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

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Augmintech Education Pvt Ltd

60:05:46

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1 - Learning Python Fundamentals

1 - History Scope Features and Applications of Python and Installing IDE.mp4

33:34

2 - Understanding Python Syntax

2 - Python Identifiers Syntax Indentation variables and Comments.mp4

31:41

3 - Exploring Python Numeric Types

3 - Understand Python Numbers Integer Float Complex Numbers Booleans.mp4

11:01

4 - Grasping Python Variables

4 - Identifiers and Variables Creation Rules for Naming Assignment and Output.mp4

37:24

5 - Pythons Data Type Fundamentals

5 - Numeric Data TypesBooleansType Conversion Converting One Data Type to Another.mp4

31:34

6 - Mastering Python Operators

6 - Arithmetic Operators Assignment Operators Comparison Operators.mp4

50:04

7 - Project Simple Calculator.mp4

12:32

7 - Manipulating Strings

8 - Defining and String to Variable Single line and Mutliline Strings.mp4

38:20

9 - Define String Indexing String Slicing String Concatenation Checking String.mp4

36:14

10 - Project Email Slicer.mp4

10:17

8 - Managing Lists

11 - Python List List Length List Indexing List Slicing List Methods Check Lists.mp4

01:02:45

9 - Utilizing Tuples

12 - Python Tuples Tuple Items Tuple length Tuple constructor Tuple Indexing.mp4

43:14

10 - Harnessing Sets

13 - Python Set Set Items Access Items Add Items Remove Items Join Two Sets.mp4

51:00

11 - Diving into Dictionaries

14 - Python Dictionary Dictionary Items Dictionary Length Accessing items Update.mp4

36:51

15 - Project Currency Converter.mp4

20:16

12 - Input and Output Functions

16 - Input output Functions Printinput.mp4

51:07

17 - Project Quiz Game.mp4

11:39

13 - Conditional Logic

18 - Flow Control Statements Conditional Statements if statement How if condition.mp4

33:12

19 - Project Age Calculator.mp4

15:16

14 - Iterating with Loops

20 - Python Loops for loops How for loop works while loop How while loop works.mp4

52:34

21 - Project Rock Paper Scissor.mp4

13:40

15 - Controlling Flow with Transfer Statements

22 - Break statement How break works continue statement How continue works.mp4

28:22

16 - Creating and Using Functions

23 - Functions passed as parameter Nested Functions Pass Sequence Types of Function.mp4

01:00:15

24 - Python Functions Creating a Function Calling a Function Function Arguments.mp4

32:51

25 - Project Contact Book App.mp4

26:34

17 - Exploring Modules and Packages

26 - Python Modules Create a Module Naming and Renaming a Module Builtin Modules.mp4

37:41

27 - Project Dice Rolling.mp4

16:29

18 - Library Management System

28 - Project BMI Calculator.mp4

11:00

19 - List Comprehensions

29 - Comprehensions in python List Comprehensions Dictionary Comprehensions.mp4

24:35

30 - Project Number Guessing Game.mp4

15:30

20 - OOPs Fundamentals

31 - Introduction to Object Oriented Programming Classes and Objects Create Class.mp4

01:09:16

21 - OOPs Principle

32 - OOPs Principles Encapsulation Inheritance Method Overriding Types of Inherit.mp4

01:50:58

33 - Project ATM.mp4

16:07

22 - Working with File Systems

34 - What is a File File Modes Open a file on server Read only parts of file.mp4

01:02:36

23 - Handling Exceptions

35 - Exceptions Exceptions Handling try block Many Exceptions else block.mp4

57:40

36 - Project Todo List App.mp4

23:30

24 - Regular Expressions Mastery

37 - Regular Expressions RegEx Module Sequence Characters Reg Ex Functions.mp4

51:21

38 - project Password Generator.mp4

12:45

25 - Tic Tac Toe Project

39 - Project Tic Tac Toe Project.mp4

30:09

26 - Understanding the Date Time Module

40 - Python Datetime Module Datetime Module Class Python Date Class Python Date.mp4

01:02:00

41 - Project Birthday Finder.mp4

09:08

27 - Exploring Databases

42 - Test MySQL Connector Create Connection Create Database Create if Database Ex.mp4

01:06:53

28 - Networking in Python

43 - Python Urllib Module Python Networking What is a Socket Socket Terminology.mp4

46:04

29 - Applying Decorators and Generators

44 - Python Decorators Chaining Decorators Decorators with Parameters Generators.mp4

01:05:04

30 - Working with Arrays

45 - Python Arrays Create an Array Adding Elements to an Array Accessing Element.mp4

40:36

31 - Harnessing the Range

46 - Range Function Parameter Values.mp4

08:39

32 - Managing Python Packages with PIP

47 - What is a PIP What is a Package Check if PIP is installed Install PIP.mp4

14:00

33 - Understanding Closures

48 - Python Closures Closure Function when to use Closures.mp4

22:00

34 - Handling JSON Data

49 - JSON in Python Parse JSON Convert from JSON to Python.mp4

35:31

35 - Intro of NumPy

50 - What is NumPy Why use NumPy Why NumPy is Faster than Lists.mp4

00:54

36 - Creating NumPy Arrays

51 - Create a NumPy ND array Object Dimension in Arrays 0D Arrays 1D Arrays.mp4

32:15

37 - Indexing and Slicing in NumPy

52 - Access Array Elements Access 2DArrays Access 3DArrays Negative Indexing.mp4

31:01

38 - Exploring NumPy Data Types

53 - Data Types in NumPy checking the data type of an array.mp4

21:32

39 - Copying vs Viewing NumPy Arrays

54 - Difference between copy and view copy view making changes in the view.mp4

13:11

40 - Manipulating Array Shapes in NumPy

55 - Shape of an Array Get the shape of an Array.mp4

07:00

41 - Reshaping NumPy Arrays

56 - Reshaping Arrays Reshape from 1D to 2DReshape from 1D to 3DCan we reshape.mp4

16:14

42 - Iterating over NumPy Arrays

57 - Iterating Arrays Iterating 2D arrays iterating 3D arrays Iterating Arrays.mp4

21:32

43 - Joining NumPy Arrays

58 - Joining NumPy Arrays Joining using Stack Functions Stacking along rows.mp4

19:31

44 - Splitting NumPy Arrays

59 - Splitting NumPy Arrays Splitting into Arrays Splitting 2D Arrays Split.mp4

20:51

45 - Searching NumPy Arrays

60 - Joining NumPy Arrays Joining using Stack Functions Stacking along Rows.mp4

24:18

46 - Sorting NumPy Arrays

61 - Sorting arrays Sorting arrays of strings Boolean array Sorting a 2D array.mp4

15:30

47 - Filtering NumPy Arrays

62 - Filtering Arrays Creating filtering Arrays Creating Filter directly from Array.mp4

20:21

48 - Randomness with NumPy

63 - What is Random Number Pseudo Random and True Random Generate Random Number.mp4

16:04

49 - Generating Data Distributions in NumPy

64 - What is Data Distribution Random Distribution Random Permutation of elements.mp4

19:41

50 - Combining NumPy and Seaborn

65 - Visualize Distributions with Seaborn Install Seaborn Distplots.mp4

13:32

51 - Understanding Normal and Binomial Distributions in NumPy

66 - Normal Distribution Visualization of Normal Distribution Binomial Distribution.mp4

32:03

52 - Leveraging NumPy Universal Functions

67 - What are ufuncs Why use ufuncs What is Vectorization.mp4

40:05

53 - Rounding and Logging with NumPy Ufuncs

68 - Rounding Decimals Truncation Rounding Floor Ceil Logs Log at Base 2.mp4

21:00

54 - NumPy Ufuncs for Summations Products and Differences

69 - Summations Summation over an axis Cumulative Sum Products.mp4

22:00

55 - NumPy Ufuncs for LCM GCD Trigonometric and Hyperbolic Functions

70 - Finding LCM Finding LCM in Arrays Finding GCD Finding GCD in Arrays.mp4

39:18

56 - Set Operations with NumPy Ufuncs

71 - what is a Set Create Sets in NumPy Finding Union Finding Intersection.mp4

18:00

57 - Python Data Analysis with Pandas

72 - 37.1-Python-Pandas.mp4

27:48

72 - Pandas text Data Operations Create a Text DataFrame with Pandas.mp4

27:48

58 - Working with Pandas DataFrames

73 - What is a DataFrame Structure of DataFrame pandas DataFrame Create DataFram.mp4

55:07

59 - Reading Files with Pandas

74 - Read CSV Files MaxRows Read JSON Dictionary as JSON Analyzing DataFrames.mp4

18:00

60 - Data Cleaning in Pandas

75 - Data Cleaning Cleaning Empty Cells Remove Rows Replace Empty Values Replace.mp4

22:42

61 - Handling Missing Values with Pandas

76 - Pandas Missing Values Handling Missing Data Calculation with Missing Data.mp4

35:05

62 - Merging Joining and Concatenating DataFrames in Pandas

77 - Combining DataFrames Merging DataFrames Parameters Concat DataFrames.mp4

38:40

63 - Grouping Data with Pandas DataFrame GroupBy

78 - Pandas groupby Operation group method Parameters Return Value.mp4

33:00

79 - What are Ufuncs Why use Ufuncs What is Vectorization.mp4

40:05

64 - Sorting DataFrames in Pandas

80 - Pandas sortvalues Sort By Labels Order of Sorting Sort the Columns.mp4

35:07

65 - Text Data Operations with Pandas

81 - Pandas text DataOperationsCreate a Text DataFrame with PandasChange the Case.mp4

27:48

66 - Statistical Analysis with Pandas

82 - Pandas Statistics Percent change Covariance Correlation Data Ranking Rank.mp4

17:09

67 - Indexing and Selecting Data with Pandas

83 - Pandas Indexing loc iloc Use of Notations Using the index operator.mp4

28:05

68 - Reindexing and Iterating in Pandas

84 - Regular Expressions RegEx Module Sequence Characters RegEx Functions.mp4

45:42

69 - Leveraging DateTime Functionality in Pandas

85 - Pandas Dates Create a Range of Dates Convert string to DataTime.mp4

35:19

70 - Managing TimeDeltas in Pandas

86 - Pandas TimeDeltas Passing Strings Passing Integers Data Offsets Totimedelta.mp4

17:08

71 - Handling Categorical Data in Pandas

87 - Categorical Data in Pandas Uses of Categorical Data Object Creation Category.mp4

38:12

72 - Generating Summary Statistics with Pandas

88 - Pandas Summary Statistics Pandas Sum Pandas Count Pandas Max.mp4

32:23

73 - Visualizing Data with Pandas

89 - Basic Plotting using plot Plotting methods Bar plot.mp4

35:40

74 - Intro to Matplotlib

90 - What is Matplotlib Installation of Matplotlib Import Matplotlib.mp4

24:43

75 - Customizing Markers and Lines in Matplotlib

91 - Matplotlib Markers Format Strings using fmt Line Reference Color Reference.mp4

44:14

76 - Adding Labels Titles and Grids in Matplotlib

92 - Create Lables for a Plots Create Title For a Plots.mp4

26:01

77 - Creating Subplots with Matplotlib

93 - Display Multiple Plots The subplot Function Subplot Title Super Title.mp4

26:16

78 - Crafting Scatter Plots and Bar Plots in Matplotlib

94 - Creating Scatter Plots Compare Plots Colors Color Each DotColorMap and Color.mp4

53:27

79 - Visualizing Data with Histograms and Pie Charts in Matplotlib

95 - Histogram hist function Create a Histogram Pie chart Lable Pie Chart Start.mp4

32:15

80 - Getting Started with Seaborn and Exploring Color Palettes

96 - Seaborn VS Matplotlib Seaborn Importing Libraries Importing DataSet.mp4

25:26

81 - Visualizing Data Distributions with Seaborn

97 - Plotting Univariate Distribution Parameters Displots Jointplot Pairplot Rug.mp4

36:00

82 - Utilizing Seaborn for Categorical Data

98 - Categorical data Plots Barplot Countplot Boxplot Violinplot Stripplot.mp4

39:45

83 - Building Matrix Plots Grids and Regression Plots

99 - Matrix Plots Heatmap Cluster Plots Griss Facet Grid Joint Grid Regression.mp4

01:13:32

84 - Creating Histograms and KDE Plots with Seaborn

100 - Histogram What id KDE Fitting Parametric Distrbution Kernel Density Estimat.mp4

20:24

85 - Introduction to SciPy

101 - What is SciPy Installation of SciPy Import SciPy Checking SciPy Version.mp4

37:38

86 - Understanding SciPy Optimizers Sparse Data and Graphs

102 - SciPy Optimizers Roots of an Equation Minimizing Function What is a Sparse.mp4

15:19

103 - Sparse Matrix Methods Working with Graphs Adjancency Matrix.mp4

15:19

87 - Working with SciPy Spatial Data

104 - Working with Spatial Data Triangulation Convex Hull KDTrees Distance Matrix.mp4

31:31

88 - Exploring Matlab Array and Interpolation

105 - Working with matlab Array EXporting Data in Matlab Format.mp4

30:04

89 - Introduction to Plotly and Cufflinks

106 - What is Plotly and Cuffinks Features of Plotly Install Plotly.mp4

39:13

90 - Creating Plots using Geographical Plotting and Choropleth Maps

107 - What is Geographical Plotting How to import packeges Choropleth maps.mp4

15:21

91 - Introduction to Machine Learning

108 - What is Machine Learning How does Machine Learning Work.mp4

20:19

92 - Exploring Machine Learning Life Cycle

109 - Machine Learning Life Cycle Gathering Data Data Preparation Data Analysis.mp4

07:30

93 - Exploring Regression Techniques

110 - Regression Linear Regression Terminologies Related to Liner Regression.mp4

53:09

94 - Understanding Classification

111 - Classificatio Algorithm Types of classification Learners in classification Pro.mp4

40:04

95 - Working with Support Vector Machine Algorithm

112 - What is Support Vector Machine Types of SVM Hyperplane and Support Vector.mp4

27:30

96 - Working with Naive Bayes Algorithm

113 - Naive Bayes Alorithm Why it is called Naive Bayes Bayes Theorem.mp4

20:22

97 - Understanding Decision Tree Classifier

114 - What is Decision Tree Decisions Tree Classification Algorithm.mp4

31:54

Description

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.

Who is this for?

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.

What You Need to Know?

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.

More details

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:
Python
NumPy
Pandas
Matplotlib
Seaborn
Plotly
Big Data
SciPy
Who 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!
Who this course is for:
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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