Python for Data Science: A Comprehensive Journey to Mastery

سرفصل های دوره

Mastering Python, Data Analysis, and Machine Learning

1 - Introduction

1 - Introduction

2 - Data types operators and data structures in Python

2 - Python Data Types

3 - Operators

4 - Arithmatic Operators

5 - Assignment Operators

6 - Comparison Operators

7 - More on Strings

8 - String Methods

9 - Lists

10 - Tuples

11 - Sets

12 - Dictionaries

13 - Identity Operators

14 - Compound Data Structures

3 - Python Loops and Comprehensions

15 - Python loops

16 - Range Understanding

17 - Creating and Modifying Lists

18 - Looping Through Dictionaries

19 - Enumerate Function

20 - List Comprehentions

21 - Adding Conditionals to List Comprehentions

4 - Comprehensive Guide to Python Functions

22 - Python Functions

23 - Functions Parameters

24 - Return values

25 - Default Parameters

26 - VariableLength Arguments

27 - Lambda Functions

28 - Higher Order Functions

29 - Recursive Functions

30 - Docstrings

31 - Functions Annotations

32 - Nested Functions

33 - Decorators

5 - NumPy for Efficient Numerical Computations

34 - Introduction to numpy

35 - Array Attributes

36 - Array Indexing and Slicing

37 - Array Operations

38 - Reshaping Arrays

39 - Stacking and Splitting Arrays

40 - Splitting Arrays

41 - Broadcasting

42 - Boolean Indexing and Filtering

43 - Advanced Array Manipulations

6 - Data Manipulation with Pandas A Comprehensive Guide

44 - Introduction to Pandas

45 - Pandas Series

46 - Pandas DataFames

47 - Loading Data Into a DataFrame

48 - Handling Missing Data NaN Values

49 - Basic DataFrame Operations

50 - Grouping Data in Pandas

51 - Merging and Joining DataFrames

52 - Data Cleaning

7 - HandsOn Machine Learning Exploring ScikitLearn

53 - Introduction to Machine Learning and Scikitlearn

54 - Data Preprocessing

55 - Handling Missing Values

56 - Features Scaling

57 - Encoding Categorical Variables

58 - Decision Trees

59 - Support Vector Machine

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تولید کننده: Udemy