Machine Learning in Python | Complete Crash Course | Python | Scikit-learn | (Part-2/2) скачать в хорошем качестве

Machine Learning in Python | Complete Crash Course | Python | Scikit-learn | (Part-2/2)

You can book One to one consultancy session with me on Mentoga: https://mentoga.com/muhammadaammartufail #codanics #dataanalytics #pythonkachilla #pkc24 ✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅ Python ka chilla 2024 You can now register for Python ka chilla 2024 This is a paid course which you can register and find more information at the following link: https://forms.gle/kUU3eZJsFRb7Cn6r8 ✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅ Here you can access all the codes and datasets from Python ka chilla 2024: https://github.com/AammarTufail/pytho... ✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅ --------------------------------------------------------------------------------------------------------------------------------------- Welcome to Part 2 of our Machine Learning in Python crash course! Building on the foundations from Part 1, we’ll explore more advanced concepts to help you optimize, tune, and evaluate your models using scikit-learn. From cross-validation strategies to hyperparameter tuning, this session will guide you through the crucial steps that turn basic models into powerful tools for real-world applications. Whether you’re refining your existing skills or continuing from Part 1, this video has everything you need to take your Python ML projects to the next level! What’s Covered in Part 2 Cross-Validation & Model Evaluation Why cross-validation is vital for reliable performance metrics Common techniques (K-Fold, Stratified K-Fold) and when to use them Analyzing performance across multiple folds for robust insights Hyperparameter Tuning Understanding grid search, random search, and Bayesian optimization Practical tips to balance performance with computational resources Best practices to avoid overfitting or underfitting Regularization & Model Optimization How techniques like L1 (Lasso) and L2 (Ridge) regularization help control complexity Insights into ensemble methods (e.g., Random Forest, Gradient Boosting) When and why to choose certain algorithms over others Feature Engineering & Selection Strategies to select relevant features and reduce dimensionality Implementing transformations and pipelines in scikit-learn Data preprocessing for improved model performance Practical Coding Walkthrough Building a complete ML pipeline from data loading to final model evaluation Code snippets and real-world examples in Python Tips to interpret results and refine your approach Why This Matters Maximize Performance: Tuning and validation techniques can significantly boost your model’s accuracy and reliability. Efficient Workflows: Learn to streamline iterative tasks—like repeated training and validation—by leveraging scikit-learn’s powerful utilities. Real-World Impact: Gain the confidence to deploy or present your models, knowing they’re optimized and tested rigorously. Who Should Watch Intermediate & Advanced Coders: Those comfortable with Python basics, ready to deep-dive into model optimization. --------------------------------------------------------------------------------------------------------------------------------------- ✅Our Free Books: https://codanics.com/books/abc-of-sta... ✅Our website: https://www.codanics.com ✅Our Courses: https://www.codanics.com/courses ✅Our YouTube Channel:    / @codanics   ✅ Our whatsapp channel: https://whatsapp.com/channel/0029Va7n... ✅Our Facebook Group:   / codanics   ✅Our Discord group for community Discussion:   / discord   ✉️For more Details contact us at [email protected] Time Stamps: 00:00:00 Part-1 of this Lecture is here 00:00:15 Evaluation Metrics in ML 00:04:45 Regression Metrics in ML 00:24:07 Classification Metrics in ML 00:55:06 Complete Previous tasks 00:55:46 Removing Outliers in Python 01:12:09 Data Scaling and Preprocessing 01:30:49 Data Transformation in Python 01:35:45 Data Normalization in Python 01:46:52 Pipeline in ML 01:54:09 Pipeline in Python using scikit-learn 02:05:05 Feature Encoding in Python 02:17:25 Intermediate use of sk-learn for ML 02:42:38 Improving ML model performance 02:57:18 Polynomial Regression in Python 03:05:30 Kaggle is important for ML 03:14:39 Ridge Regression in Python 03:40:13 Lasso Regression in Python 04:09:50 Logistic Regression and Classification metrics in Python 04:29:04 KNN in Python 04:41:54 SVM in Python 04:52:04 Decision Trees Algorithm in Python 05:07:32 Random Forest Algorithm in Python 05:19:12 CatBoost Algorithm in python 05:31:55 Naive Bayes Algorithm 05:48:10 Types of Naive Bayes Algorithm 05:54:27 Naive Bayes Algorithms in python for ML 05:58:15 Cross validation methods 06:10:49 Hyperparameter Tuning in python for ML 06:27:02 Best model selection 06:45:53 All ML models so far 06:48:10 PyCaret for Automatic Machine Learning 07:27:54 PyCaret for Regression Tasks 07:44:36 Like Share and Subscribe