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

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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:
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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.
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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
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