Complete Python, ML & AI Bootcamp: Ultimate Beginner’s Guide

سرفصل های دوره

Master Python, Machine Learning & Artificial Intelligence: From Coding Basics to AI Applications

1 - Introduction to Python

1 - Mastering-Python-Fundamentals.pdf

1 - Python Overview and Applications

2 - Installing Python and Setting Up Your Environment

3 - Writing Your First Python Program and Understanding Syntax

4 - Adding Comments and Writing Clean Code

2 - Python Variables and Assignments

5 - Understanding Variables and Assignments

6 - Assigning Multiple Values to Variables

3 - Python Data Types

7 - Overview of Python Data Types

8 - Working with Numbers in Python

9 - Strings Slicing Modifying and Concatenating

10 - String Formatting Escape Characters and String Methods

11 - Python Booleans and Logical Values

12 - Python Operators

4 - Python Collections

13 - Introduction to Python Lists

14 - Working with Tuples in Python

15 - Sets Unique and Unordered Data Collections

16 - Managing Dictionaries in Python

5 - Control Flow in Python

17 - If Else and Elif Conditional Logic

18 - While Loops in Python

19 - For Loops for Iteration

6 - Python Functions

20 - Introduction to Python Functions

21 - Working with Lambda Functions

7 - Advanced Python Concepts

22 - Python Arrays Basics and Usage

23 - Classes and Objects in Python

24 - Understanding Inheritance in Python

25 - Iterators for Managing Data

26 - Python Polymorphism Reusing Code

27 - Python Scope Local and Global Variables

8 - Python Libraries and Utilities

28 - Working with Modules in Python

29 - Managing Dates and Times in Python

30 - Performing Mathematical Operations with Python Math

31 - Using JSON for Data Management

32 - Simplifying Tasks with Regular Expressions RegEx

33 - Managing Packages with PIP

9 - Handling Errors and User Interaction

34 - Handling Errors with Try and Except

35 - Taking User Input in Python

36 - String Formatting Techniques

37 - File Handling in Python Read Write and Delete

37 - python-fondation.zip

10 - Introduction to Machine Learning

38 - Overview

38 - Overview.pdf

39 - 1.pdf

39 - What is Machine Learning

40 - 2.pdf

40 - Types of Machine Learning

41 - 3.pdf

41 - Machine Learning Pipeline

42 - 4.pdf

42 - Key Concepts Features Labels Training and Testing

43 - 5.pdf

43 - Tools and Libraries for Machine Learning in Python

11 - Data Preprocessing

44 - Exploratory Data Analysis

45 - Data Cleaning Handling missing data

46 - Data Cleaning Removing duplicates and fixing inconsistencies

47 - Feature Scaling

48 - Data Transformation and Encoding

49 - Splitting Data TrainTest Split

50 - Practical Implementation

12 - Supervised Learning Regression

51 - Introduction to Linear Regression

52 - Implementing Linear Regression in Python

53 - Polynomial Regression

54 - Ridge Lasso and Elastic Net Regression

55 - Project Predicting Housing Prices

13 - Supervised Learning Classification

56 - Understanding Logistic Regression

57 - Implementing Logistic Regression in Python

58 - Decision Trees

59 - kNearest Neighbors kNN

60 - Support Vector Machines SVM

61 - Project Comparing Classification Models

14 - Ensemble Learning

62 - Introduction to Ensemble Learning

63 - Random Forest

64 - Gradient Boosting Algorithms

65 - Project Credit card fraud detection using ensemble methods

15 - Unsupervised Learning Clustering

66 - KMeans Clustering

67 - Hierarchical Clustering

68 - DensityBased Clustering

69 - Project Customer Segmentation Using Clustering Algorithms

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تولید کننده: Udemy