., noun, verb, adjective). It is crucial for various NLP tasks that require understanding the grammatical structure of text.
Stemming and Lemmatization
These techniques reduce words to their base or root form. Stemming cuts off prefixes and suffixes, while lemmatization considers the morphological analysis of the words, leading to more accurate results.
Named Entity Recognition (NER)
NER identifies and classifies named entities in text into predefined categories such as the names of persons, organizations, locations, etc. It's essential for tasks like data extraction from documents and content classification.
Sentiment Analysis
This technique determines the emotional tone behind a body of text. It's widely used in business and social media monitoring to gauge public opinion and customer sentiment.
- Computer vision
- Natural language processing (NLP)
If you're into NLP, here are 5 fundamental concepts you should know:
Before we start, What is NLP?
Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on the interaction between computers and humans through language.
It enables machines to understand, interpret, and respond to human language in a way that is both meaningful and useful.
Data scientists need NLP to analyze, process, and generate insights from large volumes of textual data, aiding in tasks ranging from sentiment analysis to automated summarization.
Tokenization
Tokenization involves breaking down text into smaller units, such as words or phrases. This is the first step in preprocessing textual data for further analysis or NLP applications.
Part-of-Speech Tagging:
This process involves identifying the part of speech for each word in a sentence (e.g., noun, verb, adjective). It is crucial for various NLP tasks that require understanding the grammatical structure of text.
Stemming and Lemmatization
These techniques reduce words to their base or root form. Stemming cuts off prefixes and suffixes, while lemmatization considers the morphological analysis of the words, leading to more accurate results.
Named Entity Recognition (NER)
NER identifies and classifies named entities in text into predefined categories such as the names of persons, organizations, locations, etc. It's essential for tasks like data extraction from documents and content classification.
Sentiment Analysis
This technique determines the emotional tone behind a body of text. It's widely used in business and social media monitoring to gauge public opinion and customer sentiment.
If you want to Excel in AI and become an expert, master these essential concepts:
Core AI Concepts:
โข Machine Learning (ML) โ Supervised, Unsupervised, and Reinforcement Learning
โข Deep Learning (DL) โ Neural Networks, CNNs, RNNs, Transformers
โข Natural Language Processing (NLP) โ Text processing, LLMs (GPT, BERT)
โข Computer Vision (CV) โ Image classification, Object detection
โข AI Ethics & Bias โ Responsible AI development
Essential AI Tools & Frameworks:
โข Python Libraries โ TensorFlow, PyTorch, Scikit-Learn, Keras
โข Data Processing โ Pandas, NumPy, OpenCV, NLTK, SpaCy
โข Pretrained Models โ OpenAI GPT, Stable Diffusion, DALLยทE, CLIP
โข MLOps & Deployment โ Docker, FastAPI, Hugging Face, Flask, Gradio
Mathematical Foundations:
โข Linear Algebra โ Vectors, Matrices, Tensors
โข Probability & Statistics โ Bayesโ Theorem, Hypothesis Testing
โข Optimization โ Gradient Descent, Backpropagation
AI in Real-World Applications:
โข Chatbots & Virtual Assistants โ Build AI-powered bots
โข Recommendation Systems โ Personalized content suggestions
โข Autonomous Systems โ Self-driving cars, Robotics
โข AI in Healthcare โ Disease prediction, Medical imaging
Future Trends in AI:
โข AGI (Artificial General Intelligence) โ Next-level AI development
โข AI in Business & Automation โ AI-powered decision-making
โข Low-Code/No-Code AI โ Democratizing AI for everyone
Free AI Resources:https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E
Like it if you need a complete tutorial on all these topics! ๐โค๏ธ
Core AI Concepts:
โข Machine Learning (ML) โ Supervised, Unsupervised, and Reinforcement Learning
โข Deep Learning (DL) โ Neural Networks, CNNs, RNNs, Transformers
โข Natural Language Processing (NLP) โ Text processing, LLMs (GPT, BERT)
โข Computer Vision (CV) โ Image classification, Object detection
โข AI Ethics & Bias โ Responsible AI development
Essential AI Tools & Frameworks:
โข Python Libraries โ TensorFlow, PyTorch, Scikit-Learn, Keras
โข Data Processing โ Pandas, NumPy, OpenCV, NLTK, SpaCy
โข Pretrained Models โ OpenAI GPT, Stable Diffusion, DALLยทE, CLIP
โข MLOps & Deployment โ Docker, FastAPI, Hugging Face, Flask, Gradio
Mathematical Foundations:
โข Linear Algebra โ Vectors, Matrices, Tensors
โข Probability & Statistics โ Bayesโ Theorem, Hypothesis Testing
โข Optimization โ Gradient Descent, Backpropagation
AI in Real-World Applications:
โข Chatbots & Virtual Assistants โ Build AI-powered bots
โข Recommendation Systems โ Personalized content suggestions
โข Autonomous Systems โ Self-driving cars, Robotics
โข AI in Healthcare โ Disease prediction, Medical imaging
Future Trends in AI:
โข AGI (Artificial General Intelligence) โ Next-level AI development
โข AI in Business & Automation โ AI-powered decision-making
โข Low-Code/No-Code AI โ Democratizing AI for everyone
Free AI Resources:https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E
Like it if you need a complete tutorial on all these topics! ๐โค๏ธ
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How to revolutionize Hollywood with AI.
Unlock new possibilities:
1. Voice Cloning
Clone voices of Hollywood icons:
โข Legally clone and use voices with permission.
โข Recreate iconic voices for new projects.
โข Preserve legendary performances for future generations.
2. Custom Voices
Create unique voices for your projects:
โข Generate up to 20 seconds of dialogue.
โข Select from preset voice options or create your own.
3. Lip Sync Tool
Bring still characters to life:
โข Use ElevenLabs's Lip Sync tool.
โข Select a face and add a script.
โข Generate videos with synchronized lip movements.
AI is reshaping the industry, voice cloning is part of a broader trend.
Filmmakers can now recreate voices of iconic actors.
Unlock new possibilities:
1. Voice Cloning
Clone voices of Hollywood icons:
โข Legally clone and use voices with permission.
โข Recreate iconic voices for new projects.
โข Preserve legendary performances for future generations.
2. Custom Voices
Create unique voices for your projects:
โข Generate up to 20 seconds of dialogue.
โข Select from preset voice options or create your own.
3. Lip Sync Tool
Bring still characters to life:
โข Use ElevenLabs's Lip Sync tool.
โข Select a face and add a script.
โข Generate videos with synchronized lip movements.
AI is reshaping the industry, voice cloning is part of a broader trend.
Filmmakers can now recreate voices of iconic actors.
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Step-by-Step Approach to Learn Python
โ Learn the Basics โ Syntax, Variables, Data Types (int, float, string, boolean)
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โ Control Flow โ If-Else, Loops (For, While), List Comprehensions
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โ Data Structures โ Lists, Tuples, Sets, Dictionaries
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โ Functions & Modules โ Defining Functions, Lambda Functions, Importing Modules
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โ File Handling โ Reading/Writing Files, CSV, JSON
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โ Object-Oriented Programming (OOP) โ Classes, Objects, Inheritance, Polymorphism
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โ Error Handling & Debugging โ Try-Except, Logging, Debugging Techniques
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โ Advanced Topics โ Regular Expressions, Multi-threading, Decorators, Generators
Free Python Resources: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
ENJOY LEARNING ๐๐
โ Learn the Basics โ Syntax, Variables, Data Types (int, float, string, boolean)
โ
โ Control Flow โ If-Else, Loops (For, While), List Comprehensions
โ
โ Data Structures โ Lists, Tuples, Sets, Dictionaries
โ
โ Functions & Modules โ Defining Functions, Lambda Functions, Importing Modules
โ
โ File Handling โ Reading/Writing Files, CSV, JSON
โ
โ Object-Oriented Programming (OOP) โ Classes, Objects, Inheritance, Polymorphism
โ
โ Error Handling & Debugging โ Try-Except, Logging, Debugging Techniques
โ
โ Advanced Topics โ Regular Expressions, Multi-threading, Decorators, Generators
Free Python Resources: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
ENJOY LEARNING ๐๐
Google, Harvard, and even OpenAI are offering FREE Generative AI courses (no payment required) ๐
Here are 8 FREE courses to master AI in 2024:
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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/