68382
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho
Softmax vs Hardmax by hand ✍️ ~ interactive calculator 👉 https://byhand.ai/vhUJDH
Softmax turns a set of raw scores (z) into a probability distribution (Y) over choices (a, b, c, d, e). Instead of just saying which option is best, it tells us how likely each option is to be chosen. In this example, most of the probability mass is concentrated on c, while the other options are still possible but clearly less likely. That's the point of softmax: it converts relative scores into meaningful, comparable probabilities that sum to 100%.
Think of a raffle. Hardmax is when the person who bought the most tickets always wins the prize — the top score takes it, every time. Softmax is when everyone's chance is proportional to the tickets they hold: even if I bought just one ticket, I may still get lucky. Who knows. That's the psychology of softmax.
This is how a language model chooses its next word. Each time a word appears in the training data, it earns a ticket. Hardmax would always speak the word with the most tickets — the same safe choice, over and over. Softmax gives every word a chance proportional to its tickets, so less common words can still be spoken. The word with the most tickets still has the highest chance of winning — just not 100%. That's what lets the model surprise us with its creativity (and also its hallucinations) instead of repeating itself.
/channel/CodeProgrammer 😱
Searched 35 free courses, so you don't have to! 🔍✨
Here are the 35 best free courses: 🎓
1. Data Science: Machine Learning 🤖
Link: https://lnkd.in/gUNVYgGB
2. Introduction to computer science 💻
Link: https://lnkd.in/gR66-htH
3. Introduction to programming with scratch 🧩
Link: https://lnkd.in/gBDUf_Wx
4. Computer science for business professionals 💼
Link: https://lnkd.in/g8gQ6N-H
5. How to conduct and write a literature review 📝
Link: https://lnkd.in/gsh63GET
6. Software Construction 🛠
Link: https://lnkd.in/ghtwpNFJ
7. Machine Learning with Python: from linear models to deep learning 🐍🧠
Link: https://lnkd.in/g_T7tAdm
8. Startup Success: How to launch a technology company in 6 steps 🚀
Link: https://lnkd.in/gN3-_Utz
9. Data analysis: statistical modeling and computation in applications 📊
Link: https://lnkd.in/gCeihcZN
10. The art and science of searching in systematic reviews 🔎
Link: https://lnkd.in/giFW5q4y
11. Introduction to conducting systematic review 📋
Link: https://lnkd.in/g6EEgCkW
12. Introduction to computer science and programming using python 🖥
Link: https://lnkd.in/gwhMpWck
13. Introduction to computational thinking and data science 💡
Link: https://lnkd.in/gfjuDp5y
14. Becoming an Entrepreneur 💸
Link: https://lnkd.in/gqkYmVAW
15. High-dimensional data analysis 📈
Link: https://lnkd.in/gv9RV9Zc
16. Statistics and R 📉
Link: https://lnkd.in/gUY3jd8v
17. Conduct a literature review 📚
Link: https://lnkd.in/g4au3w2j
18. Systematic Literature Review: An Introduction 🧐
Link: https://lnkd.in/gVwGAzzY
19. Introduction to systematic review and meta-analysis 🧮
Link: https://lnkd.in/gnpN9ivf
20. Creating a systematic literature review ✍️
Link: https://lnkd.in/gbevCuy6
21. Systematic reviews and meta-analysis 📊
Link: https://lnkd.in/ggnNeX5j
22. Research methodologies 🕵️♂️
Link: https://lnkd.in/gqh3VKCC
23. Quantitative and Qualitative research for beginners 📊💬
Link: https://shorturl.at/uNT58
24. Writing case studies: science of delivery 📑
Link: https://shorturl.at/ejnMY
25. research methodology: complete research project blueprint 🗺
Link: https://lnkd.in/gFU8Nbrv
26. How to write a successful research paper 📜
Link: https://lnkd.in/g-ni3u5q
27. Research proposal bootcamp: how to write a research proposal 🏃♂️
Link: https://lnkd.in/gNRitBwX
28. Understanding technology 📱
Link: https://lnkd.in/gfjUnHfd
29. Introduction to artificial intelligence with Python 🤖🐍
Link: https://lnkd.in/gygaeAcY
30. Introduction to programming with Python 💻
Link: https://lnkd.in/gAdyf6xR
31. Web programming with Python and JavaScript 🌐
Link: https://lnkd.in/g_i5-SeG
32. Understanding Research methods 🔬
Link: https://lnkd.in/g-xBFj4v
33. How to write and publish a scientific paper 📢
Link: https://lnkd.in/giwTe2is
34. Introduction to systematic review and meta-analysis 📊
Link: https://lnkd.in/gnpN9ivf
35. Research for impact 🌍
Link: https://lnkd.in/gRsWsUsq
Self Attention vs Cross Attention by hand ✍️
Resize the matrices yourself 👉 https://byhand.ai/aMisxP
Two attention mechanisms, side by side. Both project X into queries; both compute attention via S = Kᵀ × Q and F = V × A. The only difference is the source of K and V.
Self attention uses X for everything. Q, K, and V all come from projecting X. Each X token attends to every other X token. The score matrix S is square — 128 × 128.
Cross attention uses X for queries and a second sequence E for keys and values. Each X token attends to every E token instead. The score matrix S is rectangular — 64 × 128.
Notice what's shared and what's not:
X is the same in both — same 36 × 128 input.
Q and K share the 16 dimension — that's what makes the dot product Kᵀ × Q valid in either case.
V dimensions are independent: self-attention uses 12, cross-attention uses 12. The choice doesn't depend on which mechanism you're using; it depends on what output dimension your downstream layer expects.
/channel/CodeProgrammer
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
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 🦞🦞🦞
🔥 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.
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
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.
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
/channel/CodeProgrammer 🌟
Please more Likes 👍
🚀 Machine Learning Workflow: Step-by-Step Breakdown
Understanding the ML pipeline is essential to build scalable, production-grade models.
👉 Initial Dataset
Start with raw data. Apply cleaning, curation, and drop irrelevant or redundant features.
Example: Drop constant features or remove columns with 90% missing values.
👉 Exploratory Data Analysis (EDA)
Use mean, median, standard deviation, correlation, and missing value checks.
Techniques like PCA and LDA help with dimensionality reduction.
Example: Use PCA to reduce 50 features down to 10 while retaining 95% variance.
👉 Input Variables
Structured table with features like ID, Age, Income, Loan Status, etc.
Ensure numeric encoding and feature engineering are complete before training.
👉 Processed Dataset
Split the data into training (70%) and testing (30%) sets.
Example: Stratified sampling ensures target distribution consistency.
👉 Learning Algorithms
Apply algorithms like SVM, Logistic Regression, KNN, Decision Trees, or Ensemble models like Random Forest and Gradient Boosting.
Example: Use Random Forest to capture non-linear interactions in tabular data.
👉 Hyperparameter Optimization
Tune parameters using Grid Search or Random Search for better performance.
Example: Optimize max_depth and n_estimators in Gradient Boosting.
👉 Feature Selection
Use model-based importance ranking (e.g., from Random Forest) to remove noisy or irrelevant features.
Example: Drop features with zero importance to reduce overfitting.
👉 Model Training and Validation
Use cross-validation to evaluate generalization. Train final model on full training set.
Example: 5-fold cross-validation for reliable performance metrics.
👉 Model Evaluation
Use task-specific metrics:
- Classification – MCC, Sensitivity, Specificity, Accuracy
- Regression – RMSE, R², MSE
Example: For imbalanced classes, prefer MCC over simple accuracy.
💡 This workflow ensures models are robust, interpretable, and ready for deployment in real-world applications.
/channel/CodeProgrammer ✅
On GitHub, a repository has been curated comprising over 500 valuable services designed for daily tasks. 📂🛠️
The collection includes projects compatible with various operating systems, smartphones, web browsers, and torrent clients, alongside tools for productivity, software development, design, and content management. 🖥️📱🎨
https://github.com/Furthir/awesome-useful-projects?tab=readme-ov-file#creative 🔗
Confused between ML, NLP, Generative, and other AI models? 🤔
Here’s a quick breakdown of the 6 most important types of AI models you must understand in 2026👇
1. Machine Learning Models 🤖
They learn from labeled and unlabeled data to classify, predict, and detect patterns. Think decision trees, SVMs, and XGBoost.
2. Deep Learning Models 🧠
Neural networks built for unstructured data like images, audio, and text. Includes CNNs, RNNs, Transformers, and GANs.
3. NLP Models 💬
Focused on understanding and generating human language - used in chatbots, summarizers, and assistants like GPT and BERT.
4. Generative Models ✨
These models create, from text to images to music. Powered by models like GPT-4, DALL·E, and StyleGAN.
5. Hybrid Models 🔗
Combine the best of rule-based and neural AI. Perfect for use cases needing both reasoning and context awareness (e.g., RAG pipelines).
6. Computer Vision Models 👁
Built for images and videos. Used in object detection, facial recognition, and medical scans - powered by models like YOLO and ResNet.
Each AI model has its strengths and knowing which one fits your use case is half the battle. Save this guide as your cheat sheet! 📝✅
Most people learn Python in random order. No wonder they feel stuck.
This roadmap fixes that.
Here are the 5 layers every data professional must master, in order:
𝟭. 𝗖𝗼𝗿𝗲 𝗣𝘆𝘁𝗵𝗼𝗻 (𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻)
Variables, loops, functions, error handling, collections.
Do not skip this. Everything else breaks without it.
𝟮. 𝗗𝗮𝘁𝗮 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 & 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴
Pandas, NumPy, file handling, SQL integration, data cleaning.
This is where your actual job begins.
𝟯. 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗟𝗶𝗯𝗿𝗮𝗿𝗶𝗲𝘀
Matplotlib, Seaborn, EDA, statistical functions, hypothesis testing.
Can you turn raw data into a decision? This layer teaches you how.
𝟰. 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 & 𝗠𝗟
Scikit-Learn, clustering, feature engineering, big data tools.
This is what gets you promoted.
𝟱. 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 & 𝗕𝗲𝘀𝘁 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀
Git, virtual environments, unit testing, workflow scheduling.
This is what separates professionals from beginners.
The mistake most people make, they jump straight to ML without nailing the foundation.
You cannot build insights on broken code.
Master the layers. In order. With real data.
Save this roadmap and share it with someone who needs direction.
Where are you on this right now?
♻️ Repost to help someone learning Python the right way
/channel/CodeProgrammer ✅
Register for the FREE Python Demo Session!
📅 Date: 30 April 2026
⏰ Time: 7:30 PM
🔗 Zoom Link: https://us06web.zoom.us/meeting/register/HSOTmzzpTkGIGm9C9oGbaA
Everyone is welcome!
/channel/CodeProgrammer
GitHub repositories to enhance your Python proficiency:
- Web development with Django — https://github.com/django/django
- Data Science tools — https://github.com/rasbt/python-machine-learning-book
- Algorithmic challenges — https://github.com/TheAlgorithms/Python
- Machine learning recipes — https://github.com/ageron/handson-ml2
- Testing best practices — https://github.com/pytest-dev/pytest
- Automation scripts — https://github.com/soimort/you-get
- Advanced Python concepts — https://github.com/faif/python-patterns
Bookmark and share
/channel/CodeProgrammer 🌟
Follow the Machine Learning with Python channel on WhatsApp: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
Читать полностью…
11 Plots Data Scientists Use 90% of the Time 📊🚀
Here’s the secret → Data scientists don’t actually use 100+ types of charts. 🤫
When real decisions are on the line, it always comes back to the same 11.
/channel/DataScienceM
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
/channel/DataAnalyticsX ✅
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
/channel/CodeProgrammer ✅
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
/channel/CodeProgrammer 🔰
Master DevOps 2026
/channel/DataAnalyticsX 🌟
Complete Python Course: Learn From Beginner To Advanced
Complete Python Course From Beginner To Advanced...
🏷 Category: N/A
🌍 Language: English (US)
👥 Students: 35,544 students
⭐️ Rating: 4.2/5.0 (773 reviews)
🏃♂️ Enrollments Left: N/A
⏳ Expires In: 0D:4H:4M
💰 Price: $9.59 => FREE
🆔 Coupon: CM260417IN
⚠️ Note: You may need to watch a short ad to access the course. This helps keep the service free for everyone. 🙏
💎 By: /channel/Udemy26
🔥 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.
/channel/CodeProgrammer
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?
/channel/CodeProgrammer ✅
🚀 Thrilled to announce a major milestone in our collective upskilling journey! 🌟
I am incredibly excited to share a curated ecosystem of high-impact resources focused on Machine Learning and Artificial Intelligence. By consolidating a comprehensive library of PDFs—from foundational onboarding to advanced strategic insights—into a single, unified repository, we are effectively eliminating search friction and accelerating our learning velocity. 📚✨
This initiative represents a powerful opportunity to align our technical growth with future-ready priorities, ensuring we are always ahead of the curve. 💡🔗
⛓️ Unlock your potential here:
https://github.com/Ramakm/AI-ML-Book-References
#MachineLearning #AI #ContinuousLearning #GrowthMindset #TechCommunity #OpenSource
Found this - AI Builders, pay attention.
A curated marketplace just launched where AI builders list their systems and get paid - setup fee + monthly recurring. No sales, no client chasing. They handle everything, you just build.
100% free to join. No fees, no subscription, no hidden costs. They only take 20% when you earn - on setup fee and recurring. That's it.
Accepted builders are earning from day one. Spots are limited by design.
Takes 5 minutes to apply. You'll need a 90-second video of your system in action.
→ brainlancer.com
Daily updates from the CEO: https://www.linkedin.com/in/soner-catakli/
Follow, like & share in "your network" - these guys are building something seriously worth watching.
PS: First systems go live tomorrow. Builders who join early get the best positioning... investor-backed marketing means they bring the clients to you.
Super VIP Cheatsheet: Machine Learning
/channel/CodeProgrammer
AI Engineer 2026 Complete Course, GEN AI, Deep, Machine, LLM
Master Machine Learning, Deep Learning, LLMs & AI Systems with hands-on, real-world projects...
🏷 Category: N/A
🌍 Language: English (US)
👥 Students: 6,344 students
⭐️ Rating: 4.0/5.0 (38 reviews)
🏃♂️ Enrollments Left: N/A
⏳ Expires In: 0D:0H:0M
💰 Price: $23.03 => FREE
🆔 Coupon: 25BBPMXNVD35
⚠️ Note: You may need to watch a short ad to access the course. This helps keep the service free for everyone. 🙏
💎 By: /channel/Udemy26