Complete Python For Data Science & Machine Learning From A-Z

Python with Machine Learning & Data Science, Data Visulation, Numpy & Pandas for Data Analysis, Kaggle projects from A-Z

What you'll learn

Pandas is an open source Python package that is most widely used for data science/data analysis and machine learning tasks.

Pandas is mainly used for data analysis and associated manipulation of tabular data in DataFrames.

Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.

Pandas Pyhon aims to be the fundamental high-level building block for doing practical, real world data analysis in Python

Numpy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices.

NumPy aims to provide an array object that is up to 50x faster than traditional Python lists.

NumPy brings the computational power of languages like C and Fortran to Python.

Machine learning isn’t just useful for predictive texting or smartphone voice recognition. Machine learning is constantly being applied to new industries.

Learn Machine Learning with Hands-On Examples

What is Machine Learning?

Python instructors on OAK Academy specialize in everything from software development to data analysis, and are known for their effective.

Python is a general-purpose, object-oriented, high-level programming language.

Python is a multi-paradigm language, which means that it supports many programming approaches. Along with procedural and functional programming styles

Python is a computer programming language often used to build websites and software, automate tasks, and conduct data analysis.

Whether you work in artificial intelligence or finance or are pursuing a career in web development or data science, Python is one of the most important skills

Its simple syntax and readability makes Python perfect for Flask, Django, data science, and machine learning.

Installing Anaconda Distribution for Windows

Installing Anaconda Distribution for MacOs

Installing Anaconda Distribution for Linux

Reviewing The Jupyter Notebook

Reviewing The Jupyter Lab

Python Introduction

First Step to Coding

Using Quotation Marks in Python Coding

How Should the Coding Form and Style Be (Pep8)

Introduction to Basic Data Structures in Python

Performing Assignment to Variables

Performing Complex Assignment to Variables

Type Conversion

Arithmetic Operations in Python

Examining the Print Function in Depth

Escape Sequence Operations

Boolean Logic Expressions

Order Of Operations In Boolean Operators

Practice with Python

Examining Strings Specifically

Accessing Length Information (Len Method)

Search Method In Strings Startswith(), Endswith()

Character Change Method In Strings Replace()

Spelling Substitution Methods in String

Character Clipping Methods in String

Indexing and Slicing Character String

Complex Indexing and Slicing Operations

String Formatting with Arithmetic Operations

String Formatting With % Operator

String Formatting With String Format Method

String Formatting With f-string Method

Creation of List

Reaching List Elements – Indexing and Slicing

Adding & Modifying & Deleting Elements of List

Adding and Deleting by Methods

Adding and Deleting by Index

Other List Methods

Creation of Tuple

Reaching Tuple Elements Indexing And Slicing

Creation of Dictionary

Reaching Dictionary Elements

Adding & Changing & Deleting Elements in Dictionary

Dictionary Methods

Creation of Set

Adding & Removing Elements Methods in Sets

Difference Operation Methods In Sets

Intersection & Union Methods In Sets

Asking Questions to Sets with Methods

Comparison Operators

Structure of “if” Statements

Structure of “if-else” Statements

Structure of “if-elif-else” Statements

Structure of Nested “if-elif-else” Statements

Coordinated Programming with “IF” and “INPUT”

Ternary Condition

For Loop in Python

For Loop in Python(Reinforcing the Topic)

Using Conditional Expressions and For Loop Together

Continue Command

Break Command

List Comprehension

While Loop in Python

While Loops in Python Reinforcing the Topic

Getting know to the Functions

How to Write Function

Return Expression in Functions

Writing Functions with Multiple Argument

Writing Docstring in Functions

Using Functions and Conditional Expressions Together

Arguments and Parameters

High Level Operations with Arguments

all(), any() Functions

map() Function

filter() Function

zip() Function

enumerate() Function

max(), min() Functions

sum() Function

round() Function

Lambda Function

Local and Global Variables

Features of Class

Instantiation of Class

Attribute of Instantiation

Write Function in the Class

Inheritance Structure

Requirements

A working computer (Windows, Mac, or Linux)

No prior knowledge of Python for beginners is required

Motivation to learn the the second largest number of job postings relative program language among all others

Desire to learn machine learning python

Curiosity for python programming

Desire to learn python programming, pycharm, python pycharm

Nothing else! It’s just you, your computer and your ambition to get started today

Description

Welcome to my " Complete Python for Data Science & Machine Learning from A-Z " course.Python with Machine Learning & Data Science, Data Visulation, Numpy & Pandas for Data Analysis, Kaggle projects from A-Z Python is a computer programming language often used to build websites and software, automate tasks, and conduct data analysis. Python is a general-purpose language, meaning it can be used to create a variety of different programs and isn't specialized for any specific problems.Python instructors at OAK Academy specialize in everything from software development to data analysis and are known for their effective, friendly instruction for students of all levels.Whether you work in machine learning or finance or are pursuing a career in web development or data science, Python is one of the most important skills you can learn.Python's simple syntax is especially suited for desktop, web, and business applications. Python's design philosophy emphasizes readability and usability. Python was developed upon the premise that there should be only one way (and preferably one obvious way) to do things, a philosophy that has resulted in a strict level of code standardization. The core programming language is quite small and the standard library is also large. In fact, Python's large library is one of its greatest benefits, providing a variety of different tools for programmers suited for many different tasks.Do you want to learn one of the employer’s most requested skills? If you think so, you are at the right place. Python, machine learning, Django, python programming, machine learning python, python Bootcamp, coding, data science, data analysis, programming languages.We've designed for you "Complete Python for Data Science & Machine Learning from A-Z” a straightforward course for the Complete Python programming language.In the course, you will have down-to-earth way explanations of hands-on projects. With my course, you will learn Python Programming step-by-step. I made Python 3 programming simple and easy with exercises, challenges, and lots of real-life examples.This Python course is for everyone!My "Python: Learn Python with Real Python Hands-On Examples" is for everyone! If you don’t have any previous experience, not a problem! This course is expertly designed to teach everyone from complete beginners, right through to professionals ( as a refresher).Why Python?Python is a general-purpose, high-level, and multi-purpose programming language. The best thing about Python is, that it supports a lot of today’s technology including vast libraries for Twitter, data mining, scientific calculations, designing, back-end server for websites, engineering simulations, artificial learning, augmented reality and what not! Also, it supports all kinds of App development.No prior knowledge is needed!Python doesn't need any prior knowledge to learn it and the Ptyhon code is easy to understand for beginners.What you will learn?In this course, we will start from the very beginning and go all the way to programming with hands-on examples . We will first learn how to set up a lab and install needed software on your machine. Then during the course, you will learn the fundamentals of Python development likeInstalling Anaconda Distribution for WindowsInstalling Anaconda Distribution for MacOsInstalling Anaconda Distribution for LinuxReviewing The Jupyter NotebookReviewing The Jupyter LabPython IntroductionFirst Step to CodingUsing Quotation Marks in Python CodingHow Should the Coding Form and Style Be (Pep8)Introduction to Basic Data Structures in PythonPerforming Assignment to VariablesPerforming Complex Assignment to VariablesType ConversionArithmetic Operations in PythonExamining the Print Function in DepthEscape Sequence OperationsBoolean Logic ExpressionsOrder Of Operations In Boolean OperatorsPractice with PythonExamining Strings SpecificallyAccessing Length Information (Len Method)Search Method In Strings Startswith(), Endswith()Character Change Method In Strings Replace()Spelling Substitution Methods in StringCharacter Clipping Methods in StringIndexing and Slicing Character StringComplex Indexing and Slicing OperationsString Formatting with Arithmetic OperationsString Formatting With % OperatorString Formatting With String.Format MethodString Formatting With f-string MethodCreation of ListReaching List Elements – Indexing and SlicingAdding & Modifying & Deleting Elements of ListAdding and Deleting by MethodsAdding and Deleting by IndexOther List MethodsCreation of TupleReaching Tuple Elements Indexing And SlicingCreation of DictionaryReaching Dictionary ElementsAdding & Changing & Deleting Elements in DictionaryDictionary MethodsCreation of SetAdding & Removing Elements Methods in SetsDifference Operation Methods In SetsIntersection & Union Methods In SetsAsking Questions to Sets with MethodsComparison OperatorsStructure of “if” StatementsStructure of “if-else” StatementsStructure of “if-elif-else” StatementsStructure of Nested “if-elif-else” StatementsCoordinated Programming with “IF” and “INPUT”Ternary ConditionFor Loop in PythonFor Loop in Python(Reinforcing the Topic)Using Conditional Expressions and For Loop TogetherContinue CommandBreak CommandList ComprehensionWhile Loop in PythonWhile Loops in Python Reinforcing the TopicGetting know to the FunctionsHow to Write FunctionReturn Expression in FunctionsWriting Functions with Multiple ArgumentWriting Docstring in FunctionsUsing Functions and Conditional Expressions TogetherArguments and ParametersHigh Level Operations with Argumentsall(), any() Functionsmap() Functionfilter() Functionzip() Functionenumerate() Functionmax(), min() Functionssum() Functionround() FunctionLambda FunctionLocal and Global VariablesFeatures of ClassInstantiation of ClassAttribute of InstantiationWrite Function in the ClassInheritance StructureWith my up-to-date course, you will have a chance to keep yourself up-to-date and equip yourself with a range of Python programming skills. I am also happy to tell you that I will be constantly available to support your learning and answer questions.Do not forget ! Python for beginners has the second largest number of job postings relative to all other languages. So it will earn you a lot of money and will bring a great change in your resume.What is python?Machine learning python is a general-purpose, object-oriented, high-level programming language. Whether you work in artificial intelligence or finance or are pursuing a career in web development or data science, Python bootcamp is one of the most important skills you can learn. Python's simple syntax is especially suited for desktop, web, and business applications. Python's design philosophy emphasizes readability and usability. Python was developed on the premise that there should be only one way (and preferably, one obvious way) to do things, a philosophy that resulted in a strict level of code standardization. The core programming language is quite small and the standard library is also large. In fact, Python's large library is one of its greatest benefits, providing different tools for programmers suited for a variety of tasks.Python vs. R: What is the Difference?Python and R are two of today's most popular programming tools. When deciding between Python and R in data science , you need to think about your specific needs. On one hand, Python is relatively easy for beginners to learn, is applicable across many disciplines, has a strict syntax that will help you become a better coder, and is fast to process large datasets. On the other hand, R has over 10,000 packages for data manipulation, is capable of easily making publication-quality graphics, boasts superior capability for statistical modeling, and is more widely used in academia, healthcare, and finance.What does it mean that Python is object-oriented?Python is a multi-paradigm language, which means that it supports many data analysis programming approaches. Along with procedural and functional programming styles, Python also supports the object-oriented style of programming. In object-oriented programming, a developer completes a programming project by creating Python objects in code that represent objects in the actual world. These objects can contain both the data and functionality of the real-world object. To generate an object in Python you need a class. You can think of a class as a template. You create the template once, and then use the template to create as many objects as you need. Python classes have attributes to represent data and methods that add functionality. A class representing a car may have attributes like color, speed, and seats and methods like driving, steering, and stopping.What are the limitations of Python?Python is a widely used, general-purpose programming language, but it has some limitations. Because Python in machine learning is an interpreted, dynamically typed language, it is slow compared to a compiled, statically typed language like C. Therefore, Python is useful when speed is not that important. Python's dynamic type system also makes it use more memory than some other programming languages, so it is not suited to memory-intensive applications. The Python virtual engine that runs Python code runs single-threaded, making concurrency another limitation of the programming language. Though Python is popular for some types of game development, its higher memory and CPU usage limits its usage for high-quality 3D game development. That being said, computer hardware is getting better and better, and the speed and memory limitations of Python are getting less and less relevant.How is Python used?Python is a general programming language used widely across many industries and platforms. One common use of Python is scripting, which means automating tasks in the background. Many of the scripts that ship with Linux operating systems are Python scripts. Python is also a popular language for machine learning, data analytics, data visualization, and data science because its simple syntax makes it easy to quickly build real applications. You can use Python to create desktop applications. Many developers use it to write Linux desktop applications, and it is also an excellent choice for web and game development. Python web frameworks like Flask and Django are a popular choice for developing web applications. Recently, Python is also being used as a language for mobile development via the Kivy third-party library.What jobs use Python?Python is a popular language that is used across many industries and in many programming disciplines. DevOps engineers use Python to script website and server deployments. Web developers use Python to build web applications, usually with one of Python's popular web frameworks like Flask or Django. Data scientists and data analysts use Python to build machine learning models, generate data visualizations, and analyze big data. Financial advisors and quants (quantitative analysts) use Python to predict the market and manage money. Data journalists use Python to sort through information and create stories. Machine learning engineers use Python to develop neural networks and artificial intelligent systems.How do I learn Python on my own?Python has a simple syntax that makes it an excellent programming language for a beginner to learn. To learn Python on your own, you first must become familiar with the syntax. But you only need to know a little bit about Python syntax to get started writing real code; you will pick up the rest as you go. Depending on the purpose of using it, you can then find a good Python tutorial, book, or course that will teach you the programming language by building a complete application that fits your goals. If you want to develop games, then learn Python game development. If you're going to build web applications, you can find many courses that can teach you that, too. Udemy’s online courses are a great place to start if you want to learn Python on your own.Why would you want to take this course?Our answer is simple: The quality of teaching.OAK Academy based in London is an online education company. OAK Academy gives education in the field of IT, Software, Design, development in English, Portuguese, Spanish, Turkish, and a lot of different languages on the Udemy platform where it has over 2000 hours of video education lessons. OAK Academy both increases its education series number by publishing new courses, and it makes students aware of all the innovations of already published courses by upgrading.When you enroll, you will feel the OAK Academy`s seasoned developers' expertise. Questions sent by students to our instructors are answered by our instructors within 48 hours at the latest.Video and Audio Production QualityAll our videos are created/produced as high-quality video and audio to provide you the best learning experience.You will be,Seeing clearlyHearing clearlyMoving through the course without distractionsYou'll also get:Lifetime Access to The CourseFast & Friendly Support in the Q&A sectionUdemy Certificate of Completion Ready for DownloadDive in now!We offer full support, answering any questions.See you in the  " Complete Python for Data Science & Machine Learning from A-Z " course.Python with Machine Learning & Data Science, Data Visulation, Numpy & Pandas for Data Analysis, Kaggle projects from A-Z

Overview

Section 1: Installations

Lecture 1 Installing Anaconda Distribution for Windows

Lecture 2 Installing Anaconda Distribution for MacOs

Lecture 3 Installing Anaconda Distribution for Linux

Lecture 4 Reviewing The Jupyter Notebook

Lecture 5 Reviewing The Jupyter Lab

Section 2: First Step to Coding

Lecture 6 Python Introduction

Lecture 7 Project Files

Lecture 8 First Step to Coding

Lecture 9 Using Quotation Marks in Python Coding

Lecture 10 How Should the Coding Form and Style Be (Pep8)

Section 3: Basic Operations with Python

Lecture 11 Introduction to Basic Data Structures in Python

Lecture 12 Performing Assignment to Variables

Lecture 13 Performing Complex Assignment to Variables

Lecture 14 Type Conversion

Lecture 15 Arithmetic Operations in Python

Lecture 16 Examining the Print Function in Depth

Lecture 17 Escape Sequence Operations

Section 4: Boolean Data Type in Python Programming Language

Lecture 18 Boolean Logic Expressions

Lecture 19 Order Of Operations In Boolean Operators

Lecture 20 Practice with Python

Section 5: String Data Type in Python Programming Language

Lecture 21 Examining Strings Specifically

Lecture 22 Accessing Length Information (Len Method)

Lecture 23 Search Method In Strings Startswith(), Endswith()

Lecture 24 Character Change Method In Strings Replace()

Lecture 25 Spelling Substitution Methods in String

Lecture 26 Character Clipping Methods in String

Lecture 27 Indexing and Slicing Character String

Lecture 28 Complex Indexing and Slicing Operations

Lecture 29 String Formatting with Arithmetic Operations

Lecture 30 String Formatting With % Operator

Lecture 31 String Formatting With String.Format Method

Lecture 32 String Formatting With f-string Method

Section 6: List Data Structure in Python Programming Language

Lecture 33 Creation of List

Lecture 34 Reaching List Elements – Indexing and Slicing

Lecture 35 Adding & Modifying & Deleting Elements of List

Lecture 36 Adding and Deleting by Methods

Lecture 37 Adding and Deleting by Index

Lecture 38 Other List Methods

Section 7: Tuple Data Structure in Python Programming Language

Lecture 39 Creation of Tuple

Lecture 40 Reaching Tuple Elements Indexing And Slicing

Section 8: Dictionary Data Structure in Python Programming Language

Lecture 41 Creation of Dictionary

Lecture 42 Reaching Dictionary Elements

Lecture 43 Adding & Changing & Deleting Elements in Dictionary

Lecture 44 Dictionary Methods

Section 9: Set Data Structure in Python Programming Language

Lecture 45 Creation of Set

Lecture 46 Adding & Removing Elements Methods in Sets

Lecture 47 Difference Operation Methods In Sets

Lecture 48 Intersection & Union Methods In Sets

Lecture 49 Asking Questions to Sets with Methods

Section 10: Conditional Expressions in Python Programming Language

Lecture 50 Comparison Operators

Lecture 51 Structure of “if” Statements

Lecture 52 Structure of “if-else” Statements

Lecture 53 Structure of “if-elif-else” Statements

Lecture 54 Structure of Nested “if-elif-else” Statements

Lecture 55 Coordinated Programming with “IF” and “INPUT”

Lecture 56 Ternary Condition

Section 11: For Loop in Python Programming Language

Lecture 57 For Loop in Python

Lecture 58 For Loop in Python(Reinforcing the Topic)

Lecture 59 Using Conditional Expressions and For Loop Together

Lecture 60 Continue Command

Lecture 61 Break Command

Lecture 62 List Comprehension

Section 12: While Loop in Python Programming Language

Lecture 63 While Loop in Python

Lecture 64 While Loops in Python Reinforcing the Topic

Section 13: Functions in Python Programming Language

Lecture 65 Getting know to the Functions

Lecture 66 How to Write Function

Lecture 67 Return Expression in Functions

Lecture 68 Writing Functions with Multiple Argument

Lecture 69 Writing Docstring in Functions

Lecture 70 Using Functions and Conditional Expressions Together

Section 14: Arguments And Parameters in Python Programming Language

Lecture 71 Arguments and Parameters

Lecture 72 High Level Operations with Arguments

Section 15: Most Used Functions in Python Programming Language

Lecture 73 all(), any() Functions

Lecture 74 map() Function

Lecture 75 filter() Function

Lecture 76 zip() Function

Lecture 77 enumerate() Function

Lecture 78 max(), min() Functions

Lecture 79 sum() Function

Lecture 80 round() Function

Lecture 81 Lambda Function

Section 16: Class Structure in Python Programming Language

Lecture 82 Local and Global Variables

Lecture 83 Features of Class

Lecture 84 Instantiation of Class

Lecture 85 Attribute of Instantiation

Lecture 86 Write Function in the Class

Lecture 87 Inheritance Structure

Section 17: NumPy Library Introduction

Lecture 88 Introduction to NumPy Library

Lecture 89 Notebook Project Files Link regarding NumPy Python Programming Language Library

Lecture 90 The Power of NumPy

Section 18: Creating NumPy Array in Python

Lecture 91 Creating NumPy Array with The Array() Function

Lecture 92 Creating NumPy Array with Zeros() Function

Lecture 93 Creating NumPy Array with Ones() Function

Lecture 94 Creating NumPy Array with Full() Function

Lecture 95 Creating NumPy Array with Arange() Function

Lecture 96 Creating NumPy Array with Eye() Function

Lecture 97 Creating NumPy Array with Linspace() Function

Lecture 98 Creating NumPy Array with Random() Function

Lecture 99 Properties of NumPy Array

Section 19: Functions in the NumPy Library

Lecture 100 Reshaping a NumPy Array: Reshape() Function

Lecture 101 Identifying the Largest Element of a Numpy Array

Lecture 102 Detecting Least Element of Numpy Array: Min(), Ar

Lecture 103 Concatenating Numpy Arrays: Concatenate() Functio

Lecture 104 Splitting One-Dimensional Numpy Arrays: The Split

Lecture 105 Splitting Two-Dimensional Numpy Arrays: Split(),

Lecture 106 Sorting Numpy Arrays: Sort() Function

Section 20: Indexing, Slicing, and Assigning NumPy Arrays

Lecture 107 Indexing Numpy Arrays

Lecture 108 Slicing One-Dimensional Numpy Arrays

Lecture 109 Slicing Two-Dimensional Numpy Arrays

Lecture 110 Assigning Value to One-Dimensional Arrays

Lecture 111 Assigning Value to Two-Dimensional Array

Lecture 112 Fancy Indexing of One-Dimensional Arrrays

Lecture 113 Fancy Indexing of Two-Dimensional Arrrays

Lecture 114 Combining Fancy Index with Normal Indexing

Lecture 115 Combining Fancy Index with Normal Slicing

Section 21: Operations in Numpy Library

Lecture 116 Operations with Comparison Operators

Lecture 117 Arithmetic Operations in Numpy

Lecture 118 Statistical Operations in Numpy

Lecture 119 Solving Second-Degree Equations with NumPy

Section 22: Pandas Library Introduction

Lecture 120 Introduction to Pandas Library

Lecture 121 Pandas Project Files Link

Section 23: Series Structures in the Pandas Library

Lecture 122 Creating a Pandas Series with a List

Lecture 123 Creating a Pandas Series with a Dictionary

Lecture 124 Creating Pandas Series with NumPy Array

Lecture 125 Object Types in Series

Lecture 126 Examining the Primary Features of the Pandas Seri

Lecture 127 Most Applied Methods on Pandas Series

Lecture 128 Indexing and Slicing Pandas Series

Section 24: DataFrame Structures in Pandas Library

Lecture 129 Creating Pandas DataFrame with List

Lecture 130 Creating Pandas DataFrame with NumPy Array

Lecture 131 Creating Pandas DataFrame with Dictionary

Lecture 132 Examining the Properties of Pandas DataFrames

Section 25: Element Selection Operations in DataFrame Structures

Lecture 133 Element Selection Operations in Pandas DataFrames: Lesson 1

Lecture 134 Element Selection Operations in Pandas DataFrames: Lesson 2

Lecture 135 Top Level Element Selection in Pandas DataFrames:Lesson 1

Lecture 136 Top Level Element Selection in Pandas DataFrames:Lesson 2

Lecture 137 Top Level Element Selection in Pandas DataFrames:Lesson 3

Lecture 138 Element Selection with Conditional Operations in

Section 26: Structural Operations on Pandas DataFrame

Lecture 139 Adding Columns to Pandas Data Frames

Lecture 140 Removing Rows and Columns from Pandas Data frames

Lecture 141 Null Values in Pandas Dataframes

Lecture 142 Dropping Null Values: Dropna() Function

Lecture 143 Filling Null Values: Fillna() Function

Lecture 144 Setting Index in Pandas DataFrames

Section 27: Multi-Indexed DataFrame Structures

Lecture 145 Multi-Index and Index Hierarchy in Pandas DataFrames

Lecture 146 Element Selection in Multi-Indexed DataFrames

Lecture 147 Selecting Elements Using the xs() Function in Multi-Indexed DataFrames

Section 28: Structural Concatenation Operations in Pandas DataFrame

Lecture 148 Concatenating Pandas Dataframes: Concat Function

Lecture 149 Merge Pandas Dataframes: Merge() Function: Lesson 1

Lecture 150 Merge Pandas Dataframes: Merge() Function: Lesson 2

Lecture 151 Merge Pandas Dataframes: Merge() Function: Lesson 3

Lecture 152 Merge Pandas Dataframes: Merge() Function: Lesson 4

Lecture 153 Joining Pandas Dataframes: Join() Function

Section 29: Functions That Can Be Applied on a DataFrame

Lecture 154 Loading a Dataset from the Seaborn Library

Lecture 155 Examining the Data Set 1

Lecture 156 Aggregation Functions in Pandas DataFrames

Lecture 157 Examining the Data Set 2

Lecture 158 Coordinated Use of Grouping and Aggregation Functions in Pandas Dataframes

Lecture 159 Advanced Aggregation Functions: Aggregate() Function

Lecture 160 Advanced Aggregation Functions: Filter() Function

Lecture 161 Advanced Aggregation Functions: Transform() Function

Lecture 162 Advanced Aggregation Functions: Apply() Function

Section 30: Pivot Tables in Pandas Library

Lecture 163 Examining the Data Set 3

Lecture 164 Pivot Tables in Pandas Library

Section 31: File Operations in Pandas Library

Lecture 165 Accessing and Making Files Available

Lecture 166 Data Entry with Csv and Txt Files

Lecture 167 Data Entry with Excel Files

Lecture 168 Outputting as an CSV Extension

Lecture 169 Outputting as an Excel File

Section 32: Matplotlib

Lecture 170 What is Matplotlib

Lecture 171 Using Pyplot

Lecture 172 Pyplot – Pylab - Matplotlib

Lecture 173 Figure, Subplot and Axes

Lecture 174 Figure Customization

Lecture 175 Plot Customization

Lecture 176 Grid, Spines, Ticks

Lecture 177 Basic Plots in Matplotlib I

Lecture 178 Basic Plots in Matplotlib II

Section 33: Seaborn

Lecture 179 What is Seaborn?

Lecture 180 Controlling Figure Aesthetics in Seaborn

Lecture 181 Example in Seaborn

Lecture 182 Color Palettes in Seaborn

Lecture 183 Basic Plots in Seaborn

Lecture 184 Multi-Plots in Seaborn

Lecture 185 Regression Plots and Squarify in Seaborn

Section 34: "Geoplotlib "

Lecture 186 What is Geoplotlib?

Lecture 187 Example - 1

Lecture 188 Example - 2

Lecture 189 Example - 3

Section 35: Intro to Machine Learning with Python

Lecture 190 What is Machine Learning?

Lecture 191 Machine Learning Terminology

Lecture 192 Machine Learning: Project Files

Section 36: Evaluation Metrics in Machine Learning

Lecture 193 Classification vs Regression in Machine Learning

Lecture 194 Machine Learning Model Performance Evaluation: Classification Error Metrics

Lecture 195 Evaluating Performance: Regression Error Metrics in Python

Lecture 196 Machine Learning With Python

Section 37: Supervised Learning with Machine Learning

Lecture 197 What is Supervised Learning in Machine Learning?

Section 38: Linear Regression Algorithm in Machine Learning A-Z

Lecture 198 Linear Regression Algorithm Theory in Machine Learning A-Z

Lecture 199 Linear Regression Algorithm With Python Part 1

Lecture 200 Linear Regression Algorithm With Python Part 2

Lecture 201 Linear Regression Algorithm With Python Part 3

Lecture 202 Linear Regression Algorithm With Python Part 4

Section 39: Bias Variance Trade-Off in Machine Learning

Lecture 203 What is Bias Variance Trade-Off?

Section 40: Logistic Regression Algorithm in Machine Learning A-Z

Lecture 204 What is Logistic Regression Algorithm in Machine Learning?

Lecture 205 Logistic Regression Algorithm with Python Part 1

Lecture 206 Logistic Regression Algorithm with Python Part 2

Lecture 207 Logistic Regression Algorithm with Python Part 3

Lecture 208 Logistic Regression Algorithm with Python Part 4

Lecture 209 Logistic Regression Algorithm with Python Part 5

Section 41: K-fold Cross-Validation in Machine Learning A-Z

Lecture 210 K-Fold Cross-Validation Theory

Lecture 211 K-Fold Cross-Validation with Python

Section 42: K Nearest Neighbors Algorithm in Machine Learning A-Z

Lecture 212 K Nearest Neighbors Algorithm Theory

Lecture 213 K Nearest Neighbors Algorithm with Python Part 1

Lecture 214 K Nearest Neighbors Algorithm with Python Part 2

Lecture 215 K Nearest Neighbors Algorithm with Python Part 3

Section 43: Hyperparameter Optimization

Lecture 216 Hyperparameter Optimization Theory

Lecture 217 Hyperparameter Optimization with Python

Section 44: Decision Tree Algorithm in Machine Learning A-Z

Lecture 218 Decision Tree Algorithm Theory

Lecture 219 Decision Tree Algorithm with Python Part 1

Lecture 220 Decision Tree Algorithm with Python Part 2

Lecture 221 Decision Tree Algorithm with Python Part 3

Lecture 222 Decision Tree Algorithm with Python Part 4

Lecture 223 Decision Tree Algorithm with Python Part 5

Section 45: Random Forest Algorithm in Machine Learning A-Z

Lecture 224 Random Forest Algorithm Theory

Lecture 225 Random Forest Algorithm with Pyhon Part 1

Lecture 226 Random Forest Algorithm with Pyhon Part 2

Section 46: Support Vector Machine Algorithm in Machine Learning A-Z

Lecture 227 Support Vector Machine Algorithm Theory

Lecture 228 Support Vector Machine Algorithm with Python Part 1

Lecture 229 Support Vector Machine Algorithm with Python Part 2

Lecture 230 Support Vector Machine Algorithm with Python Part 3

Lecture 231 Support Vector Machine Algorithm with Python Part 4

Section 47: Unsupervised Learning with Machine Learning

Lecture 232 Unsupervised Learning Overview

Section 48: K Means Clustering Algorithm in Machine Learning A-Z

Lecture 233 K Means Clustering Algorithm Theory

Lecture 234 K Means Clustering Algorithm with Python Part 1

Lecture 235 K Means Clustering Algorithm with Python Part 2

Lecture 236 K Means Clustering Algorithm with Python Part 3

Lecture 237 K Means Clustering Algorithm with Python Part 4

Section 49: Hierarchical Clustering Algorithm in machine learning data science

Lecture 238 Hierarchical Clustering Algorithm Theory

Lecture 239 Hierarchical Clustering Algorithm with Python Part 1

Lecture 240 Hierarchical Clustering Algorithm with Python Part 2

Section 50: Principal Component Analysis (PCA) in Machine Learning A-Z

Lecture 241 Principal Component Analysis (PCA) Theory

Lecture 242 Principal Component Analysis (PCA) with Python Part 1

Lecture 243 Principal Component Analysis (PCA) with Python Part 2

Lecture 244 Principal Component Analysis (PCA) with Python Part 3

Section 51: Recommender System Algorithm in Machine Learning A-Z

Lecture 245 What is the Recommender System? Part 1

Lecture 246 What is the Recommender System? Part 2

Section 52: First Contact with Kaggle

Lecture 247 What is Kaggle?

Lecture 248 FAQ about Kaggle

Lecture 249 Registering on Kaggle and Member Login Procedures

Lecture 250 Project Link File - Hearth Attack Prediction Project, Machine Learning

Lecture 251 Getting to Know the Kaggle Homepage

Section 53: Competition Section on Kaggle

Lecture 252 Competitions on Kaggle: Lesson 1

Lecture 253 Competitions on Kaggle: Lesson 2

Section 54: Dataset Section on Kaggle

Lecture 254 Datasets on Kaggle

Section 55: Code Section on Kaggle

Lecture 255 Examining the Code Section in Kaggle: Lesson 1

Lecture 256 Examining the Code Section in Kaggle: Lesson 2

Lecture 257 Examining the Code Section in Kaggle: Lesson 3

Section 56: Discussion Section on Kaggle

Lecture 258 What is Discussion on Kaggle?

Section 57: Other Most Used Options on Kaggle

Lecture 259 Courses in Kaggle

Lecture 260 Ranking Among Users on Kaggle

Lecture 261 Blog and Documentation Sections

Section 58: Details on Kaggle

Lecture 262 User Page Review on Kaggle

Lecture 263 Treasure in The Kaggle

Lecture 264 Publishing Notebooks on Kaggle

Lecture 265 What Should Be Done to Achieve Success in Kaggle?

Section 59: Introduction to Machine Learning with Real Hearth Attack Prediction Project

Lecture 266 First Step to the Hearth Attack Prediction Project

Lecture 267 FAQ about Machine Learning, Data Science

Lecture 268 Notebook Design to be Used in the Project

Lecture 269 Project Link File - Hearth Attack Prediction Project, Machine Learning

Lecture 270 Examining the Project Topic

Lecture 271 Recognizing Variables In Dataset

Section 60: First Organization

Lecture 272 Required Python Libraries

Lecture 273 Loading the Statistics Dataset in Data Science

Lecture 274 Initial analysis on the dataset

Section 61: Preparation For Exploratory Data Analysis (EDA) in Data Science

Lecture 275 Examining Missing Values

Lecture 276 Examining Unique Values

Lecture 277 Separating variables (Numeric or Categorical)

Lecture 278 Examining Statistics of Variables

Section 62: Exploratory Data Analysis (EDA) - Uni-variate Analysis

Lecture 279 Numeric Variables (Analysis with Distplot): Lesson 1

Lecture 280 Numeric Variables (Analysis with Distplot): Lesson 2

Lecture 281 Categoric Variables (Analysis with Pie Chart): Lesson 1

Lecture 282 Categoric Variables (Analysis with Pie Chart): Lesson 2

Lecture 283 Examining the Missing Data According to the Analysis Result

Section 63: Exploratory Data Analysis (EDA) - Bi-variate Analysis

Lecture 284 Numeric Variables – Target Variable (Analysis with FacetGrid): Lesson 1

Lecture 285 Numeric Variables – Target Variable (Analysis with FacetGrid): Lesson 2

Lecture 286 Categoric Variables – Target Variable (Analysis with Count Plot): Lesson 1

Lecture 287 Categoric Variables – Target Variable (Analysis with Count Plot): Lesson 2

Lecture 288 Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 1

Lecture 289 Examining Numeric Variables Among Themselves (Analysis with Pair Plot) Lesson 2

Lecture 290 Feature Scaling with the Robust Scaler Method

Lecture 291 Creating a New DataFrame with the Melt() Function

Lecture 292 Numerical - Categorical Variables (Analysis with Swarm Plot): Lesson 1

Lecture 293 Numerical - Categorical Variables (Analysis with Swarm Plot): Lesson 2

Lecture 294 Numerical - Categorical Variables (Analysis with Box Plot): Lesson 1

Lecture 295 Numerical - Categorical Variables (Analysis with Box Plot): Lesson 2

Lecture 296 Relationships between variables (Analysis with Heatmap): Lesson 1

Lecture 297 Relationships between variables (Analysis with Heatmap): Lesson 2

Section 64: Preparation for Modelling in Machine Learning

Lecture 298 Dropping Columns with Low Correlation

Lecture 299 Visualizing Outliers

Lecture 300 Dealing with Outliers – Trtbps Variable: Lesson 1

Lecture 301 Dealing with Outliers – Trtbps Variable: Lesson 2

Lecture 302 Dealing with Outliers – Thalach Variable

Lecture 303 Dealing with Outliers – Oldpeak Variable

Lecture 304 Determining Distributions of Numeric Variables

Lecture 305 Transformation Operations on Unsymmetrical Data

Lecture 306 Applying One Hot Encoding Method to Categorical Variables

Lecture 307 Feature Scaling with the Robust Scaler Method for Machine Learning Algorithms

Lecture 308 Separating Data into Test and Training Set

Section 65: Modelling for Machine Learning

Lecture 309 Logistic Regression

Lecture 310 Cross Validation

Lecture 311 Roc Curve and Area Under Curve (AUC)

Lecture 312 Hyperparameter Optimization (with GridSearchCV)

Lecture 313 Decision Tree Algorithm

Lecture 314 Support Vector Machine Algorithm

Lecture 315 Random Forest Algorithm

Lecture 316 Hyperparameter Optimization (with GridSearchCV)

Section 66: Conclusion

Lecture 317 Project Conclusion and Sharing

Section 67: Extra

Lecture 318 Complete Python for Data Science & Machine Learning from A-Z

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