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

[Codanics]

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

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

Python ka chilla 2024
You can now register for Python ka chilla 2024-25.
This is a paid course which you can register and find more information at the following link:

[JunnuAhmed]

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[ZahidAli-pkte]

جزاک اللّه خیر بھائی ۔ اللّه آپ کی فہم و فراست میں اور برکت عطا فرمائے آمین

[MuhammadHassan-iv]

sir allah apko boooht zada kamyabi ro seht da ap ki wja sa hm youth ko kuch sikhna ka moka mila

[MedicMindset]

Great breakdown of machine learning concepts! The video covers key topics like:

- *The vast amount of data* available globally, including stored and uncollected data (e.g., in DNA/neurons).
- *Ensemble learning techniques:*
- *Bagging*: Training multiple models on different subsets of data.
- *Boosting*: Sequentially improving model performance by focusing on weaknesses.
- *Stacking*: Combining multiple models' predictions.
- *Types of ML problems:* Supervised (classification/regression), unsupervised (clustering), and reinforcement learning.
- *Data preprocessing* steps like encoding categorical variables, handling missing data, and normalization.
- *Evaluation metrics* for regression (RMSE, R²) and classification.
- *Decision trees and Random Forests*, including entropy, Gini index, and information gain.
- *Dimensionality reduction* (PCA), *k-NN*, and *basic neural networks*.
- *Practical explanations with Python code snippets*.

The video does a great job of emphasizing the importance of mastering fundamentals before diving into complex algorithms.

[Codecraft-tube]

Few days ago, my cousin who is an IT professional suggested me to learn machine learning. He also explained pros and cons. He advised me to follow my interest. Now after scrolling many tutorials I finally found the worthy one. Your tutorial is best. You explain each concepts clearly - The way I understand concepts better. I appreciate your efforts and thanks a lot for this tutorial. 🥰

[FutureMan-]

Awesome and kamal sir g allah ap mazeed daaay and dont stop uplodes videos

[ARCGISPROMASTERCLASS]

Excellant way of Teaching Machine Learning, Thank You sir

[SherazRaees-nx]

Sir ap cheetah hain.... Allah apko iska duniya ar akhirat mai bahoot zaida de

[gujarm]

MashAllah great Job Sir!
Learning from Canada
JazakAllah u Khair
Ramadan Mubarak!

[adnanhyder]

MashaAllah hats of to you sir for all of your content and harrdwork 👏👏

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Ya phir YouTube ap ko bohattt acha pay kar Raha ha in keywords Jo ap continuously itnay achay lecture deliver kar rHay ha

[NotesandPens-rowx]

Sir mein crash course he search kr raha tha 2 din se :D .. Mashallah your video lands as an angel :)

[AppliedmathematicsStatisti-ee]

Being a teacher and PhD in statistics, I appreciate your great efforts in applied statistics work. WellDone Dr Sahib.

[HafizMuhammadUsama]

I love you sir!
Love you for your teaching method

[shahabahmad]

From code most important is concept, and i am here at right place, thank you sir

[ZahidMughal-gu]

As Salaam O Alaikum Sir Aap ka Teaching Method Unique Hai I was watching your Lectures for many days There are many chanels on youtube but your teaching is very unique and so easy I have really like your teaching and I have learnt many things the video of machine learning and python 101 etc. Thanks for the Struggle and Really Hardwork for us. I really inspire with your content.

[saadfarooq]

ماشاءاللہ
Really good work
Spread knowledge

[BinishaMaharjan-mq]

i was really confused about from where to begin and this video showed me a path thank you

[akashawan]

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