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

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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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    تاریخ انتشار: ۱۳ آذر ۱۴۰۲
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