🏆 – AI/ML Engineer
Stage 1 – Python Basics
Stage 2 – Statistics & Probability
Stage 3 – Linear Algebra & Calculus
Stage 4 – Data Preprocessing
Stage 5 – Exploratory Data Analysis (EDA)
Stage 6 – Supervised Learning
Stage 7 – Unsupervised Learning
Stage 8 – Feature Engineering
Stage 9 – Model Evaluation & Tuning
Stage 10 – Deep Learning Basics
Stage 11 – Neural Networks & CNNs
Stage 12 – RNNs & LSTMs
Stage 13 – NLP Fundamentals
Stage 14 – Deployment (Flask, Docker)
Stage 15 – Build projects
Stage 1 – Python Basics
Stage 2 – Statistics & Probability
Stage 3 – Linear Algebra & Calculus
Stage 4 – Data Preprocessing
Stage 5 – Exploratory Data Analysis (EDA)
Stage 6 – Supervised Learning
Stage 7 – Unsupervised Learning
Stage 8 – Feature Engineering
Stage 9 – Model Evaluation & Tuning
Stage 10 – Deep Learning Basics
Stage 11 – Neural Networks & CNNs
Stage 12 – RNNs & LSTMs
Stage 13 – NLP Fundamentals
Stage 14 – Deployment (Flask, Docker)
Stage 15 – Build projects
❤5
Master Artificial Intelligence in 10 days with free resources 😄👇
Day 1: Introduction to AI
- Start with an overview of what AI is and its various applications.
- Read articles or watch videos explaining the basics of AI.
Day 2-3: Machine Learning Fundamentals
- Learn the basics of machine learning, including supervised and unsupervised learning.
- Study concepts like data, features, labels, and algorithms.
Day 4-5: Deep Learning
- Dive into deep learning, understanding neural networks and their architecture.
- Learn about popular deep learning frameworks like TensorFlow or PyTorch.
Day 6: Natural Language Processing (NLP)
- Explore the basics of NLP, including tokenization, sentiment analysis, and named entity recognition.
Day 7: Computer Vision
- Study computer vision, including image recognition, object detection, and convolutional neural networks.
Day 8: AI Ethics and Bias
- Explore the ethical considerations in AI and the issue of bias in AI algorithms.
Day 9: AI Tools and Resources
- Familiarize yourself with AI development tools and platforms.
- Learn how to access and use AI datasets and APIs.
Day 10: AI Project
- Work on a small AI project. For example, build a basic chatbot, create an image classifier, or analyze a dataset using AI techniques.
Here are 5 amazing AI projects with free datasets: https://bit.ly/3ZVDjR1
Throughout the 10 days, it's important to practice what you learn through coding and practical exercises. Additionally, consider reading AI-related books and articles, watching online courses, and participating in AI communities and forums to enhance your learning experience.
Free Books and Courses to Learn Artificial Intelligence
👇👇
Introduction to AI Free Udacity Course
Introduction to Prolog programming for artificial intelligence Free Book
Introduction to AI for Business Free Course
Artificial Intelligence: Foundations of Computational Agents Free Book
Learn Basics about AI Free Udemy Course
(4.4 Star ratings out of 5)
Amazing AI Reverse Image Search
(4.7 Star ratings out of 5)
13 AI Tools to improve your productivity: https://t.iss.one/crackingthecodinginterview/619
4 AI Certifications for Developers: https://t.iss.one/datasciencefun/1375
Join @free4unow_backup for more free courses
ENJOY LEARNING👍👍
Day 1: Introduction to AI
- Start with an overview of what AI is and its various applications.
- Read articles or watch videos explaining the basics of AI.
Day 2-3: Machine Learning Fundamentals
- Learn the basics of machine learning, including supervised and unsupervised learning.
- Study concepts like data, features, labels, and algorithms.
Day 4-5: Deep Learning
- Dive into deep learning, understanding neural networks and their architecture.
- Learn about popular deep learning frameworks like TensorFlow or PyTorch.
Day 6: Natural Language Processing (NLP)
- Explore the basics of NLP, including tokenization, sentiment analysis, and named entity recognition.
Day 7: Computer Vision
- Study computer vision, including image recognition, object detection, and convolutional neural networks.
Day 8: AI Ethics and Bias
- Explore the ethical considerations in AI and the issue of bias in AI algorithms.
Day 9: AI Tools and Resources
- Familiarize yourself with AI development tools and platforms.
- Learn how to access and use AI datasets and APIs.
Day 10: AI Project
- Work on a small AI project. For example, build a basic chatbot, create an image classifier, or analyze a dataset using AI techniques.
Here are 5 amazing AI projects with free datasets: https://bit.ly/3ZVDjR1
Throughout the 10 days, it's important to practice what you learn through coding and practical exercises. Additionally, consider reading AI-related books and articles, watching online courses, and participating in AI communities and forums to enhance your learning experience.
Free Books and Courses to Learn Artificial Intelligence
👇👇
Introduction to AI Free Udacity Course
Introduction to Prolog programming for artificial intelligence Free Book
Introduction to AI for Business Free Course
Artificial Intelligence: Foundations of Computational Agents Free Book
Learn Basics about AI Free Udemy Course
(4.4 Star ratings out of 5)
Amazing AI Reverse Image Search
(4.7 Star ratings out of 5)
13 AI Tools to improve your productivity: https://t.iss.one/crackingthecodinginterview/619
4 AI Certifications for Developers: https://t.iss.one/datasciencefun/1375
Join @free4unow_backup for more free courses
ENJOY LEARNING👍👍
❤7
For those who feel like they're not learning much and feeling demotivated. You should definitely read these lines from one of the book by Andrew Ng 👇
No one can cram everything they need to know over a weekend or even a month. Everyone I
know who’s great at machine learning is a lifelong learner. Given how quickly our field is changing,
there’s little choice but to keep learning if you want to keep up.
How can you maintain a steady pace of learning for years? If you can cultivate the habit of
learning a little bit every week, you can make significant progress with what feels like less effort.
Everyday it gets easier but you need to do it everyday ❤️
No one can cram everything they need to know over a weekend or even a month. Everyone I
know who’s great at machine learning is a lifelong learner. Given how quickly our field is changing,
there’s little choice but to keep learning if you want to keep up.
How can you maintain a steady pace of learning for years? If you can cultivate the habit of
learning a little bit every week, you can make significant progress with what feels like less effort.
Everyday it gets easier but you need to do it everyday ❤️
❤8🔥3
Media is too big
VIEW IN TELEGRAM
OnSpace Mobile App builder: Idea → AppStore → Profit.
👉https://onspace.ai/?via=tg_ggpt
With OnSpace, you can turn your idea into a real iOS or Android app in AppStore/PlayStore.
What will you get:
- Create app by chatting with AI
- Real-time app demo.
- Add payments and monetize like in-app-purchase and Stripe.
- Functional login & signup.
- Database + dashboard in minutes.
- Preview, download, and publish to AppStore.
- Full tutorial on YouTube and within 1 day customer service
🫵It’s your shortcut from concept to cash flow.
👉https://onspace.ai/?via=tg_ggpt
With OnSpace, you can turn your idea into a real iOS or Android app in AppStore/PlayStore.
What will you get:
- Create app by chatting with AI
- Real-time app demo.
- Add payments and monetize like in-app-purchase and Stripe.
- Functional login & signup.
- Database + dashboard in minutes.
- Preview, download, and publish to AppStore.
- Full tutorial on YouTube and within 1 day customer service
🫵It’s your shortcut from concept to cash flow.
❤6
✅ 50 Must-Know Generative AI Concepts for Interviews 🎨🤖
📍 Generative AI Basics
1. What is Generative AI?
2. Generative AI vs Traditional AI
3. Applications of Generative AI
4. Diffusion Models vs GANs
5. Text, Image, Audio, Code Generation
📍 Large Language Models (LLMs)
6. What is a Language Model?
7. , BERT, T5 – key differences
8. Prompt Engineering
9. Zero-shot, Few-shot, Fine-tuning
10. Tokenization & Attention Mechanism
📍 Foundational Concepts
11. Transformers
12. Self-Attention
13. Positional Encoding
14. Pre-training & Fine-tuning
15. Loss Functions in Language Models (e.g., Cross-Entropy)
📍 Image Generation
16. GANs (Generative Adversarial Networks)
17. StyleGAN / CycleGAN
18. Diffusion Models (e.g., DALL·E, Stable Diffusion)
19. CLIP (Contrastive Language-Image Pretraining)
20. Text-to-Image Models
📍 Audio & Video Generation
21. Text-to-Speech (TTS)
22. Voice Cloning
23. AI Music Generation
24. Video Generation with AI
25. Deepfakes & Synthetic Media
📍 Evaluation & Safety
26. Evaluating LLMs (BLEU, ROUGE, perplexity)
27. Hallucinations in LLMs
28. Content Filtering & Safety Layers
29. Jailbreaks & Model Misuse
30. Red Teaming in AI
📍 Popular Tools & Platforms
31. OpenAI (Chat, DALL·E)
32. Google ChatGPT
33. Anthropic Claude
34. Meta Llama
35. Hugging Face Transformers
📍 Use Cases in Industries
36. Marketing & Content Generation
37. Customer Support (AI Chatbots)
38. Education (Tutors, Summarizers)
39. Healthcare (Medical Report Generation)
40. Coding (Code Assistants like Copilot)
📍 Fine-Tuning & Customization
41. LoRA (Low-Rank Adaptation)
42. RLHF (Reinforcement Learning from Human Feedback)
43. Retrieval-Augmented Generation (RAG)
44. Embeddings & Vector DBs (e.g., FAISS, Pinecone)
45. System vs User Prompts in LLMs
📍 Ethics & Future
46. AI Copyright & Ownership
47. Bias & Fairness in Generative Models
48. AI Watermarking & Detection
49. Responsible Deployment
50. Future of Human-AI Collaboration
📍 Generative AI Basics
1. What is Generative AI?
2. Generative AI vs Traditional AI
3. Applications of Generative AI
4. Diffusion Models vs GANs
5. Text, Image, Audio, Code Generation
📍 Large Language Models (LLMs)
6. What is a Language Model?
7. , BERT, T5 – key differences
8. Prompt Engineering
9. Zero-shot, Few-shot, Fine-tuning
10. Tokenization & Attention Mechanism
📍 Foundational Concepts
11. Transformers
12. Self-Attention
13. Positional Encoding
14. Pre-training & Fine-tuning
15. Loss Functions in Language Models (e.g., Cross-Entropy)
📍 Image Generation
16. GANs (Generative Adversarial Networks)
17. StyleGAN / CycleGAN
18. Diffusion Models (e.g., DALL·E, Stable Diffusion)
19. CLIP (Contrastive Language-Image Pretraining)
20. Text-to-Image Models
📍 Audio & Video Generation
21. Text-to-Speech (TTS)
22. Voice Cloning
23. AI Music Generation
24. Video Generation with AI
25. Deepfakes & Synthetic Media
📍 Evaluation & Safety
26. Evaluating LLMs (BLEU, ROUGE, perplexity)
27. Hallucinations in LLMs
28. Content Filtering & Safety Layers
29. Jailbreaks & Model Misuse
30. Red Teaming in AI
📍 Popular Tools & Platforms
31. OpenAI (Chat, DALL·E)
32. Google ChatGPT
33. Anthropic Claude
34. Meta Llama
35. Hugging Face Transformers
📍 Use Cases in Industries
36. Marketing & Content Generation
37. Customer Support (AI Chatbots)
38. Education (Tutors, Summarizers)
39. Healthcare (Medical Report Generation)
40. Coding (Code Assistants like Copilot)
📍 Fine-Tuning & Customization
41. LoRA (Low-Rank Adaptation)
42. RLHF (Reinforcement Learning from Human Feedback)
43. Retrieval-Augmented Generation (RAG)
44. Embeddings & Vector DBs (e.g., FAISS, Pinecone)
45. System vs User Prompts in LLMs
📍 Ethics & Future
46. AI Copyright & Ownership
47. Bias & Fairness in Generative Models
48. AI Watermarking & Detection
49. Responsible Deployment
50. Future of Human-AI Collaboration
❤7