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34 articles tagged #Machine Learning.
P.01Explore how AutoML is accelerating AI development in 2026 with new tools, techniques, and trends that businesses need to know.
P.02A practical guide to deploying Small Language Models (SLMs) like Phi-4, Qwen2.5, Gemma 3, and Llama 3.2 in production. Benchmarks, quantization, edge deployment patterns, and when SLMs beat large models.
P.03EditorPickThe network latency between your Django app and your FastAPI ML service is probably longer than inference itself. Here is how to serve models from Django directly.
P.04Not everything needs the cloud. Edge AI is putting real intelligence on devices, sensors, and cameras — with millisecond latency and zero internet dependency. Here is where the technology actually stands.
P.05DeepSeek and Alibaba's Qwen surged from 1% to 15% global AI market share in a single year. With 700M+ Hugging Face downloads, open-source AI from China is reshaping enterprise choices, developer workflows, and the competitive landscape.
P.06TII's Falcon-H1R 7B scores 88.1% on AIME-24 math, outperforming 15B models. Built on a hybrid Transformer-Mamba architecture, it signals a new era for efficient AI. Here's what it means for developers.
P.07The AI industry is shifting from massive general-purpose models to smaller, specialized ones that outperform giants in specific tasks. Here's why this matters and how to take advantage of it.
P.08The AI industry is pivoting from massive models to efficient SLMs offering 10-30x reductions in latency and cost. Learn why smaller is better and how to leverage SLMs in your applications.
P.09Understand overfitting and underfitting in ML models with practical solutions including cross-validation, regularization, early stopping, and data augmentation.
P.10Explore dimensionality reduction techniques — PCA, LDA, t-SNE, and autoencoders — for improving model performance and data visualization.
P.11Discover the key advantages of Random Forest algorithms — high accuracy, resistance to overfitting, feature importance, and handling missing data.
P.12Master hyperparameter tuning with GridSearchCV using KNN, Random Forest, and SVM models with custom scoring functions.
P.13Build a sentiment analysis pipeline with text preprocessing, TF-IDF vectorization, and Multinomial Naive Bayes classification on Twitter data.
P.14Learn how centroid-based clustering algorithms like K-means partition datasets into meaningful groups based on distance metrics.
P.15A guide to clustering algorithm types — partition-based, hierarchical, density-based, and model-based — with use cases and selection criteria.
P.16Understand decision tree algorithms for classification and regression, their pros and cons, and build an Iris classifier with Python code.
P.17Learn SVM theory including hyperplanes and the kernel trick, then build a classifier on the breast cancer dataset using scikit-learn.
P.18Build a logistic regression model to predict diabetes outcomes using the Pima Indians dataset, covering sigmoid functions, feature scaling, and evaluation.
P.19Implement K-Nearest Neighbors classification using scikit-learn with data visualization, model training, and performance evaluation on real datasets.
P.20Understand the KNN algorithm — how it works, distance metrics, choosing K, and its applications in both classification and regression tasks.
P.21Compare Naive Bayes, SVM, Decision Tree, and Random Forest for email spam detection with a complete Python pipeline from data loading to evaluation.
P.22Learn Occam's Razor, regularization, pruning, ensemble methods, cross-validation, Bayesian model selection, genetic algorithms, and more to boost ML performance.
P.23Master MAE, MSE, R², RMSE, accuracy, precision, recall, F1-score, AUC-ROC, and confusion matrices with formulas and Python code examples.
P.24Understand the bias-variance tradeoff in machine learning with mathematical formulas, visual explanations, and strategies to find the right balance.
P.25Build an insurance cost prediction model using multivariate linear regression with one-hot encoding, evaluation metrics, and residual analysis.
P.26EditorPickMaster 7 encoding techniques for categorical variables — one-hot, label, dummy, binning, count, frequency, and target encoding with Python examples.
P.27Build a linear regression model from scratch using scikit-learn, with data visualization, feature selection, and model evaluation metrics.
P.28Explore reinforcement learning fundamentals — agents, environments, states, actions, Q-learning, SARSA, Actor-Critic, and deep RL approaches.
P.29Understand unsupervised learning methods including clustering, dimensionality reduction, anomaly detection, and generative models with practical examples.
P.30EditorPickA structured 100-day data science bootcamp roadmap covering Python, statistics, machine learning, deep learning, and real-world projects.
P.31EditorPickA comprehensive guide covering 10 regression types — linear, polynomial, logistic, ridge, lasso, elastic net, and more — with Python code examples and selection criteria.
P.32Discover how artificial intelligence and machine learning are transforming augmented and virtual reality applications in gaming, education, and beyond.
P.33Understand the key differences between artificial intelligence, machine learning, and deep learning with clear definitions, examples, and real-world applications.
P.34A step-by-step guide to writing and publishing research papers in artificial intelligence, machine learning, and deep learning — from ideation to submission.