Must-Know Python Libraries for Data Science: NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, SciPy, Statsmodels, TensorFlow, Plotly, Jupyter

What is learning Python like in 2026? What's the best path to follow? Egor Howell shares an up-to-date streamlined roadmap for aspiring data professionals.

Discover the top 10 Python machine learning libraries for data science, including scikit-learn, TensorFlow, and Keras, and learn how to choose the best one for your project https://lnkd.in/gShWVMvJ #PythonMachineLearningLibraries Read the full article https://lnkd.in/gShWVMvJ

🚀 Master Forecasting End-to-End with Forecasting with Python (Version 1 & Version 2) 📈🐍 If you want to build serious forecasting expertise—from absolute fundamentals to cutting-edge advanced methods—my books Forecasting with Python Version 1 & Version 2 provide a complete, structured roadmap. What You Will Learn: 1. Time Series Foundations 2. Understanding time series data and its unique properties 3. Loading, indexing, resampling, rolling windows, and shifting 4. Handling missing values and outliers 5. Professional time series visualization 6. Exploratory Analysis & Diagnostics 7. Trend / Seasonality / Residual decomposition 8. ACF / PACF interpretation 9. Stationarity and unit root testing (ADF / KPSS) 10. Seasonality detection using FFT / Periodogram Data Preparation for Forecasting: 1. Proper train/test splits for time series 2. Time series cross-validation 3. Timestamp feature engineering 4. Normalization, differencing, and multiple seasonalities Classical Forecasting Methods: 1. Naïve and baseline forecasting 2. Moving averages (SMA / WMA / EMA) 3. Simple Exponential Smoothing 4. Holt’s Trend Method 5. Holt-Winters Triple Exponential Smoothing 6. ETS Framework 7. ARIMA / SARIMA & Box-Jenkins 8. Full derivation of AR / MA / ARIMA 9. Identifying p, d, q using ACF/PACF 10, ARIMA diagnostics and model fitting 11. Seasonal ARIMA (SARIMA) Advanced Forecasting ModelsL: 1. Intermittent Demand Models (Croston, SBA, TSB) 2. ARCH / GARCH Volatility Models 3. Bayesian Forecasting 4. Markov Chains & Hidden Markov Models Machine Learning for Time Series: 1. Decision Trees 2. Random Forests 3. Support Vector Regression 4. Deep Learning & Modern AI Forecasting 5. LSTM / GRU / Seq2Seq Models 6. N-BEATS / N-HiTS 7. Temporal Fusion Transformer (TFT) 8. PatchTST / iTransformer / Mamba SSM 9. State Space / Probabilistic / Advanced Methods 10. Kalman Filters 11. Advanced State Space Models 12. Gaussian Processes 13. Density Forecasting Foundation Models & Production Forecasting: 1. Chronos / TimeGPT / Moirai 2. DeepAR / Normalizing Flows 3. Causal Forecasting / Intervention Analysis 4. Production Pipelines & Online Learning Why These Books Stand Out ✅ Beginner to Advanced in Logical Sequence ✅ Mathematical Intuition + Theory + Python Implementation ✅ Production-Ready Python Code Included ✅ Designed for Real Industry Application Learn the concept → Understand the math → Implement in Python → Apply in practice 📩 To Purchase: Email: krishnaidu@mathnal.tech WhatsApp: +91-7993651356 Invest in one of the most valuable analytical skills in modern business, analytics, and data science. #Forecasting #TimeSeriesAnalysis #PythonProgramming #DataScience #MachineLearning #PredictiveAnalytics #DemandForecasting #BusinessAnalytics #SupplyChainAnalytics #ARIMA #DeepLearning #ForecastingWithPython #LearnPython #Analytics #TimeSeriesForecasting

🚀 Master Forecasting End-to-End with Forecasting with Python (Version 1 & Version 2) 📈🐍 If you want to build serious forecasting expertise—from absolute fundamentals to cutting-edge advanced methods—my books Forecasting with Python Version 1 & Version 2 provide a complete, structured roadmap. What You Will Learn: 1. Time Series Foundations 2. Understanding time series data and its unique properties 3. Loading, indexing, resampling, rolling windows, and shifting 4. Handling missing values and outliers 5. Professional time series visualization 6. Exploratory Analysis & Diagnostics 7. Trend / Seasonality / Residual decomposition 8. ACF / PACF interpretation 9. Stationarity and unit root testing (ADF / KPSS) 10. Seasonality detection using FFT / Periodogram Data Preparation for Forecasting: 1. Proper train/test splits for time series 2. Time series cross-validation 3. Timestamp feature engineering 4. Normalization, differencing, and multiple seasonalities Classical Forecasting Methods: 1. Naïve and baseline forecasting 2. Moving averages (SMA / WMA / EMA) 3. Simple Exponential Smoothing 4. Holt’s Trend Method 5. Holt-Winters Triple Exponential Smoothing 6. ETS Framework 7. ARIMA / SARIMA & Box-Jenkins 8. Full derivation of AR / MA / ARIMA 9. Identifying p, d, q using ACF/PACF 10, ARIMA diagnostics and model fitting 11. Seasonal ARIMA (SARIMA) Advanced Forecasting ModelsL: 1. Intermittent Demand Models (Croston, SBA, TSB) 2. ARCH / GARCH Volatility Models 3. Bayesian Forecasting 4. Markov Chains & Hidden Markov Models Machine Learning for Time Series: 1. Decision Trees 2. Random Forests 3. Support Vector Regression 4. Deep Learning & Modern AI Forecasting 5. LSTM / GRU / Seq2Seq Models 6. N-BEATS / N-HiTS 7. Temporal Fusion Transformer (TFT) 8. PatchTST / iTransformer / Mamba SSM 9. State Space / Probabilistic / Advanced Methods 10. Kalman Filters 11. Advanced State Space Models 12. Gaussian Processes 13. Density Forecasting Foundation Models & Production Forecasting: 1. Chronos / TimeGPT / Moirai 2. DeepAR / Normalizing Flows 3. Causal Forecasting / Intervention Analysis 4. Production Pipelines & Online Learning Why These Books Stand Out ✅ Beginner to Advanced in Logical Sequence ✅ Mathematical Intuition + Theory + Python Implementation ✅ Production-Ready Python Code Included ✅ Designed for Real Industry Application Learn the concept → Understand the math → Implement in Python → Apply in practice 📩 To Purchase: Email: krishnaidu@mathnal.tech WhatsApp: +91-7993651356 Invest in one of the most valuable analytical skills in modern business, analytics, and data science. #Forecasting #TimeSeriesAnalysis #PythonProgramming #DataScience #MachineLearning #PredictiveAnalytics #DemandForecasting #BusinessAnalytics #SupplyChainAnalytics #ARIMA #DeepLearning #ForecastingWithPython #LearnPython #Analytics #TimeSeriesForecasting

Python is the silent backbone of AI 🐍 Everyone talks about AI models. Few talk about what actually runs them. It’s Python. Behind almost every major AI breakthrough: • Data is processed using Python 📊 • Models are trained using Python libraries 🧠 • APIs are built and deployed using Python 🔗 • Automation pipelines run on Python ⚙️ From research labs to startups, Python is everywhere 🌍 Libraries like: • TensorFlow • PyTorch • Scikit-learn • Pandas …have turned complex AI into something developers can actually build with. Why Python? Because it’s: • Simple to learn • Extremely flexible • Backed by a massive ecosystem 🌐 • Built for fast development 🚀 AI didn’t just grow because of ideas 💡 It scaled because of tools 🛠️ And Python became that tool. That’s why I’m not just learning AI. I’m learning to build with Python 💻 Because in an AI-first world, understanding the backbone matters. If you want to start, here are 5 great Python courses 📚 • Python for Everybody – University of Michigan (Coursera) • CS50’s Introduction to Programming with Python – Harvard University • Complete Python Course – CodeWithHarry • Python for Data Science & AI – IBM

If you're in the early stages of your data science journey, you might wonder how to go about learning Python — or if it's even necessary in the age of AI coding agents. Egor Howell offers clear and actionable insights in his new article.

Hyperparameter Optimization Machine Learning using bayesian optimization #machinelearning #datascience #hyperparameteroptmization #bayesianoptimization Bayesian Optimization Pure Python implementation of bayesian global optimization with gaussian processes. This is a constrained global optimization package built upon bayesian inference and gaussian processes, that attempts to find the maximum value of an unknown function in as few iterations as possible. This technique is particularly suited for optimization of high cost functions and situations where the balance between exploration and exploitation is important. Bayesian optimization works by constructing a posterior distribution of functions (gaussian process) that best describes the function you want to optimize. As the number of observations grows, the posterior distribution improves, and the algorithm becomes more certain of which regions in parameter space are worth exploring. https://lnkd.in/gq9d2Pi6

Day-7 Python + AI: Understanding Tuples for Efficient Data Handling Tuples are an important data type in Python, especially useful in AI when working with fixed and secure data. Why Tuples Matter in AI - Immutable (cannot be changed), ensuring data integrity - Faster than lists for fixed data - Useful for storing coordinates, labels, and structured data Example Program # Using tuple in an AI-like scenario data = (1, 2, 3, 4, 5) # Simple processing result = tuple(x * 2 for x in data) print("Original Data:", data) print("Processed Data:", result) Benefits of Using AI with Python - Ensures data safety with immutable structures - Improves performance in data handling - Simple and efficient for structured datasets - Scalable for real-world AI applications Understanding tuples helps in building reliable and efficient AI systems using Python. #Python #AI #MachineLearning #DataScience #Programming

🚀 Discovering the Power of Recommendation Systems in Python In the world of machine learning, recommendation systems are key to personalizing experiences on platforms like Netflix or Amazon. This article explores how to build one from scratch using Python, focusing on collaborative and content-based approaches to predict user preferences efficiently. 🔍 Understanding the Fundamentals Recommendation systems analyze patterns in user and item data to suggest relevant options. They are divided into: - Collaborative filtering: Uses similarities between users or items, such as the matrix factorization method. - Content-based filtering: Based on item features, such as genres or descriptions. 📊 Practical Implementation with Libraries Python offers powerful tools for this: - Surprise: Ideal for collaborative filtering, with algorithms like KNN and SVD ready to use. - Scikit-learn: For preprocessing and evaluation metrics, such as RMSE to measure accuracy. - Pandas and NumPy: Handle rating datasets, like the classic MovieLens. The typical process includes loading data, training models, and generating recommendations. For example, with Surprise, you can train an SVD model in minutes and predict ratings for specific users, optimizing hyperparameters with cross-validation. ⚡ Challenges and Best Practices Face problems like the "cold start" for new users, solving it with hybrids that combine methods. Evaluate with metrics like precision and recall, and scale using Spark for large datasets. The article includes step-by-step code for a functional prototype. For more information visit: https://enigmasecurity.cl #MachineLearning #Python #RecommendationSystems #DataScience #ArtificialIntelligence If you're passionate about cybersecurity and tech, consider donating to Enigma Security for more content: https://lnkd.in/er_qUAQh Connect with me on LinkedIn to discuss more: https://lnkd.in/eXXHi_Rr 📅 Mon, 13 Apr 2026 09:35:56 GMT 🔗Subscribe to the Membership: https://lnkd.in/eh_rNRyt

#Python Python + Pandas → Data Manipulation Python + Scikit-learn → ML Engineering Python + TensorFlow → Deep Learning Python + Matplotlib → Data Visualization Python + Seaborn → Advanced Charts Python + BeautifulSoup → Web Scraping Python + FastAPI → Performance APIs Python + SQLAlchemy → DB Access Python + OpenCV → Computer Vision