Machine Learning in Python | Complete Crash Course | Python | Scikit-learn | (Part-1/2) скачать в хорошем качестве

Machine Learning in Python | Complete Crash Course | Python | Scikit-learn | (Part-1/2)

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... ✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅ --------------------------------------------------------------------------------------------------------------------------------------- Ready to dive into Machine Learning with Python? Welcome to Part 1 of our Complete Crash Course! In this video, we’ll introduce the core concepts of machine learning, set up your environment, and walk through the fundamental steps—from importing libraries and managing datasets to performing exploratory data analysis. We’ll focus on scikit-learn, one of the most popular Python libraries for ML, to build and evaluate simple models. Whether you’re a beginner or need a refresher, this crash course is designed to help you understand machine learning in a clear, practical way. What You’ll Learn in Part 1 Environment Setup Installing and configuring Python and scikit-learn Overview of essential libraries like NumPy, Pandas, and Matplotlib Data Preparation & Cleaning Importing datasets and handling missing values Data exploration and visualization for insights Basic Machine Learning Concepts Key terminology: features, labels, training, testing A quick overview of supervised learning and unsupervised learning Your First scikit-learn Model Step-by-step guide to building a simple classification or regression model Understanding model training, validation, and performance metrics What’s Next? A teaser for Part 2, where we’ll dive deeper into advanced techniques, hyperparameter tuning, and best practices Who Is This For? Beginners: No prior experience needed—just basic familiarity with Python and a passion to learn. Data Enthusiasts: Developers, analysts, or students interested in machine learning foundations. Career Changers: Professionals pivoting to data science or AI roles who need a clear starting point. Why This Crash Course? Hands-On Approach: Real examples and guided coding sessions. Beginner-Friendly: We break down complex ideas into easy-to-follow steps. Practical Insights: Gain skills you can apply to real-world data science projects. --------------------------------------------------------------------------------------------------------------------------------------- ✅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] Timestamps: 00:00:00 Introduction to the Crash Course 00:02:07 What is Machine Learning? 00:21:45 Types of Machine Learning 00:31:45 Supervised Machine Learning 00:50:05 Unsupervised Machine Learning 00:58:19 Semi-supervised Machine Learning 00:59:18 Reinforcement Learning 01:07:07 Application of ML 01:13:58 Data is Important for ML 01:19:04 Scikit-learn for ML 01:24:59 Scikit-learn in Python 01:51:02 Intermediate ML in Python 02:27:32 Metrics for Classification and Regression 02:57:26 ML model building and deployment 03:31:54 What is an algorithm? 03:40:19 Training and Testing data, Features, Labels 03:44:37 Overfitting vs. Underfitting 03:54:51 Data Pre-processing 04:32:42 Imputing Missing Values Methods in python 05:27:43 Data Scaling and Normalization Theory 05:49:27 Feature Scaling vs. Normalization 06:01:20 What is Feature Encoding? 06:14:25 Why do we need feature encoding? 06:27:09 Regression in one go Theory 07:08:37 Logistic Regression 07:16:45 Regression vs. Classification Metrics 07:24:57 Testing Data Matters 07:45:29 Support Vector Machines (SVMs) Theory 08:09:17 K-nearest Neighbours (KNNs) Theory 08:31:05 Euclidean Distance in ML 08:50:05 Manhattan Distance in ML 08:59:15 Minkowski Distance in ML 09:11:54 Hamming Distance in ML 09:16:49 Algorithms we learned so far 09:24:34 Decision Tree Algorithms Theory 09:34:07 Elements of Decision Tree Algorithm 09:46:58 Entropy, gini impurity and information gain 10:09:07 Ensemble Algorithms in ML 10:30:28 Random Forest Algorithm Theory 10:49:22 Ensemble Algorithms Family 10:57:22 Boosting in Ensemble Algorithm 11:30:38 Boosting Algorithm vs. Neural Network 11:44:08 Part-2 of Machine Learning Crash Course