Become a Python pro! 🚀 This comprehensive tutorial takes you from beginner to hero, covering the basics, machine learning, and web development projects.

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📖 TABLE OF CONTENT
00:00:00 Introduction
00:01:49 Installing Python 3
00:06:10 Your First Python Program
00:08:11 How Python Code Gets Executed
00:11:24 How Long It Takes To Learn Python
00:13:03 Variables
00:18:21 Receiving Input
00:22:16 Python Cheat Sheet
00:22:46 Type Conversion
00:29:31 Strings
00:37:36 Formatted Strings
00:40:50 String Methods
00:48:33 Arithmetic Operations
00:51:33 Operator Precedence
00:55:04 Math Functions
00:58:17 If Statements
01:06:32 Logical Operators
01:11:25 Comparison Operators
01:16:17 Weight Converter Program
01:20:43 While Loops
01:24:07 Building a Guessing Game
01:30:51 Building the Car Game
01:41:48 For Loops
01:47:46 Nested Loops
01:55:50 Lists
02:01:45 2D Lists
02:05:11 My Complete Python Course
02:06:00 List Methods
02:13:25 Tuples
02:15:34 Unpacking
02:18:21 Dictionaries
02:26:21 Emoji Converter
02:30:31 Functions
02:35:21 Parameters
02:39:24 Keyword Arguments
02:44:45 Return Statement
02:48:55 Creating a Reusable Function
02:53:42 Exceptions
02:59:14 Comments
03:01:46 Classes
03:07:46 Constructors
03:14:41 Inheritance
03:19:33 Modules
03:30:12 Packages
03:36:22 Generating Random Values
03:44:37 Working with Directories
03:50:47 Pypi and Pip
03:55:34 Project 1: Automation with Python
04:10:22 Project 2: Machine Learning with Python
04:58:37 Project 3: Building a Website with Django

#python #ai #machinelearning #webdevelopment

Learn Machine Learning in a way that is accessible to absolute beginners. You will learn the basics of Machine Learning and how to use TensorFlow to implement many different concepts.✏ Kylie Ying developed this course. Check out her channel: https://www.youtube.com/c/YCubed⭐ Code and Resources ⭐🔗 Supervised learning (classification/MAGIC): https://colab.research.google.....com/drive/16w3TDn_tA Supervised learning (regression/bikes): https://colab.research.google.....com/drive/1m3oQ9b0oY Unsupervised learning (seeds): https://colab.research.google.....com/drive/1zw_6ZnFPC Dataets (add a note that for the bikes dataset, they may have to open the downloaded csv file and remove special characters)🔗 MAGIC dataset: https://archive.ics.uci.edu/ml..../datasets/MAGIC+Gamm Bikes dataset: https://archive.ics.uci.edu/ml..../datasets/Seoul+Bike Seeds/wheat dataset: https://archive.ics.uci.edu/ml/datasets/seeds🏗 Google provided a grant to make this course possible. ⭐ Contents ⭐⌨ (0:00:00) Intro⌨ (0:00:58) Data/Colab Intro⌨ (0:08:45) Intro to Machine Learning⌨ (0:12:26) Features⌨ (0:17:23) Classification/Regression⌨ (0:19:57) Training Model⌨ (0:30:57) Preparing Data⌨ (0:44:43) K-Nearest Neighbors⌨ (0:52:42) KNN Implementation⌨ (1:08:43) Naive Bayes⌨ (1:17:30) Naive Bayes Implementation⌨ (1:19:22) Logistic Regression⌨ (1:27:56) Log Regression Implementation⌨ (1:29:13) Support Vector Machine⌨ (1:37:54) SVM Implementation⌨ (1:39:44) Neural Networks⌨ (1:47:57) Tensorflow⌨ (1:49:50) Classification NN using Tensorflow⌨ (2:10:12) Linear Regression⌨ (2:34:54) Lin Regression Implementation⌨ (2:57:44) Lin Regression using a Neuron⌨ (3:00:15) Regression NN using Tensorflow⌨ (3:13:13) K-Means Clustering⌨ (3:23:46) Principal Component Analysis⌨ (3:33:54) K-Means and PCA Implementations🎉 Thanks to our Champion and Sponsor supporters:👾 Raymond Odero👾 Agustín Kussrow👾 aldo ferretti👾 Otis Morgan👾 DeezMaster--Learn to code for free and get a developer job: https://www.freecodecamp.orgRead hundreds of articles on programming: https://freecodecamp.org/news