ROC Plot: Clearly explained 🔥
💡 You can use an ROC (Receiver Operating Characteristics) curve to evaluate the results of a classifier. The ROC curve represents the trade-off between the True positive rate (TPR) and the False positive rate (FPR).
🤔 Specificity and Sensitivity
The True positive rate is also called sensitivity, and the True negative rate (TNR) is called specificity.
Specificity is a measure for the whole negative part of a data set, while sensitivity is a measure for the whole positive part.
🤖 The ROC plot uses the True positive rate (TPR) on the y-axis, and the false positive rate (FPR) is on the x-axis (formula FPR = 1 - TNR). You see a visual explanation in the figure.
😎 To interpret the ROC curve, note that a classifier with a random performance level is a straight line from the origin (0, 0) to the top right corner (1, 1).
A poor classifier lies below this line, and a classifier improves as it deviates upward from the bisector.
📊 Another criterion in the ROC curve is the area under the ROC curve (AUC) score. Here, we calculate the area under the curve. A good classifier has an AUC-Score > 0.5.
Interested in AI Engineering?
https://t.iss.one/CodeProgrammer✅
💡 You can use an ROC (Receiver Operating Characteristics) curve to evaluate the results of a classifier. The ROC curve represents the trade-off between the True positive rate (TPR) and the False positive rate (FPR).
🤔 Specificity and Sensitivity
The True positive rate is also called sensitivity, and the True negative rate (TNR) is called specificity.
Specificity is a measure for the whole negative part of a data set, while sensitivity is a measure for the whole positive part.
🤖 The ROC plot uses the True positive rate (TPR) on the y-axis, and the false positive rate (FPR) is on the x-axis (formula FPR = 1 - TNR). You see a visual explanation in the figure.
😎 To interpret the ROC curve, note that a classifier with a random performance level is a straight line from the origin (0, 0) to the top right corner (1, 1).
A poor classifier lies below this line, and a classifier improves as it deviates upward from the bisector.
📊 Another criterion in the ROC curve is the area under the ROC curve (AUC) score. Here, we calculate the area under the curve. A good classifier has an AUC-Score > 0.5.
Interested in AI Engineering?
https://t.iss.one/CodeProgrammer
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🔥 Precision-Recall plot: Clearly explained
🔍 The precision-recall plot is a model-wide measure for evaluating classifiers. The plot is based on the evaluation metrics of Precision and Recall.
🧐 Recall (identical to sensitivity) is a measure of the whole positive part of a dataset, whereas precision is a measure of positive predictions.
The precision-recall plot uses precision on the y-axis and recall on the x-axis. You see a visual explanation in the figure.
🤔 It is easy to interpret a precision-recall plot. In general, precision decreases as recall increases. Conversely, as precision increases, recall decreases.
💡 A random classifier lies on the y-axis (precision) at y = P/( P + N ) (P: number of positive labels, N: number of negative labels). A poor classifier lies below this line, and a good classifier lies well above this line.
🌟 You can see two different plots in the figure. On the left side, you see the random line is y=0.5. The ratio of positives (P) and negatives (N) is 1:1. On the right side, you see the random line is y=0.25. There, we have a ratio of positives and negatives of 1:3.
📊 Another quality criterion in the precision-recall plot is the area under the curve (AUC) score, where the area under the curve is calculated. An AUC score close to 1 characterizes a good classifier.
https://t.iss.one/CodeProgrammer
🔍 The precision-recall plot is a model-wide measure for evaluating classifiers. The plot is based on the evaluation metrics of Precision and Recall.
🧐 Recall (identical to sensitivity) is a measure of the whole positive part of a dataset, whereas precision is a measure of positive predictions.
The precision-recall plot uses precision on the y-axis and recall on the x-axis. You see a visual explanation in the figure.
🤔 It is easy to interpret a precision-recall plot. In general, precision decreases as recall increases. Conversely, as precision increases, recall decreases.
💡 A random classifier lies on the y-axis (precision) at y = P/( P + N ) (P: number of positive labels, N: number of negative labels). A poor classifier lies below this line, and a good classifier lies well above this line.
🌟 You can see two different plots in the figure. On the left side, you see the random line is y=0.5. The ratio of positives (P) and negatives (N) is 1:1. On the right side, you see the random line is y=0.25. There, we have a ratio of positives and negatives of 1:3.
📊 Another quality criterion in the precision-recall plot is the area under the curve (AUC) score, where the area under the curve is calculated. An AUC score close to 1 characterizes a good classifier.
https://t.iss.one/CodeProgrammer
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30 Days with Python — this is a step-by-step guide to learning the Python programming language over 30 days.
Completing this task may take more than 100 days, so proceed at your own pace.
Repo: https://github.com/Asabeneh/30-Days-Of-Python
https://t.iss.one/CodeProgrammer🌟
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Completing this task may take more than 100 days, so proceed at your own pace.
Repo: https://github.com/Asabeneh/30-Days-Of-Python
https://t.iss.one/CodeProgrammer
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Top Machine Learning Algorithms You Should Actually Understand 🤖
Most individuals merely memorize algorithms. In contrast, professional engineers comprehend the appropriate application contexts and the underlying reasons for algorithmic failure.
This is not a simple list; it is an explanation of how Machine Learning (ML) functions in practical environments. 🛠
1️⃣ ➤ Linear Regression 📈
This serves as the foundational starting point.
The process involves fitting a straight line to data to address a fundamental question: how does the input affect the output?
↳ Example: Predicting house prices based on size.
This method performs effectively when relationships are linear but fails when patterns become non-linear.
2️⃣ ➤ Logistic Regression 📊
Despite its nomenclature, this algorithm is utilized for classification tasks.
It predicts probabilities rather than continuous values.
↳ Example: Distinguishing between spam and non-spam emails.
A thorough understanding of this method equips one with knowledge of decision boundaries.
3️⃣ ➤ Decision Trees 🌳
Conceptualize this as a flowchart.
Data is split based on specific conditions until a final decision is reached.
↳ Example: Loan approval systems.
While easy to interpret, this approach is prone to overfitting.
4️⃣ ➤ Random Forest 🌲
This involves not a single tree, but hundreds of trees voting collectively.
This ensemble approach significantly reduces overfitting.
↳ Example: Fraud detection systems.
It serves as a very robust baseline in real-world systems.
5️⃣ ➤ K Nearest Neighbors (KNN) 🔍
There is no explicit training phase.
The system simply compares new data points with the nearest existing data points.
↳ Example: Recommendation systems.
While simple, it becomes computationally slow at scale.
6️⃣ ➤ K Means Clustering 🎯
This is a form of unsupervised learning.
It groups similar data points into distinct clusters.
↳ Example: Customer segmentation.
This method is effective only if the clusters are well-separated.
7️⃣ ➤ Support Vector Machine (SVM) ⚖️
This algorithm identifies the optimal boundary between different classes.
It functions by maximizing the margin between classes.
↳ Example: Text classification.
While powerful, it lacks scalability for very large datasets.
8️⃣ ➤ Naive Bayes 📧
This method is based on probability theory.
It operates under the assumption that features are independent.
↳ Example: Email filtering.
It remains surprisingly effective for straightforward problems.
9️⃣ ➤ XGBoost 🏆
This algorithm is a consistent winner in competitions for a specific reason.
It sequentially improves weak models to create a strong predictor.
↳ Example: Structured data problems.
If uncertainty exists regarding which model to utilize, this is an excellent starting point.
🔟 ➤ Neural Networks 🧠
This constitutes the foundation of deep learning.
It is capable of handling highly complex patterns.
↳ Example: Image, text, and speech processing.
It requires substantial data, computational resources, and fine-tuning.
How They Fit Together 🧩
Simple Data → Linear / Logistic
Structured Data → Random Forest / XGBoost
Similarity Based → KNN
Unlabeled Data → K Means
High Dimension → SVM
Complex Patterns → Neural Networks
Real Insight 💡
Most real-world systems do not employ every available algorithm.
They rely on:
→ Strong baselines
→ High-quality data
→ Proper evaluation
They do not depend on overly complex models.
TL;DR 📝
Start simple.
Understand deeply.
Then scale complexity.
This is the methodology employed by professional Machine Learning engineers.
Most individuals merely memorize algorithms. In contrast, professional engineers comprehend the appropriate application contexts and the underlying reasons for algorithmic failure.
This is not a simple list; it is an explanation of how Machine Learning (ML) functions in practical environments. 🛠
1️⃣ ➤ Linear Regression 📈
This serves as the foundational starting point.
The process involves fitting a straight line to data to address a fundamental question: how does the input affect the output?
↳ Example: Predicting house prices based on size.
This method performs effectively when relationships are linear but fails when patterns become non-linear.
2️⃣ ➤ Logistic Regression 📊
Despite its nomenclature, this algorithm is utilized for classification tasks.
It predicts probabilities rather than continuous values.
↳ Example: Distinguishing between spam and non-spam emails.
A thorough understanding of this method equips one with knowledge of decision boundaries.
3️⃣ ➤ Decision Trees 🌳
Conceptualize this as a flowchart.
Data is split based on specific conditions until a final decision is reached.
↳ Example: Loan approval systems.
While easy to interpret, this approach is prone to overfitting.
4️⃣ ➤ Random Forest 🌲
This involves not a single tree, but hundreds of trees voting collectively.
This ensemble approach significantly reduces overfitting.
↳ Example: Fraud detection systems.
It serves as a very robust baseline in real-world systems.
5️⃣ ➤ K Nearest Neighbors (KNN) 🔍
There is no explicit training phase.
The system simply compares new data points with the nearest existing data points.
↳ Example: Recommendation systems.
While simple, it becomes computationally slow at scale.
6️⃣ ➤ K Means Clustering 🎯
This is a form of unsupervised learning.
It groups similar data points into distinct clusters.
↳ Example: Customer segmentation.
This method is effective only if the clusters are well-separated.
7️⃣ ➤ Support Vector Machine (SVM) ⚖️
This algorithm identifies the optimal boundary between different classes.
It functions by maximizing the margin between classes.
↳ Example: Text classification.
While powerful, it lacks scalability for very large datasets.
8️⃣ ➤ Naive Bayes 📧
This method is based on probability theory.
It operates under the assumption that features are independent.
↳ Example: Email filtering.
It remains surprisingly effective for straightforward problems.
9️⃣ ➤ XGBoost 🏆
This algorithm is a consistent winner in competitions for a specific reason.
It sequentially improves weak models to create a strong predictor.
↳ Example: Structured data problems.
If uncertainty exists regarding which model to utilize, this is an excellent starting point.
🔟 ➤ Neural Networks 🧠
This constitutes the foundation of deep learning.
It is capable of handling highly complex patterns.
↳ Example: Image, text, and speech processing.
It requires substantial data, computational resources, and fine-tuning.
How They Fit Together 🧩
Simple Data → Linear / Logistic
Structured Data → Random Forest / XGBoost
Similarity Based → KNN
Unlabeled Data → K Means
High Dimension → SVM
Complex Patterns → Neural Networks
Real Insight 💡
Most real-world systems do not employ every available algorithm.
They rely on:
→ Strong baselines
→ High-quality data
→ Proper evaluation
They do not depend on overly complex models.
TL;DR 📝
Start simple.
Understand deeply.
Then scale complexity.
This is the methodology employed by professional Machine Learning engineers.
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Thrilled to announce a major milestone in our professional development journey! 🚀 We are excited to unveil a strategic, curated ecosystem of 800+ high-impact Computer Science learning modules from industry titans like MIT, Harvard, and other top-tier global institutions. 🎓✨
This centralized repository represents a powerful synergy of knowledge, meticulously organized by key verticals including algorithms, ML, networks, and robotics, ensuring seamless alignment with your career growth objectives. 📈💡
Say goodbye to fragmented roadmaps and hello to a ready-made, optimized pathway for Computer Science excellence—empowering you to leverage these resources without the need for manual assembly or redundant effort. ⚙️🌟
Unlock your full potential and scale your expertise today:
⛓️ Strategic Resource Hub:
https://github.com/Developer-Y/cs-video-courses
#ContinuousLearning #GrowthMindset #TechExcellence #CareerStrategy #Innovation
This centralized repository represents a powerful synergy of knowledge, meticulously organized by key verticals including algorithms, ML, networks, and robotics, ensuring seamless alignment with your career growth objectives. 📈💡
Say goodbye to fragmented roadmaps and hello to a ready-made, optimized pathway for Computer Science excellence—empowering you to leverage these resources without the need for manual assembly or redundant effort. ⚙️🌟
Unlock your full potential and scale your expertise today:
⛓️ Strategic Resource Hub:
https://github.com/Developer-Y/cs-video-courses
#ContinuousLearning #GrowthMindset #TechExcellence #CareerStrategy #Innovation
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cnn-vgg19-model-tranform-learning.pdf
7 MB
Excited to share latest Deep Learning project: Faulty Solar Panel Detection using CNN + VGG19! 🚀
☀️ Problem: Manual solar panel inspection is slow, costly, and error-prone due to environmental degradation.
💡 Solution: An image classification model detecting 6 fault types via VGG19 Transfer Learning (ImageNet pretrained).
📂 Dataset: 885 images across 6 classes:
• 🐦 Bird-drop
• ✅ Clean
• 🌫 Dusty
• ⚡️ Electrical-damage
• 💥 Physical-Damage
• ❄️ Snow-Covered
🏗 Architecture:
• Base: VGG19 (frozen for feature extraction)
• Head: GlobalAveragePooling2D → Dropout(0.3) → Dense(90)
• Training: Phase 1 (Head only, 46K params) → Phase 2 (Fine-tune top layers, lr=0.0001)
📊 Results (2 epochs):
✅ Val Accuracy: 81.36%
📉 Val Loss: 0.589
🔍 Takeaways:
→ Transfer learning works well on small datasets (~885 images).
→ Fine-tuning significantly boosted performance over feature extraction alone.
→ Model effectively distinguishes subtle differences (e.g., dusty vs. bird-drop).
🛠 Stack: Python | TensorFlow/Keras | VGG19 | OpenCV | Scikit-learn | Seaborn | Matplotlib
https://t.iss.one/CodeProgrammer🔰
☀️ Problem: Manual solar panel inspection is slow, costly, and error-prone due to environmental degradation.
💡 Solution: An image classification model detecting 6 fault types via VGG19 Transfer Learning (ImageNet pretrained).
📂 Dataset: 885 images across 6 classes:
• 🐦 Bird-drop
• ✅ Clean
• 🌫 Dusty
• ⚡️ Electrical-damage
• 💥 Physical-Damage
• ❄️ Snow-Covered
🏗 Architecture:
• Base: VGG19 (frozen for feature extraction)
• Head: GlobalAveragePooling2D → Dropout(0.3) → Dense(90)
• Training: Phase 1 (Head only, 46K params) → Phase 2 (Fine-tune top layers, lr=0.0001)
📊 Results (2 epochs):
✅ Val Accuracy: 81.36%
📉 Val Loss: 0.589
🔍 Takeaways:
→ Transfer learning works well on small datasets (~885 images).
→ Fine-tuning significantly boosted performance over feature extraction alone.
→ Model effectively distinguishes subtle differences (e.g., dusty vs. bird-drop).
🛠 Stack: Python | TensorFlow/Keras | VGG19 | OpenCV | Scikit-learn | Seaborn | Matplotlib
https://t.iss.one/CodeProgrammer
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🔥 Google Colab has added the option of retraining 500+ open-source neural networks
Unsloth has released a convenient notebook for configuring models.
Instructions:
1. Open the page in Colab: https://colab.research.google.com/github/unslothai/unsloth/blob/main/studio/Unsloth_Studio_Colab.ipynb
2. Run the blocks and the Unsloth Studio itself.
3. Select a model and a dataset.
4. Click "Start Training" and monitor the progress in real time.
5. Everything is ready - you can immediately compare the regular and fine-tuned versions of the model in the chat.
Unsloth has released a convenient notebook for configuring models.
Instructions:
1. Open the page in Colab: https://colab.research.google.com/github/unslothai/unsloth/blob/main/studio/Unsloth_Studio_Colab.ipynb
2. Run the blocks and the Unsloth Studio itself.
3. Select a model and a dataset.
4. Click "Start Training" and monitor the progress in real time.
5. Everything is ready - you can immediately compare the regular and fine-tuned versions of the model in the chat.
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This FREE AI engineering roadmap
Will teach you more in 2026 than a 4-year college degree...
Here's the exact 6-step blueprint 👇
1️⃣STEP 1: Python Programming Foundations
Harvard CS50's Python Programming Course : https://lnkd.in/ePCvXwXP
→ Build unshakeable coding fundamentals
→ 6-8 weeks to Python mastery
2️⃣ STEP 2: Machine Learning Foundations
Stanford CS229: Machine Learning : https://lnkd.in/eEsdZbVc
→ Learn from the legends at Stanford
→ Master ML algorithms and math foundations
→ 10-12 weeks of pure gold
3️⃣ STEP 3: Deep Learning Mastery
Fast.ai Practical Deep Learning : https://course.fast.ai/
→ Jeremy Howard's legendary course
→ Build real AI applications from day 1
→ 8-10 weeks of hands-on projects
4️⃣ STEP 4: Natural Language Processing
Stanford CS224N/Ling284 : https://lnkd.in/ebQZ5_T3
→ Master transformers and language models
→ The foundation of ChatGPT and GPT-4
→ 10-12 weeks of cutting-edge NLP
5️⃣ STEP 5: Generative AI Introduction
Microsoft Generative AI for Beginners
: https://lnkd.in/ewsH8gMT
→ 21 Lessons teaching everything you need to know to start building Generative AI applications
→ 6-8 weeks of creative AI
6️⃣ STEP 6: Large Language Models
LLM University by Cohere : https://cohere.com/llmu
→ Fine-tune and deploy production LLMs
→ Build and deploy LLM models
→ 6-8 weeks of enterprise-level skills
https://t.iss.one/CodeProgrammer✅
Will teach you more in 2026 than a 4-year college degree...
Here's the exact 6-step blueprint 👇
1️⃣STEP 1: Python Programming Foundations
Harvard CS50's Python Programming Course : https://lnkd.in/ePCvXwXP
→ Build unshakeable coding fundamentals
→ 6-8 weeks to Python mastery
2️⃣ STEP 2: Machine Learning Foundations
Stanford CS229: Machine Learning : https://lnkd.in/eEsdZbVc
→ Learn from the legends at Stanford
→ Master ML algorithms and math foundations
→ 10-12 weeks of pure gold
3️⃣ STEP 3: Deep Learning Mastery
Fast.ai Practical Deep Learning : https://course.fast.ai/
→ Jeremy Howard's legendary course
→ Build real AI applications from day 1
→ 8-10 weeks of hands-on projects
4️⃣ STEP 4: Natural Language Processing
Stanford CS224N/Ling284 : https://lnkd.in/ebQZ5_T3
→ Master transformers and language models
→ The foundation of ChatGPT and GPT-4
→ 10-12 weeks of cutting-edge NLP
5️⃣ STEP 5: Generative AI Introduction
Microsoft Generative AI for Beginners
: https://lnkd.in/ewsH8gMT
→ 21 Lessons teaching everything you need to know to start building Generative AI applications
→ 6-8 weeks of creative AI
6️⃣ STEP 6: Large Language Models
LLM University by Cohere : https://cohere.com/llmu
→ Fine-tune and deploy production LLMs
→ Build and deploy LLM models
→ 6-8 weeks of enterprise-level skills
https://t.iss.one/CodeProgrammer
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Today, the public mint for Lobsters on TON goes live on Getgems 🦞
This is not just another NFT drop.
In my view, Lobsters is one of the first truly cohesive products at the intersection of blockchain, NFTs, and AI.
Here, the NFT is not just an image and not just a collectible.
Each Lobster is an NFT with a built-in AI agent inside: a digital character with its own soul, on-chain biography, persistent memory, and a unified identity across Telegram, Mini App, Claude, and API.
So you are not just getting an asset in your wallet.
You are getting an AI-native digital character that can interact, remember, and stay consistent across different interfaces.
What makes this especially interesting is the timing.
In the recent video Pavel Durov shared in his post about agentic bots in Telegram, the lobster imagery was right there. Against that backdrop, Lobsters does not feel like a random mint — it feels like a very precise fit for the new narrative:
Telegram-native agents + TON infrastructure + NFT ownership layer + AI utility
Put simply, this is one of the first real attempts to turn an NFT from “just an image” into a digital agent.
Public mint: today, 16:00
Price: 50 TON
👉 Mint your Lobster on Getgems 🦞🦞🦞
This is not just another NFT drop.
In my view, Lobsters is one of the first truly cohesive products at the intersection of blockchain, NFTs, and AI.
Here, the NFT is not just an image and not just a collectible.
Each Lobster is an NFT with a built-in AI agent inside: a digital character with its own soul, on-chain biography, persistent memory, and a unified identity across Telegram, Mini App, Claude, and API.
So you are not just getting an asset in your wallet.
You are getting an AI-native digital character that can interact, remember, and stay consistent across different interfaces.
What makes this especially interesting is the timing.
In the recent video Pavel Durov shared in his post about agentic bots in Telegram, the lobster imagery was right there. Against that backdrop, Lobsters does not feel like a random mint — it feels like a very precise fit for the new narrative:
Telegram-native agents + TON infrastructure + NFT ownership layer + AI utility
Put simply, this is one of the first real attempts to turn an NFT from “just an image” into a digital agent.
Public mint: today, 16:00
Price: 50 TON
👉 Mint your Lobster on Getgems 🦞🦞🦞
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LLM Engineering Roadmap (2026 Practical Guide) 🗺✨
If your goal is to build real LLM apps (not just prompts), follow this order. 🚀
1️⃣ Python + APIs 🐍🔌
You’ll spend most of your time wiring systems.
Learn:
→ functions, classes
→ working with APIs (requests, JSON)
→ async basics
→ environment variables
Resources
→ Python for Everybody
https://lnkd.in/gUqkvnGG
→ Introduction to Python
https://lnkd.in/g7xfYJVZ
→ MLTUT Python Basics Course
https://lnkd.in/gCqfyCGZ
2️⃣ Text Basics (NLP) 📝🧠
You don’t need heavy theory, just the essentials.
Learn:
→ tokenization
→ text cleaning
→ similarity (cosine)
→ basic embeddings idea
Resources
→ Natural Language Processing Specialization
https://lnkd.in/gz_xmqD9
→ NLP in Python
https://lnkd.in/gnpcJxhz
3️⃣ Transformers (What’s happening behind the API) 🤖🔍
Enough to not treat it like a black box.
Learn:
→ tokens, context window
→ attention (high level)
→ why embeddings work
→ limits of LLMs
Resources
→ Generative AI with Large Language Models
https://lnkd.in/gk3PPtyf
→ Hugging Face Transformers Course
https://lnkd.in/ggSR5JNb
4️⃣ Prompting (Make outputs reliable) 💬🎯
Treat prompts like code.
Learn:
→ few-shot examples
→ structured outputs (JSON)
→ system vs user instructions
→ simple evals (does it break?)
Resources
→ Prompt Engineering for ChatGPT
https://lnkd.in/gyg4EiJS
→ Prompt Engineering with LLMs
https://lnkd.in/gn67Mxga
5️⃣ Embeddings + Vector DBs 📊🗄
This is how you add your data.
Learn:
→ embedding generation
→ similarity search
→ indexing
Tools:
→ FAISS
→ Pinecone
→ Chroma
Resources
→ Working with Embeddings
https://lnkd.in/gnngPW4E
→ Vector Databases & Semantic Search
https://lnkd.in/gP2HdMmD
6️⃣ RAG Pipelines 🔗🔄
Most useful apps use this pattern.
Learn:
→ chunking documents
→ retrieval + ranking
→ prompt + context design
→ basic evaluation
Resources
→ Generative AI for Software Development
https://lnkd.in/g3uduecv
→ Build RAG Apps with LangChain
https://lnkd.in/ggXJjgDN
7️⃣ Build Real Applications 🛠💻
Keep them small and usable.
Build:
→ document Q&A (PDF → answers)
→ internal knowledge bot
→ code assistant (repo Q&A)
→ support chatbot
Tools:
→ LangChain
→ LlamaIndex
→ OpenAI APIs
Resources
→ Build LLM Apps with LangChain & Python
https://lnkd.in/g6xXVX_8
→ LLM Applications
https://lnkd.in/gzs8_SRk
8️⃣ Deployment 🚢☁️
Make it usable by others.
Learn:
→ FastAPI endpoints
→ streaming responses
→ caching (reduce cost)
→ logging + monitoring
Tools:
→ FastAPI
→ Docker
→ AWS / GCP
Resources
→Machine Learning Engineering for Production (MLOps)
https://lnkd.in/gCMtYSk5
→ MLOps Fundamentals
https://lnkd.in/g8TGrUzT
https://t.iss.one/DataAnalyticsX✅
If your goal is to build real LLM apps (not just prompts), follow this order. 🚀
1️⃣ Python + APIs 🐍🔌
You’ll spend most of your time wiring systems.
Learn:
→ functions, classes
→ working with APIs (requests, JSON)
→ async basics
→ environment variables
Resources
→ Python for Everybody
https://lnkd.in/gUqkvnGG
→ Introduction to Python
https://lnkd.in/g7xfYJVZ
→ MLTUT Python Basics Course
https://lnkd.in/gCqfyCGZ
2️⃣ Text Basics (NLP) 📝🧠
You don’t need heavy theory, just the essentials.
Learn:
→ tokenization
→ text cleaning
→ similarity (cosine)
→ basic embeddings idea
Resources
→ Natural Language Processing Specialization
https://lnkd.in/gz_xmqD9
→ NLP in Python
https://lnkd.in/gnpcJxhz
3️⃣ Transformers (What’s happening behind the API) 🤖🔍
Enough to not treat it like a black box.
Learn:
→ tokens, context window
→ attention (high level)
→ why embeddings work
→ limits of LLMs
Resources
→ Generative AI with Large Language Models
https://lnkd.in/gk3PPtyf
→ Hugging Face Transformers Course
https://lnkd.in/ggSR5JNb
4️⃣ Prompting (Make outputs reliable) 💬🎯
Treat prompts like code.
Learn:
→ few-shot examples
→ structured outputs (JSON)
→ system vs user instructions
→ simple evals (does it break?)
Resources
→ Prompt Engineering for ChatGPT
https://lnkd.in/gyg4EiJS
→ Prompt Engineering with LLMs
https://lnkd.in/gn67Mxga
5️⃣ Embeddings + Vector DBs 📊🗄
This is how you add your data.
Learn:
→ embedding generation
→ similarity search
→ indexing
Tools:
→ FAISS
→ Pinecone
→ Chroma
Resources
→ Working with Embeddings
https://lnkd.in/gnngPW4E
→ Vector Databases & Semantic Search
https://lnkd.in/gP2HdMmD
6️⃣ RAG Pipelines 🔗🔄
Most useful apps use this pattern.
Learn:
→ chunking documents
→ retrieval + ranking
→ prompt + context design
→ basic evaluation
Resources
→ Generative AI for Software Development
https://lnkd.in/g3uduecv
→ Build RAG Apps with LangChain
https://lnkd.in/ggXJjgDN
7️⃣ Build Real Applications 🛠💻
Keep them small and usable.
Build:
→ document Q&A (PDF → answers)
→ internal knowledge bot
→ code assistant (repo Q&A)
→ support chatbot
Tools:
→ LangChain
→ LlamaIndex
→ OpenAI APIs
Resources
→ Build LLM Apps with LangChain & Python
https://lnkd.in/g6xXVX_8
→ LLM Applications
https://lnkd.in/gzs8_SRk
8️⃣ Deployment 🚢☁️
Make it usable by others.
Learn:
→ FastAPI endpoints
→ streaming responses
→ caching (reduce cost)
→ logging + monitoring
Tools:
→ FastAPI
→ Docker
→ AWS / GCP
Resources
→Machine Learning Engineering for Production (MLOps)
https://lnkd.in/gCMtYSk5
→ MLOps Fundamentals
https://lnkd.in/g8TGrUzT
https://t.iss.one/DataAnalyticsX
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Most AI channels optimize for attention.
We optimize for signal.
• real tools
• reproducible workflows
• technical breakdowns
If you care about depth, not hype
✅ this is for you.
🔣 Join the channel
We optimize for signal.
• real tools
• reproducible workflows
• technical breakdowns
If you care about depth, not hype
✅ this is for you.
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