You can book One to one consultancy session with me on Mentoga: https://mentoga.com/muhammadaammartufail
#codanics #dataanalytics #pythonkachilla #pkc24
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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
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Here you can access all the codes and datasets from Python ka chilla 2024: https://github.com/AammarTufail/python-ka-chilla-2024
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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.
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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
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