Python With Machine Learning: 100 Days Of Coding Like A Pro

Published 10/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 19.06 GB | Duration: 59h 37m

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

What you'll learn
You'll achieve mastery in the Python programming language through the creation of 100 distinctive projects spanning 100 days
You will learn NumPy, Pandas, Matplotlib, Seaborn, Scikit, Plotly, SciPy, etc.
Develop a collection of 100 Python projects to build and enhance your portfolio for developer job applications
You will learn the practical ways of using Python for Data Science and Machine Learning
Build games, apps, websites, tools etc.

Requirements
Not a single line of coding experience required - I'll guide you through all the essentials you need to learn
PC or Mac with stable internet connection
No paid software required. I will guide you on how to download and install each software used in the course.

Description
Course Description:Are you eager to become a Python programming expert and delve deep into the realms of Machine Learning and Data Science? Do you aspire to build real-world projects that not only solidify your skills but also pave the way for a successful career in tech? Look no further! Our comprehensive course, "Master Python with Machine Learning and Data Science by Building 100 Projects," is designed to transform you into a Python powerhouse and equip you with the skills needed to excel in the world of technology.Course Highlights:Python Mastery: Begin your journey by mastering Python, one of the most versatile and in-demand programming languages in the industry. You'll start with the basics and gradually progress to advanced topics, ensuring a strong foundation.Hands-On Learning: This course is project-based, meaning you won't just learn theory; you'll apply your knowledge by building 100 diverse and practical projects. Each project is carefully designed to reinforce specific Python, Machine Learning, and Data Science concepts.Real-World Applications: Get ready to create websites, games, applications, and tools that mimic real-world scenarios. You'll work on projects that solve actual problems and showcase your skills to potential employers.Machine Learning & Data Science: Dive into the fascinating fields of Machine Learning and Data Science. You'll learn how to analyze data, create predictive models, and extract valuable insights from large datasets.Web Development: Explore the world of web development as you build dynamic websites using Python and popular frameworks like Django and Flask. Learn to create web applications with real-time functionality.Game Development: Develop interactive games with Python and popular libraries like Pygame. From 2D platformers to puzzle games, you'll gain a solid understanding of game design and coding.App Development: Create desktop and mobile applications using Python and tools like Tkinter and Kivy. Build applications that can run on various platforms, from Windows to Android.Tools and Utilities: Craft handy tools and utilities that can simplify everyday tasks. Whether it's automating data processing or building productivity apps, you'll have the skills to do it all.Project Portfolio: Throughout the course, you'll build a professional portfolio with 100 projects that showcase your versatility and proficiency as a Python developer, making you stand out to potential employers.Career Advancement: As you complete this course, you'll have a strong Python foundation, expertise in Machine Learning and Data Science, and a portfolio of impressive projects, making you a sought-after candidate in the job market.Lifetime Access: Gain lifetime access to course materials, updates, and a supportive community of learners and instructors. Continue learning and growing even after completing the course.Tools Included:PythonNumPyPandasMatplotlibSeabornPlotlyBig Data SciPyWho Should Enroll:Beginners who want to start their programming journey with Python.Intermediate Python developers looking to enhance their skills in Machine Learning and Data Science.Anyone aspiring to become a web developer, game developer, app developer, or data scientist.Tech enthusiasts seeking hands-on experience in building practical projects.By the end of this course, you'll have the knowledge, experience, and confidence to tackle real-world challenges in Python, Machine Learning, and Data Science. Enroll today and embark on a transformative learning journey that can open doors to a world of exciting opportunities in the tech industry!

Overview
Section 1: Learning Python Fundamentals

Lecture 1 History, Scope, Features and Applications of Python and Installing IDE

Section 2: Understanding Python Syntax

Lecture 2 Python Identifiers, Syntax, Indentation, variables and Comments

Section 3: Exploring Python Numeric Types

Lecture 3 Understand Python Numbers: Integer, Float, Complex Numbers, Booleans

Section 4: Grasping Python Variables

Lecture 4 Identifiers and Variables: Creation, Rules for Naming, Assignment and Output

Section 5: Python's Data Type Fundamentals

Lecture 5 Numeric Data Types,Booleans,Type Conversion: Converting One Data Type to Another

Section 6: Mastering Python Operators

Lecture 6 Arithmetic Operators, Assignment Operators, Comparison Operators

Lecture 7 Project: Simple Calculator

Section 7: Manipulating Strings

Lecture 8 Defining and String to Variable, Single line and Mutliline Strings

Lecture 9 Define String Indexing, String Slicing, String Concatenation, Checking String

Lecture 10 Project: Email Slicer

Section 8: Managing Lists

Lecture 11 Python List, List Length, List Indexing, List Slicing, List Methods, Check Lists

Section 9: Utilizing Tuples

Lecture 12 Python Tuples, Tuple Items, Tuple length, Tuple constructor, Tuple Indexing

Section 10: Harnessing Sets

Lecture 13 Python Set, Set Items, Access Items, Add Items, Remove Items, Join Two Sets

Section 11: Diving into Dictionaries

Lecture 14 Python Dictionary, Dictionary Items, Dictionary Length, Accessing items, Update

Lecture 15 Project: Currency Converter

Section 12: Input and Output Functions

Lecture 16 Input output Functions, Print(),input()

Lecture 17 Project: Quiz Game

Section 13: Conditional Logic

Lecture 18 Flow Control Statements, Conditional Statements, if statement, How if condition

Lecture 19 Project: Age Calculator

Section 14: Iterating with Loops

Lecture 20 Python Loops, for loops, How for loop works ?,while loop, How while loop works ?

Lecture 21 Project: Rock Paper Scissor

Section 15: Controlling Flow with Transfer Statements

Lecture 22 Break statement, How break works ? continue statement, How continue works ?

Section 16: Creating and Using Functions

Lecture 23 Functions passed as parameter, Nested Functions, Pass Sequence Types of Function

Lecture 24 Python Functions, Creating a Function, Calling a Function, Function Arguments

Lecture 25 Project: Contact Book App

Section 17: Exploring Modules and Packages

Lecture 26 Python Modules, Create a Module, Naming and Renaming a Module, Built-in Modules

Lecture 27 Project: Dice Rolling

Section 18: Library Management System

Lecture 28 Project: BMI Calculator

Section 19: List Comprehensions

Lecture 29 Comprehensions in python, List Comprehensions, Dictionary Comprehensions

Lecture 30 Project: Number Guessing Game

Section 20: OOPs Fundamentals

Lecture 31 Introduction to Object Oriented Programming, Classes and Objects, Create Class

Section 21: OOPs Principle

Lecture 32 OOPs Principles, Encapsulation, Inheritance, Method Overriding, Types of Inherit

Lecture 33 Project: ATM

Section 22: Working with File Systems

Lecture 34 What is a File ?,File Modes, Open a file on server, Read only parts of file

Section 23: Handling Exceptions

Lecture 35 Exceptions, Exceptions Handling, try block, Many Exceptions, else block

Lecture 36 Project: To-do List App

Section 24: Regular Expressions Mastery

Lecture 37 Regular Expressions, RegEx Module, Sequence Characters, Reg Ex Functions

Lecture 38 project: Password Generator

Section 25: Tic Tac Toe Project

Lecture 39 Project - Tic Tac Toe Project

Section 26: Understanding the Date Time Module

Lecture 40 Python Datetime Module, Datetime Module Class, Python Date Class, Python Date

Lecture 41 Project: Birthday Finder

Section 27: Exploring Databases

Lecture 42 Test MySQL Connector, Create Connection, Create Database, Create if Database Ex

Section 28: Networking in Python

Lecture 43 Python Urllib Module, Python Networking, What is a Socket ? , Socket Terminology

Section 29: Applying Decorators and Generators

Lecture 44 Python Decorators, Chaining Decorators, Decorators with Parameters, Generators

Section 30: Working with Arrays

Lecture 45 Python Arrays, Create an Array, Adding Elements to an Array, Accessing Element

Section 31: Harnessing the Range

Lecture 46 Range Function, Parameter Values.

Section 32: Managing Python Packages with PIP

Lecture 47 What is a PIP ? What is a Package ? Check if PIP is installed ? Install PIP

Section 33: Understanding Closures

Lecture 48 Python Closures, Closure Function, when to use Closures ?

Section 34: Handling JSON Data

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

Section 35: Intro of NumPy

Lecture 50 What is NumPy ?,Why use NumPy, Why NumPy is Faster than Lists ?

Section 36: Creating NumPy Arrays

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

Section 37: Indexing and Slicing in NumPy

Lecture 52 Access Array Elements, Access 2D-Arrays, Access 3D-Arrays, Negative Indexing

Section 38: Exploring NumPy Data Types

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

Section 39: Copying vs. Viewing NumPy Arrays

Lecture 54 Difference between copy and view, copy, view, making changes in the view

Section 40: Manipulating Array Shapes in NumPy

Lecture 55 Shape of an Array, Get the shape of an Array

Section 41: Reshaping NumPy Arrays

Lecture 56 Reshaping Arrays, Reshape from 1-D to 2-D,Reshape from 1-D to 3-D,Can we reshape

Section 42: Iterating over NumPy Arrays

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

Section 43: Joining NumPy Arrays

Lecture 58 Joining NumPy Arrays, Joining using Stack Functions, Stacking along rows

Section 44: Splitting NumPy Arrays

Lecture 59 Splitting NumPy Arrays, Splitting into Arrays, Splitting 2-D Arrays, Split()

Section 45: Searching NumPy Arrays

Lecture 60 Joining NumPy Arrays, Joining using Stack Functions, Stacking along Rows

Section 46: Sorting NumPy Arrays

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

Section 47: Filtering NumPy Arrays

Lecture 62 Filtering Arrays, Creating filtering Arrays, Creating Filter directly from Array

Section 48: Randomness with NumPy

Lecture 63 What is Random Number, Pseudo Random and True Random, Generate Random Number

Section 49: Generating Data Distributions in NumPy

Lecture 64 What is Data Distribution ?,Random Distribution, Random Permutation of elements

Section 50: Combining NumPy and Seaborn

Lecture 65 Visualize Distributions with Seaborn, Install Seaborn, Distplots

Section 51: Understanding Normal and Binomial Distributions in NumPy

Lecture 66 Normal Distribution, Visualization of Normal Distribution, Binomial Distribution

Section 52: Leveraging NumPy Universal Functions

Lecture 67 What are ufuncs ?,Why use ufuncs ?,What is Vectorization ?

Section 53: Rounding and Logging with NumPy Ufuncs

Lecture 68 Rounding Decimals, Truncation, Rounding, Floor, Ceil, Logs, Log at Base 2

Section 54: NumPy Ufuncs for Summations, Products, and Differences

Lecture 69 Summations, Summation over an axis, Cumulative Sum, Products

Section 55: NumPy Ufuncs for LCM, GCD, Trigonometric, and Hyperbolic Functions

Lecture 70 Finding LCM, Finding LCM in Arrays, Finding GCD, Finding GCD in Arrays

Section 56: Set Operations with NumPy Ufuncs

Lecture 71 what is a Set ?,Create Sets in NumPy, Finding Union, Finding Intersection

Section 57: Python Data Analysis with Pandas

Lecture 72 Pandas text Data, Operations, Create a Text DataFrame with Pandas

Section 58: Working with Pandas DataFrames

Lecture 73 What is a DataFrame ? Structure of DataFrame, pandas. DataFrame, Create DataFram

Section 59: Reading Files with Pandas

Lecture 74 Read CSV Files, Max_Rows, Read JSON, Dictionary as JSON, Analyzing DataFrames

Section 60: Data Cleaning in Pandas

Lecture 75 Data Cleaning, Cleaning Empty Cells, Remove Rows ,Replace Empty Values, Replace

Section 61: Handling Missing Values with Pandas

Lecture 76 Pandas Missing Values, Handling Missing Data, Calculation with Missing Data

Section 62: Merging, Joining, and Concatenating DataFrames in Pandas

Lecture 77 Combining DataFrames, Merging DataFrames, Parameters, Concat DataFrames

Section 63: Grouping Data with Pandas DataFrame GroupBy

Lecture 78 Pandas groupby() Operation ,group() method ,Parameters, Return Value

Lecture 79 What are Ufuncs ?,Why use Ufuncs ? , What is Vectorization ?

Section 64: Sorting DataFrames in Pandas

Lecture 80 Pandas sort_values(), Sort By Labels, Order of Sorting, Sort the Columns

Section 65: Text Data Operations with Pandas

Lecture 81 Pandas text Data,Operations,Create a Text DataFrame with Pandas,Change the Case

Section 66: Statistical Analysis with Pandas

Lecture 82 Pandas Statistics, Percent change, Covariance, Correlation, Data Ranking, Rank

Section 67: Indexing and Selecting Data with Pandas

Lecture 83 Pandas Indexing, .loc() ,iloc(), Use of Notations, Using the index operator

Section 68: Reindexing and Iterating in Pandas

Lecture 84 Regular Expressions, RegEx Module, Sequence Characters, RegEx Functions

Section 69: Leveraging DateTime Functionality in Pandas

Lecture 85 Pandas Dates, Create a Range of Dates, Convert string to DataTime

Section 70: Managing TimeDeltas in Pandas

Lecture 86 Pandas TimeDeltas, Passing Strings, Passing Integers, Data Offsets, To_timedelta

Section 71: Handling Categorical Data in Pandas

Lecture 87 Categorical Data in Pandas, Uses of Categorical Data, Object Creation, Category

Section 72: Generating Summary Statistics with Pandas

Lecture 88 Pandas Summary Statistics, Pandas Sum(), Pandas Count(), Pandas Max()

Section 73: Visualizing Data with Pandas

Lecture 89 Basic Plotting using plot, Plotting methods, Bar plot

Section 74: Intro to Matplotlib

Lecture 90 What is Matplotlib ? Installation of Matplotlib, Import Matplotlib

Section 75: Customizing Markers and Lines in Matplotlib

Lecture 91 Matplotlib Markers, Format Strings using fmt, Line Reference, Color Reference

Section 76: Adding Labels, Titles, and Grids in Matplotlib

Lecture 92 Create Lables for a Plots, Create Title For a Plots

Section 77: Creating Subplots with Matplotlib

Lecture 93 Display Multiple Plots, The subplot() Function, Subplot Title, Super Title

Section 78: Crafting Scatter Plots and Bar Plots in Matplotlib

Lecture 94 Creating Scatter Plots, Compare Plots, Colors, Color Each Dot,ColorMap and Color

Section 79: Visualizing Data with Histograms and Pie Charts in Matplotlib

Lecture 95 Histogram, hist() function, Create a Histogram, Pie chart Lable, Pie Chart Start

Section 80: Getting Started with Seaborn and Exploring Color Palettes

Lecture 96 Seaborn VS Matplotlib, Seaborn, Importing Libraries, Importing DataSet

Section 81: Visualizing Data Distributions with Seaborn

Lecture 97 Plotting Univariate Distribution, Parameters, Displots, Jointplot, Pairplot, Rug

Section 82: Utilizing Seaborn for Categorical Data

Lecture 98 Categorical data Plots, Barplot, Countplot, Boxplot, Violinplot, Stripplot

Section 83: Building Matrix Plots, Grids and Regression Plots

Lecture 99 Matrix Plots, Heatmap, Cluster Plots, Griss, Facet Grid, Joint Grid, Regression

Section 84: Creating Histograms and KDE Plots with Seaborn

Lecture 100 Histogram, What id KDE?, Fitting Parametric, Distrbution, Kernel Density Estimat

Section 85: Introduction to SciPy

Lecture 101 What is SciPy?, Installation of SciPy, Import SciPy, Checking SciPy Version

Section 86: Understanding SciPy Optimizers, Sparse Data and Graphs

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

Lecture 103 Sparse Matrix Methods, Working with Graphs, Adjancency Matrix

Section 87: Working with SciPy Spatial Data

Lecture 104 Working with Spatial Data, Triangulation, Convex Hull, KDTrees ,Distance Matrix

Section 88: Exploring Matlab Array and Interpolation

Lecture 105 Working with matlab Array, EXporting Data in Matlab Format

Section 89: Introduction to Plotly and Cufflinks

Lecture 106 What is Plotly and Cuffinks?, Features of Plotly, Install Plotly

Section 90: Creating Plots using Geographical Plotting and Choropleth Maps

Lecture 107 What is Geographical Plotting ?, How to import packeges ?, Choropleth maps

Section 91: Introduction to Machine Learning

Lecture 108 What is Machine Learning?, How does Machine Learning Work?

Section 92: Exploring Machine Learning Life Cycle

Lecture 109 Machine Learning Life Cycle, Gathering Data, Data Preparation, Data Analysis

Section 93: Exploring Regression Techniques

Lecture 110 Regression, Linear Regression, Terminologies Related to Liner Regression

Section 94: Understanding Classification

Lecture 111 Classificatio Algorithm, Types of classification, Learners in classification Pro

Section 95: Working with Support Vector Machine Algorithm

Lecture 112 What is Support Vector Machine?, Types of SVM, Hyperplane and Support Vector

Section 96: Working with Naive Bayes Algorithm

Lecture 113 Naive Bayes Alorithm, Why it is called Naive Bayes?, Bayes' Theorem

Section 97: Understanding Decision Tree Classifier

Lecture 114 What is Decision Tree?, Decisions Tree Classification Algorithm

Want to learn coding from scratch? Join this course and make cool projects!,Make your own websites and apps for your startup with this course.,New to coding? This course teaches you everything for pro Python skills,If you know programming but not Python, learn fast with coding projects.,If you're okay with Python, 100 days of code challenges will boost you up.

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