Python with Machine Learning: 100 Days of Coding like a Pro

سرفصل های دوره

Master Python with Machine Learning and Data Science by building 100 projects. Build websites, games, apps and tools!

1 - Learning Python Fundamentals

1 - History Scope Features and Applications of Python and Installing IDE

2 - Understanding Python Syntax

2 - Python Identifiers Syntax Indentation variables and Comments

3 - Exploring Python Numeric Types

3 - Understand Python Numbers Integer Float Complex Numbers Booleans

4 - Grasping Python Variables

4 - Identifiers and Variables Creation Rules for Naming Assignment and Output

5 - Pythons Data Type Fundamentals

5 - Numeric Data TypesBooleansType Conversion Converting One Data Type to Another

6 - Mastering Python Operators

6 - Arithmetic Operators Assignment Operators Comparison Operators

7 - Project Simple Calculator

7 - Manipulating Strings

8 - Defining and String to Variable Single line and Mutliline Strings

9 - Define String Indexing String Slicing String Concatenation Checking String

10 - Project Email Slicer

8 - Managing Lists

11 - Python List List Length List Indexing List Slicing List Methods Check Lists

9 - Utilizing Tuples

12 - Python Tuples Tuple Items Tuple length Tuple constructor Tuple Indexing

10 - Harnessing Sets

13 - Python Set Set Items Access Items Add Items Remove Items Join Two Sets

11 - Diving into Dictionaries

14 - Python Dictionary Dictionary Items Dictionary Length Accessing items Update

15 - Project Currency Converter

12 - Input and Output Functions

16 - Input output Functions Printinput

17 - Project Quiz Game

13 - Conditional Logic

18 - Flow Control Statements Conditional Statements if statement How if condition

19 - Project Age Calculator

14 - Iterating with Loops

20 - Python Loops for loops How for loop works while loop How while loop works

21 - Project Rock Paper Scissor

15 - Controlling Flow with Transfer Statements

22 - Break statement How break works continue statement How continue works

16 - Creating and Using Functions

23 - Functions passed as parameter Nested Functions Pass Sequence Types of Function

24 - Python Functions Creating a Function Calling a Function Function Arguments

25 - Project Contact Book App

17 - Exploring Modules and Packages

26 - Python Modules Create a Module Naming and Renaming a Module Builtin Modules

27 - Project Dice Rolling

18 - Library Management System

28 - Project BMI Calculator

19 - List Comprehensions

29 - Comprehensions in python List Comprehensions Dictionary Comprehensions

30 - Project Number Guessing Game

20 - OOPs Fundamentals

31 - Introduction to Object Oriented Programming Classes and Objects Create Class

21 - OOPs Principle

32 - OOPs Principles Encapsulation Inheritance Method Overriding Types of Inherit

33 - Project ATM

22 - Working with File Systems

34 - What is a File File Modes Open a file on server Read only parts of file

23 - Handling Exceptions

35 - Exceptions Exceptions Handling try block Many Exceptions else block

36 - Project Todo List App

24 - Regular Expressions Mastery

37 - Regular Expressions RegEx Module Sequence Characters Reg Ex Functions

38 - project Password Generator

25 - Tic Tac Toe Project

39 - Project Tic Tac Toe Project

26 - Understanding the Date Time Module

40 - Python Datetime Module Datetime Module Class Python Date Class Python Date

41 - Project Birthday Finder

27 - Exploring Databases

42 - Test MySQL Connector Create Connection Create Database Create if Database Ex

28 - Networking in Python

43 - Python Urllib Module Python Networking What is a Socket Socket Terminology

29 - Applying Decorators and Generators

44 - Python Decorators Chaining Decorators Decorators with Parameters Generators

30 - Working with Arrays

45 - Python Arrays Create an Array Adding Elements to an Array Accessing Element

31 - Harnessing the Range

46 - Range Function Parameter Values

32 - Managing Python Packages with PIP

47 - What is a PIP What is a Package Check if PIP is installed Install PIP

33 - Understanding Closures

48 - Python Closures Closure Function when to use Closures

34 - Handling JSON Data

49 - JSON in Python Parse JSON Convert from JSON to Python

35 - Intro of NumPy

50 - What is NumPy Why use NumPy Why NumPy is Faster than Lists

36 - Creating NumPy Arrays

51 - Create a NumPy ND array Object Dimension in Arrays 0D Arrays 1D Arrays

37 - Indexing and Slicing in NumPy

52 - Access Array Elements Access 2DArrays Access 3DArrays Negative Indexing

38 - Exploring NumPy Data Types

53 - Data Types in NumPy checking the data type of an array

39 - Copying vs Viewing NumPy Arrays

54 - Difference between copy and view copy view making changes in the view

40 - Manipulating Array Shapes in NumPy

55 - Shape of an Array Get the shape of an Array

41 - Reshaping NumPy Arrays

56 - Reshaping Arrays Reshape from 1D to 2DReshape from 1D to 3DCan we reshape

42 - Iterating over NumPy Arrays

57 - Iterating Arrays Iterating 2D arrays iterating 3D arrays Iterating Arrays

43 - Joining NumPy Arrays

58 - Joining NumPy Arrays Joining using Stack Functions Stacking along rows

44 - Splitting NumPy Arrays

59 - Splitting NumPy Arrays Splitting into Arrays Splitting 2D Arrays Split

45 - Searching NumPy Arrays

60 - Joining NumPy Arrays Joining using Stack Functions Stacking along Rows

46 - Sorting NumPy Arrays

61 - Sorting arrays Sorting arrays of strings Boolean array Sorting a 2D array

47 - Filtering NumPy Arrays

62 - Filtering Arrays Creating filtering Arrays Creating Filter directly from Array

48 - Randomness with NumPy

63 - What is Random Number Pseudo Random and True Random Generate Random Number

49 - Generating Data Distributions in NumPy

64 - What is Data Distribution Random Distribution Random Permutation of elements

50 - Combining NumPy and Seaborn

65 - Visualize Distributions with Seaborn Install Seaborn Distplots

51 - Understanding Normal and Binomial Distributions in NumPy

66 - Normal Distribution Visualization of Normal Distribution Binomial Distribution

52 - Leveraging NumPy Universal Functions

67 - What are ufuncs Why use ufuncs What is Vectorization

53 - Rounding and Logging with NumPy Ufuncs

68 - Rounding Decimals Truncation Rounding Floor Ceil Logs Log at Base 2

54 - NumPy Ufuncs for Summations Products and Differences

69 - Summations Summation over an axis Cumulative Sum Products

55 - NumPy Ufuncs for LCM GCD Trigonometric and Hyperbolic Functions

70 - Finding LCM Finding LCM in Arrays Finding GCD Finding GCD in Arrays

56 - Set Operations with NumPy Ufuncs

71 - what is a Set Create Sets in NumPy Finding Union Finding Intersection

57 - Python Data Analysis with Pandas

72 - 37.1-Python-Pandas

72 - Pandas text Data Operations Create a Text DataFrame with Pandas

58 - Working with Pandas DataFrames

73 - What is a DataFrame Structure of DataFrame pandas DataFrame Create DataFram

59 - Reading Files with Pandas

74 - Read CSV Files MaxRows Read JSON Dictionary as JSON Analyzing DataFrames

60 - Data Cleaning in Pandas

75 - Data Cleaning Cleaning Empty Cells Remove Rows Replace Empty Values Replace

61 - Handling Missing Values with Pandas

76 - Pandas Missing Values Handling Missing Data Calculation with Missing Data

62 - Merging Joining and Concatenating DataFrames in Pandas

77 - Combining DataFrames Merging DataFrames Parameters Concat DataFrames

63 - Grouping Data with Pandas DataFrame GroupBy

78 - Pandas groupby Operation group method Parameters Return Value

79 - What are Ufuncs Why use Ufuncs What is Vectorization

64 - Sorting DataFrames in Pandas

80 - Pandas sortvalues Sort By Labels Order of Sorting Sort the Columns

65 - Text Data Operations with Pandas

81 - Pandas text DataOperationsCreate a Text DataFrame with PandasChange the Case

66 - Statistical Analysis with Pandas

82 - Pandas Statistics Percent change Covariance Correlation Data Ranking Rank

67 - Indexing and Selecting Data with Pandas

83 - Pandas Indexing loc iloc Use of Notations Using the index operator

68 - Reindexing and Iterating in Pandas

84 - Regular Expressions RegEx Module Sequence Characters RegEx Functions

69 - Leveraging DateTime Functionality in Pandas

85 - Pandas Dates Create a Range of Dates Convert string to DataTime

70 - Managing TimeDeltas in Pandas

86 - Pandas TimeDeltas Passing Strings Passing Integers Data Offsets Totimedelta

71 - Handling Categorical Data in Pandas

87 - Categorical Data in Pandas Uses of Categorical Data Object Creation Category

72 - Generating Summary Statistics with Pandas

88 - Pandas Summary Statistics Pandas Sum Pandas Count Pandas Max

73 - Visualizing Data with Pandas

89 - Basic Plotting using plot Plotting methods Bar plot

74 - Intro to Matplotlib

90 - What is Matplotlib Installation of Matplotlib Import Matplotlib

75 - Customizing Markers and Lines in Matplotlib

91 - Matplotlib Markers Format Strings using fmt Line Reference Color Reference

76 - Adding Labels Titles and Grids in Matplotlib

92 - Create Lables for a Plots Create Title For a Plots

77 - Creating Subplots with Matplotlib

93 - Display Multiple Plots The subplot Function Subplot Title Super Title

78 - Crafting Scatter Plots and Bar Plots in Matplotlib

94 - Creating Scatter Plots Compare Plots Colors Color Each DotColorMap and Color

79 - Visualizing Data with Histograms and Pie Charts in Matplotlib

95 - Histogram hist function Create a Histogram Pie chart Lable Pie Chart Start

80 - Getting Started with Seaborn and Exploring Color Palettes

96 - Seaborn VS Matplotlib Seaborn Importing Libraries Importing DataSet

81 - Visualizing Data Distributions with Seaborn

97 - Plotting Univariate Distribution Parameters Displots Jointplot Pairplot Rug

82 - Utilizing Seaborn for Categorical Data

98 - Categorical data Plots Barplot Countplot Boxplot Violinplot Stripplot

83 - Building Matrix Plots Grids and Regression Plots

99 - Matrix Plots Heatmap Cluster Plots Griss Facet Grid Joint Grid Regression

84 - Creating Histograms and KDE Plots with Seaborn

100 - Histogram What id KDE Fitting Parametric Distrbution Kernel Density Estimat

85 - Introduction to SciPy

101 - What is SciPy Installation of SciPy Import SciPy Checking SciPy Version

86 - Understanding SciPy Optimizers Sparse Data and Graphs

102 - SciPy Optimizers Roots of an Equation Minimizing Function What is a Sparse

103 - Sparse Matrix Methods Working with Graphs Adjancency Matrix

87 - Working with SciPy Spatial Data

104 - Working with Spatial Data Triangulation Convex Hull KDTrees Distance Matrix

88 - Exploring Matlab Array and Interpolation

105 - Working with matlab Array EXporting Data in Matlab Format

89 - Introduction to Plotly and Cufflinks

106 - What is Plotly and Cuffinks Features of Plotly Install Plotly

90 - Creating Plots using Geographical Plotting and Choropleth Maps

107 - What is Geographical Plotting How to import packeges Choropleth maps

91 - Introduction to Machine Learning

108 - What is Machine Learning How does Machine Learning Work

92 - Exploring Machine Learning Life Cycle

109 - Machine Learning Life Cycle Gathering Data Data Preparation Data Analysis

93 - Exploring Regression Techniques

110 - Regression Linear Regression Terminologies Related to Liner Regression

94 - Understanding Classification

111 - Classificatio Algorithm Types of classification Learners in classification Pro

95 - Working with Support Vector Machine Algorithm

112 - What is Support Vector Machine Types of SVM Hyperplane and Support Vector

96 - Working with Naive Bayes Algorithm

113 - Naive Bayes Alorithm Why it is called Naive Bayes Bayes Theorem

97 - Understanding Decision Tree Classifier

114 - What is Decision Tree Decisions Tree Classification Algorithm

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تولید کننده: Udemy