Machine Learning with Python

🧮 $40/day × 30 days = $1,200/month. That's what my students average. From their phone. In 10 minutes a day. No degree needed. No investment knowledge required. Just Copy & Paste my moves. I'm Tania, and this is real. 👉 Join for Free, Click here #ad 📢 InsideAd

This bot will help you get a course that's available for free for a limited time so you can register before others. Benefit from it t.me/UdemySybot

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

The first bot in Telegram that offers free Udemy coupons https://t.me/UdemySybot

Use our bot to get all free courses @UdemySybot

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.

💸 Low deposit… but NOT low rewards Skin.Club Dropped a small amount just for fun… and ended up getting a solid item from the first case 😅🔥 Honestly didn’t expect that kind of result If you’re tired of overpriced cases, this might be worth trying 👇 👉 Give it a shot here Ad. 18+

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: https://t.me/Udemy26

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.me/CodeProgrammer 🌟 Please more Likes 👍

🔥 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.me/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?

72-hour review of 1,200+ public betting channels shows most “VIP” picks are posted after price moves, not before. Betting Tips King tracks market drift and releases entries early, with time-stamped proof inside. VIP access includes pre-match parlays, live angles, and verified slips. Transparent process, not noise. Join: Betting Tips King | Admin: @KingR33 #ad 📢 InsideAd

🎯 Your lucky case might actually be here Skin.Club Been seeing people talk about this site everywhere… finally tried it myself 👀 Not gonna lie — drops feel way better than most places + kinda addictive 😅🔥 Might be worth testing your luck today 👇 👉 Open your case here Ad. 18+

More likes = more resources

🚀 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. https://t.me/CodeProgrammer ✅

More likes = more resources

🚀 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

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 🔗