2 In 1: Python Machine Learning Plus 30 Hour Python Bootcamp

Learn model building, algorithms, data science PLUS 30 hours of step by step coding, libraries, arguments, projects +++

What you'll learn

Define what Machine Learning does and its importance

Learn the different types of Descriptive Statistics

Apply and use Various Operations in Python

Explore the usage of Two Categories of Supervised Learning

Learn the difference of the Three Categories of Machine Learning

Understand the Role of Machine Learning

Explain the meaning of Probability and its importance

Define how Probability Process happen

Discuss the definition of Objectives and Data Gathering Step

Know the different concepts of Data Preparation and Data Exploratory Analysis Step

Define what is Supervised Learning

Differentiate Key Differences Between Supervised,Unsupervised,and Reinforced Learning

Explain the importance of Linear Regression

Learn the different types of Logistic Regression

Learn what is an Integrated Development Environment and its importance

Understand the factors why Developers use Integrated Development Environment

Learn the most important factors on How to Perform Addition operation and close Jupyter Notebook

Discuss Arithmetic Operation in Python

Identify the different Types of Built-in-Data Types in Python

Learn the most important considerations of Dictionaries-Built-in Data types

Explain the usage of Operations in Python and its importance

Understand the importance of Logical Operators

Define the different types of Controlled Statements

Be able to create and write a program to find maximum number

Differentiate the different types of range functions in Python

Explain what is Statistics, Probability and key concepts

Introduction to Python

Date and Time in Python

Sets and Trigonometry

Logarithmic in Python

Arrays in Python

Round off, and Complex Numbers

Strings in Python

Strings, ord, and chr

Lists in Python

Tuples in Python

Multiple Sequences

Loops and List in Python

Appending Sequences

Comprehension in Python

List, Item and Iterators

Zip and Attributes in Python

Mapping in Python

dir Attributes

Zip and Map Operator

Printing Dictionaries Items

Arguments and Functions in Python

Sequences in Python

Defining Functions

Changer Function

def in Python

Knownly Type of a Function

def Statementdef Statement

String Code, and Sum Tree

Sum Tree

Echo and Lambda Function

Schedule Function

def and Reducing Function in Python

for and if in Range

def Saver and ASCII, and Exception

Get Attributes and Decorator in Python

Turtle and Compilation

Logging and HTTP

Make Calculator

Binary Numbers in Python

Countdown Time in Python

Size and Path of a File

Data Visualization

Pandas Library

Encoding and Decoding in Python

Shelve in Python

Requirements

No technical knowledge or experience is required to get going in this course

A basic understanding of the importance of data science will be useful

Laptop, or Computer, or Mobile

Internet Connection

Description

Course 1: Python Machine Learning > Section 1 - Section 68Course 2: Python Bootcamp 30 Hours Of Step By Step > Section 69 - 94Everything you get with this 2 in 1 course:234-page Machine Learning workbook containing all the reference material44 hours of clear and concise step by step instructions, practical lessons and engagement25 Python coding files so you can download and follow along in the bootcamp to enhance your learning35 quizzes and knowledge checks at various stages to test your learning and confirm your growthIntroduce yourself to our community of students in this course and tell us your goalsEncouragement & celebration of your progress: 25%, 50%, 75% and then 100% when you get your certificateThis course will help you develop Machine Learning skills for solving real-life problems in the new digital world. Machine Learning combines computer science and statistics to analyze raw real-time data, identify trends, and make predictions. The participants will explore key techniques and tools to build Machine Learning solutions for businesses. You don’t need to have any technical knowledge to learn this skill.What will you learn:Define what Machine Learning does and its importanceUnderstand the Role of Machine LearningExplain what is StatisticsLearn the different types of Descriptive StatisticsExplain the meaning of Probability and its importanceDefine how Probability Process happensDiscuss the definition of Objectives and Data Gathering StepKnow the different concepts of Data Preparation and Data Exploratory Analysis StepDefine what is Supervised LearningDifferentiate Key Differences Between Supervised, Unsupervised, and Reinforced LearningLearn the difference between the Three Categories of Machine LearningExplore the usage of Two Categories of Supervised LearningExplain the importance of Linear RegressionLearn the different types of Logistic RegressionLearn what is an Integrated Development Environment and its importanceUnderstand the factors why Developers use Integrated Development EnvironmentLearn the most important factors on How to Perform Addition operations and close the Jupyter NotebookApply and use Various Operations in PythonDiscuss Arithmetic Operation in PythonIdentify the different types of Built-in-Data Types in PythonLearn the most important considerations of Dictionaries-Built-in Data typesExplain the usage of Operations in Python and its importanceUnderstand the importance of Logical OperatorsDefine the different types of Controlled StatementsBe able to create and write a program to find the maximum number…and more!Contents and OverviewYou'll start with the History of Machine Learning; Difference Between Traditional Programming and Machine Learning; What does Machine Learning do; Definition of Machine Learning; Apply Apple Sorting Example Experiences; Role of Machine Learning; Machine Learning Key Terms; Basic Terminologies of Statistics; Descriptive Statistics-Types of Statistics; Types of Descriptive Statistics; What is Inferential Statistics; What is Analysis and its types; Probability and Real-life Examples; How Probability is a Process; Views of Probability; Base Theory of Probability.Then you will learn about Defining Objectives and Data Gathering Step; Data Preparation and Data Exploratory Analysis Step; Building a Machine Learning Model and Model Evaluation; Prediction Step in the Machine Learning Process; How can a machine solve a problem-Lecture overview; What is Supervised Learning; What is Unsupervised Learning; What is Reinforced Learning; Key Differences Between Supervised,Unsupervised and Reinforced Learning; Three Categories of Machine Learning; What is Regression, Classification and Clustering; Two Categories of Supervised Learning; Category of Unsupervised Learning; Comparison of Regression , Classification and Clustering; What is Linear Regression; Advantages and Disadvantages of Linear Regression; Limitations of Linear Regression; What is Logistic Regression; Comparison of Linear Regression and Logistic Regression; Types of Logistic Regression; Advantages and Disadvantages of Logistic Regression; Limitations of Logistic Regression; What is Decision tree and its importance in Machine learning; Advantages and Disadvantages of Decision Tree.We will also cover What is Integrated Development Environment; Parts of Integrated Development Environment; Why Developers Use Integrated Development Environment; Which IDE is used for Machine Learning; What are Open Source IDE; What is Python; Best IDE for Machine Learning along with Python; Anaconda Distribution Platform and Jupyter IDE; Three Important Tabs in Jupyter; Creating new Folder and Notebook in Jupyter; Creating Three Variables in Notebook; How to Check Available Variables in Notebook; How to Perform Addition operation and Close Jupyter Notebook; How to Avoid Errors in Jupyter Notebook; History of Python; Applications of Python; What is Variable-Fundamentals of Python; Rules for Naming Variables in Python; DataTypes in Python; Arithmetic Operation in Python; Various Operations in Python; Comparison Operation in Python; Logical Operations in Python; Identity Operation in Python; Membership Operation in Python; Bitwise Operation in Python; Data Types in Python; Operators in Python; Control Statements in Python; Libraries in Python; Libraries in Python; What is Scipy library; What is Pandas Library; What is Statsmodel and its features;This course will also tackle Data Visualisation & Scikit Learn; What is Data Visualization; Matplotib Library; Seaborn Library; Scikit-learn Library; What is Dataset; Components of Dataset; Data Collection & Preparation; What is Meant by Data Collection; Understanding Data; Exploratory Data Analysis; Methods of Exploratory Data Analysis; Data Pre-Processing; Categorical Variables; Data Pre-processing Techniques.This course will also discuss What is Linear Regression and its Use Case; Dataset For Linear Regression; Import library and Load Data set- steps of linear regression; Remove the Index Column-Steps of Linear Regression; Exploring Relationship between Predictors and Response; Pairplot method explanation; Corr and Heatmap method explanation; Creating Simple Linear Regression Model; Interpreting Model Coefficients; Making Predictions with our Model; Model Evaluation Metric; Implementation of Linear Regression-lecture overview; Uploading the Dataset in Jupyter Notebook; Importing Libraries and Load Dataset into Dataframe; Remove the Index Column; Exploratory Analysis -relation of predictor and response; Creation of Linear Regression Model; Model Coefficients; Making Predictions; Evaluation of Model Performance.Next, you will learn about Model Evaluation Metrics and Logistic Regression - Diabetes Model.Who are the Instructors?Samidha Kurle from Digital Regenesys is your lead instructor – a professional making a living from her teaching skills with expertise in Machine Learning. She has joined with content creator Peter Alkema to bring you this amazing new course.You'll get premium support and feedback to help you become more confident with finance!Our happiness guarantee…We have a 30-day 100% money-back guarantee, so if you aren't happy with your purchase, we will refund your course - no questions asked!We can't wait to see you on the course!Enrol now, and master Machine Learning!Peter and Samidha

Overview

Section 1: Introduction

Lecture 1 Python Machine Learning - Introduction

Lecture 2 Course Overview On A Wipeboard: Mindmap Of Machine Learning In Python

Lecture 3 Introduce Yourself to Your Fellow Students And Tell Everyone What are Your Goals

Lecture 4 Let's Celebrate Your Progress In This Course: 25% > 50% > 75% > 100%!!

Lecture 5 Preview & Download The 234 Page Machine Learning Workbook You Get In This Course

Section 2: Introduction to Machine Learning

Lecture 6 Introduction of Instructor

Lecture 7 Machine Learning Lecture Outline

Lecture 8 Understanding of Thinking and Learning Process in Humans

Lecture 9 How Humans Think and Why we Need Machine Learning

Lecture 10 History of Machine Learning

Lecture 11 Difference Between Traditional Programming and Machine Learning

Lecture 12 Machine Learning Example

Section 3: Knowledge Check 1

Section 4: What Is Machine Learning

Lecture 13 What does Machine Learning do

Lecture 14 Definition of Machine Learning

Lecture 15 Apply Apple Sorting Example Experiences

Lecture 16 Role of Machine Learning

Lecture 17 Machine Learning Key Terms

Section 5: Knowledge Check 2

Section 6: Statistics

Lecture 18 What is Statistics

Lecture 19 Basic Terminologies of Statistics

Lecture 20 Descriptive Statistics-Types of Statistics

Lecture 21 Types of Descriptive Statistics

Lecture 22 What is Inferential Statistics

Lecture 23 What is Analysis and its types

Section 7: Knowledge Check 3

Section 8: Probability

Lecture 24 Introduction to Probability

Lecture 25 Probability and Real life Examples

Lecture 26 What is Probability

Lecture 27 How Probability is a Process

Lecture 28 Calculate Probability of an Event-Example

Lecture 29 Probability of One Fair Six-Sided Die-Example

Lecture 30 Views of Probability

Lecture 31 Base Theory of Probability

Lecture 32 Rain chances on a picnic day-Probability Example

Section 9: Knowledge Check 4

Section 10: Machine Learning Quiz 1

Section 11: Machine Learning Process

Lecture 33 Defining Objectives and Data Gathering Step

Lecture 34 Data Preparation and Data Exploratory Analysis Step

Lecture 35 Building a Machine Learning Model and Model Evaluation

Lecture 36 Prediction Step in the Machine Learning Process

Section 12: Knowledge Check 5

Section 13: Types of Machine Learning

Lecture 37 How can a machine solve a problem-Lecture overview

Lecture 38 What is Supervised Learning

Lecture 39 What is Unsupervised Learning

Lecture 40 What is Reinforced Learning

Lecture 41 Key Differences Between Supervised,Unsupervised and Reinforced Learning

Section 14: Knowledge Check 6

Section 15: Machine Learning Algorithms Part 1

Lecture 42 Three Categories of Machine Learning

Lecture 43 What is Regression, Classification and Clustering

Lecture 44 Two Categories of Supervised Learning

Lecture 45 Category of Unsupervised Learning

Lecture 46 Comparison of Regression , Classification and Clustering

Section 16: Knowledge Check 7

Section 17: Machine Learning Algorithms Part 2

Lecture 47 What is Linear Regression

Lecture 48 Advantages and Disadvantages of Linear Regression

Lecture 49 Limitations of Linear Regression

Lecture 50 You've Achieved 25% >> Let's Celebrate Your Progress And Keep Going To 50% >>

Lecture 51 What is Logistic Regression

Lecture 52 Comparison of Linear Regression and Logistic Regression

Lecture 53 Types of Logistic Regression

Lecture 54 Advantages and Disadvantages of Logistic Regression

Lecture 55 Limitations of Logistic Regression

Lecture 56 What is Decision tree and its importance in Machine learning

Lecture 57 Advantages and Disadvantages of Decision Tree

Section 18: Knowledge Check 8

Section 19: Machine Learning Algorithms Part 3

Lecture 58 Machine Learning Algorithms Part 3

Section 20: Knowledge Check 9

Section 21: Machine Learning Quiz 2

Section 22: Model Building Platform

Lecture 59 What is Integrated Development Environment

Lecture 60 Parts of Integrated Development Environment

Lecture 61 Why Developers Use Integrated Development Environment

Lecture 62 Which IDE is used for Machine Learning

Lecture 63 What are Open Source IDE

Lecture 64 What is Python

Lecture 65 Best IDE for Machine Learning along with Python

Lecture 66 Anaconda Distribution Platform and Jupyter IDE

Section 23: Knowledge Check 10

Section 24: Jupyter Notebook

Lecture 67 Three Important Tabs in Jupyter

Lecture 68 Creating new Folder and Notebook in Jupyter

Lecture 69 Creating Three Variables in Notebook

Lecture 70 How to Check Available Variables in Notebook

Lecture 71 How to Perform Addition operation and Close Jupyter Notebook

Lecture 72 How to Avoid Errors in Jupyter Notebook

Section 25: Knowledge Check 11

Section 26: Python Insights

Lecture 73 History of Python

Lecture 74 Applications of Python

Lecture 75 What is Variable-Fundamentals of Python

Lecture 76 Rules for Naming Variables in Python

Lecture 77 Types of Data in Python

Lecture 78 Operations in Python

Lecture 79 Arithmetic Operation in Python

Lecture 80 Assignment Operation in Python

Lecture 81 Comparison Operation in Python

Lecture 82 Logical Operations in Python

Lecture 83 Identity Operation in Python

Lecture 84 Membership Operation in Python

Lecture 85 Bitwise Operation in Python

Section 27: Knowledge Check 12

Section 28: Data Types in Python

Lecture 86 What is Variable

Lecture 87 Program to find out Data Types of Variables

Lecture 88 Boolean Data in Python

Lecture 89 Built-in Data in Python

Lecture 90 Lists-Built-in Data Type

Lecture 91 Tuples-Built-in Data Type

Lecture 92 Sets-Built-in Data Types

Lecture 93 Dictionaries-Built-in Data Types

Section 29: Knowledge Check 13

Section 30: Operators in Python

Lecture 94 Use of Operators in Python

Lecture 95 Arithmetic Operators

Lecture 96 Assignment Operator

Lecture 97 Comparison Operator

Lecture 98 Logical Operators

Lecture 99 Identity Operator

Lecture 100 Membership Operator

Lecture 101 Bitwise Operator

Lecture 102 You've Achieved 50% >> Let's Celebrate Your Progress And Keep Going To 75% >>

Section 31: Knowledge Check 14

Section 32: Control Statements in Python

Lecture 103 Types of Controlled Statements

Lecture 104 Use of IF Statement-Example 1

Lecture 105 Write a Program to find maximum number-Example 2

Lecture 106 How to Make code Efficient-Example 3

Lecture 107 Where to Use IF Statement

Section 33: Knowledge Check 15

Section 34: Libraries in Python

Lecture 108 What is Numpy and its use

Lecture 109 What is Scipy library

Lecture 110 What is Pandas Library

Lecture 111 What is Statsmodel and its features

Section 35: Knowledge Check 16

Section 36: NumPy Part 1

Lecture 112 What is an Array and its Example

Lecture 113 How to Access specific element of an Array

Lecture 114 Slicing Array

Lecture 115 How to know Number of Elements in Dimension of an array

Lecture 116 How to Join Two Arrays in a Single Array

Section 37: Knowledge Check 17

Section 38: NumPy Part 2

Lecture 117 Arithmetic Functions-Overview

Lecture 118 Add Method in Arithmetic Functions of Python

Lecture 119 Subtract,Multiply,Divide Methods in Arithmetic Functions

Lecture 120 MOD Method in Arithmetic Functions

Lecture 121 Remainder Method in Arithmetic Functions

Lecture 122 Power Method in Arithmetic Functions

Lecture 123 Reciprocal Method in Arithmetic Functions

Lecture 124 Creating two Dimensional Array for Arithmetic Functions

Lecture 125 Statistical Functions-overview

Lecture 126 Statistical Functions Implementation in Python

Lecture 127 Creation of Weighted Array

Lecture 128 Creating Two Dimensional Array with Statistical Functions

Section 39: Knowledge Check 18

Section 40: Pandas Part 1

Lecture 129 Import Libraries for Panda Project

Lecture 130 Create a Series from an Array

Lecture 131 Create Series from Dictionaries

Lecture 132 How to access elements from series

Lecture 133 Create a DataFrame Datastructure

Section 41: Knowledge Check 19

Section 42: Pandas Part 2

Lecture 134 Functions of pandas-pandas 2

Lecture 135 Pandas Attributes Example

Lecture 136 Head and Tail Method in Pandas

Lecture 137 Create a DataFrame Student including all Panda Functionality

Lecture 138 Descriptive Statistics Functions in Pandas

Section 43: Knowledge Check 20

Section 44: Pandas Part 3

Lecture 139 Create Jupyter Notebook and Load Dataset into it

Lecture 140 Loading Dataset into DataFrame

Lecture 141 How to find Missing Values in Dataset

Lecture 142 How to Handle Missing Values in Dataset

Section 45: Knowledge Check 21

Section 46: Data Visualisation & Scikit Learn

Lecture 143 What is Data Visualization

Lecture 144 Matplotib Library

Lecture 145 Seaborn Library

Lecture 146 Scikit-learn Library

Lecture 147 What is Dataset

Lecture 148 Components of Dataset

Section 47: Knowledge Check 22

Section 48: Matplotlib Part 1

Lecture 149 Overview of Matplotlib

Lecture 150 How to Create a Simple Plot

Lecture 151 How to Create a Graph with Multiple Points

Lecture 152 Marker on graphs

Lecture 153 Linestyle on graph

Lecture 154 How to Draw Multiple Lines on a Graph

Lecture 155 How to Draw Labels on plot

Lecture 156 How to create multiple Subplots on same canvas

Lecture 157 You've Achieved 75% >> Let's Celebrate Your Progress And Keep Going To 100% >>

Section 49: Knowledge Check 23

Section 50: Matplotlib Part 2

Lecture 158 Number of Plots in Matplotib-overview

Lecture 159 What is Bargraph and why we need it

Lecture 160 What is Histogram

Lecture 161 What is Scatter Plot and Why we need it

Lecture 162 What is Pie Chart

Section 51: Knowledge Check 24

Section 52: Python Coding - Seaborn Part 1

Lecture 163 What is Seaborn library

Lecture 164 How to import in-built datasets from seaborn

Lecture 165 Which datasets are available in seaborn

Lecture 166 Load Dataset from seaborn

Lecture 167 Themes and Styling in Seaborn

Lecture 168 How to change theme of plot

Lecture 169 Set context method in plot

Lecture 170 Color Pallets in plot

Section 53: Knowledge Check 25

Section 54: Python Coding - Seaborn Part 2

Lecture 171 Various Plots in Seaborn-lecture overview

Lecture 172 Relplot in Seaborn

Lecture 173 Catplot in Seaborn

Lecture 174 Ditplot in Seaborn

Lecture 175 Pairplot in Seaborn

Section 55: Knowledge Check 26

Section 56: Machine Learning Quiz 3

Section 57: Data Collection & Preparation

Lecture 176 What is Meant by Data Collection

Lecture 177 Understanding Data

Lecture 178 Exploratory Data Analysis

Lecture 179 Methods of Exploratory Data Analysis

Lecture 180 Data Pre-Processing

Lecture 181 Categorical Variables

Lecture 182 Data Pre-processing Techniques

Section 58: Knowledge Check 27

Section 59: Linear Regression - Use Case

Lecture 183 What is Linear Regression and its Use Case

Lecture 184 Dataset For Linear Regression

Lecture 185 Import library and Load Data set- steps of linear regression

Lecture 186 Remove the Index Column-Steps of Linear Regression

Lecture 187 Exploring Relationship between Predictors and Response

Lecture 188 Pairplot method explanation

Lecture 189 Corr and Heatmap method explanation

Lecture 190 Creating Simple Linear Regression Model

Lecture 191 Interpreting Model Coefficients

Lecture 192 Making Predictions with our Model

Lecture 193 Model Evaluation Metric

Section 60: Knowledge Check 28

Section 61: Linear Regression with Python

Lecture 194 Implementation of Linear Regression-lecture overview

Lecture 195 Uploading the Dataset in Jupyter Notebook

Lecture 196 Importing Libraries and Load Dataset into Dataframe

Lecture 197 Remove the Index Column

Lecture 198 Exploratory Analysis -relation of predictor and response

Lecture 199 Creation of Linear Regression Model

Lecture 200 Model Coefficients

Lecture 201 Making Predictions

Lecture 202 Evaluation of Model Performance

Section 62: Knowledge Check 29

Section 63: Model Evaluation Metrics

Lecture 203 Machine Learning Model Building

Lecture 204 What are Evaluation Metrics

Lecture 205 Different Kinds of Evaluation Metric

Lecture 206 Confusion Metric

Lecture 207 Accuracy

Lecture 208 Precision

Lecture 209 Recall

Lecture 210 What is F1 Score

Lecture 211 Classification Report

Section 64: Knowledge Check 30

Section 65: Logistic Regression - DIabetes Model

Lecture 212 Importing Libraries for Logistic Regression

Lecture 213 Load the dataset for logistic regression

Lecture 214 Creation of Logistics Regression Model

Lecture 215 You've Achieved 100% >> Let's Celebrate! Remember To Share Your Certificate!!

Section 66: Knowledge Check 31

Section 67: Machine Learning Quiz 4

Section 68: Additional Data Science Insights: Lessons From A Live Webinar Interview

Lecture 216 introduction of the guest speaker

Lecture 217 Perspective on other courses as one on data science and other courses

Lecture 218 Basic level of understanding about machines

Lecture 219 Pairing with physics and statistical major is good foundation for data science

Lecture 220 Having an overview on machine learning and the course

Lecture 221 Statistics on data science

Lecture 222 Learn how could data science be part on marketing

Lecture 223 Which do you find more comfortable for automation, Phython or UiPath

Lecture 224 Thoughts and overview on the Python course

Lecture 225 Can data science help predict the stock price?

Lecture 226 Can phyton be used to sort through the data

Lecture 227 How does statistics relate to data science and it is used in business

Lecture 228 Game theory that are involved, and its application to the field of data scienc

Lecture 229 Education and games thoughts on the course

Lecture 230 Full 1 Hour Live Data Science Webinar With Terence Govender from Regenesys

Section 69: Python Bootcamp - Introduction

Lecture 231 Introduction

Lecture 232 Download All Your Coding Files

Lecture 233 Introduce Yourself To Your Fellow Students And Tell Everyone What Are Your Goals

Lecture 234 Let's Celebrate Your Progress In This Course: 25% > 50% > 75% > 100%!!

Section 70: Introduction to Python

Lecture 235 Hello World Coding in Python

Lecture 236 Printing Variables in Python

Lecture 237 Strings, Floating Points, and Digits in Python

Lecture 238 Printing Variables in Python

Lecture 239 Inserting, Removing, and Pop Up of Variables in Python

Section 71: Date and Time in Python

Lecture 240 Printing Date and Time in Python

Lecture 241 Import and From Date Time in Python

Lecture 242 Printing Current Date Time in Python

Lecture 243 Printing Current Year in Python

Lecture 244 Hours, Minutes, and Seconds in Python

Lecture 245 Microseconds in Python

Lecture 246 Time stamp in Python

Lecture 247 Time Difference in Python

Lecture 248 Time Delta in Python

Lecture 249 Time Delta in Python 2

Lecture 250 Trigonometry in Python

Lecture 251 Now Date and Time in Python

Section 72: Sets, Trigonometry, Logarithmic in Python

Lecture 252 Intersection and Union of Sets in Python

Lecture 253 Difference of Sets in Python

Lecture 254 True and False in Sets Using Python

Lecture 255 Adding and Removing Elements in Sets

Lecture 256 Code for Intersection and Union in Python

Lecture 257 Element in Sets

Lecture 258 Math and CMath

Lecture 259 Logarithmic and Mod Operators

Lecture 260 You've Achieved 25% >> Let's Celebrate Your Progress And Keep Going To 50% >>

Lecture 261 Bitwise Operators in Python

Lecture 262 Binary into Decimals in Python

Lecture 263 Binary into Integers

Lecture 264 Multiple Variables in Python

Lecture 265 True and False Statement in Python

Section 73: Arrays in Python

Lecture 266 Arrays in Python

Lecture 267 Inserting Elements in Array

Lecture 268 Pop Up Arrays

Lecture 269 Index and Reverse Arrays

Lecture 270 Finding Error in Codes: Assignment

Section 74: Round off, Trigonometry, and Complex Numbers in Python

Lecture 271 Round off and Truncation

Lecture 272 Degrees into Radians and Radians into Degrees Using Python

Lecture 273 Positive and Negative Infinity in Python

Lecture 274 Not a Number Coding in Python

Lecture 275 Complex Numbers Coding in Python

Section 75: Strings in Python

Lecture 276 Printing Strings in Python

Lecture 277 Counting in Strings

Lecture 278 Open a File in Python

Lecture 279 Printing Multiple Strings in Python

Lecture 280 Strings True and False in Python

Lecture 281 Slicing and Indexing in Strings

Section 76: Strings, ord, chr, and Binary Numbers in Python

Lecture 282 Strings and Integers

Lecture 283 ord and chr Tools in Python

Lecture 284 Int and Binary Numbers in Python

Section 77: Lists and Dictionaries in Python

Lecture 285 Lists in Python

Lecture 286 Adding Strings in Lists

Lecture 287 Pop Up and Removing Strings in Lists

Lecture 288 Assignment Code

Lecture 289 Dictionaries in Python

Lecture 290 Lists and Dictionaries

Section 78: Tuples in Python

Lecture 291 Tuples in Python

Lecture 292 Lists into Tuples

Lecture 293 Why Lists and Tuples in Python

Lecture 294 Data File in Python

Section 79: Tuples and Sequences

Lecture 295 Assigning Tuples

Lecture 296 Strings

Lecture 297 Tuples into Lists

Lecture 298 Sequences in Python

Lecture 299 Multiple Sequences

Section 80: Loops, Sequences and List in Python

Lecture 300 Tuples into Loops

Lecture 301 Strings and Tuples

Lecture 302 Sequences into Loops

Lecture 303 List into Loop

Lecture 304 Item into Loop

Lecture 305 Appending Sequences

Section 81: Dictionaries and Comprehension in Python

Lecture 306 Range and List

Lecture 307 Dictionaries into Tuples

Lecture 308 Enumerator Functions

Lecture 309 List, Item and Iterators

Lecture 310 List Comprehension

Lecture 311 You've Achieved 50% >> Let's Celebrate Your Progress And Keep Going To 75% >>

Section 82: Mapping, Zip and Attributes in Python

Lecture 312 Mapping in Python

Lecture 313 Zip and Map Operator

Lecture 314 Printing Dictionaries Items

Lecture 315 dir Attributes

Lecture 316 dir Attributes 2

Section 83: Arguments and Functions in Python

Lecture 317 Arguments

Lecture 318 Sequences and Arguments

Lecture 319 Intersection of Sequences

Lecture 320 Defining Functions

Lecture 321 Multiple Functions

Section 84: Argument, Defining Functions, and def in Python

Lecture 322 Changer Function

Lecture 323 Argument Functions

Lecture 324 Multiple Arguments and Functions

Lecture 325 Knownly Type of a Function

Lecture 326 Printing Tuples Using Functions

Lecture 327 def Statement

Section 85: Argument, String Code, and Sum Tree

Lecture 328 Min and MAX of Argument

Lecture 329 Assignment

Lecture 330 String Code

Lecture 331 Finding Sum of List

Lecture 332 Sum Tree

Section 86: Echo and Lambda Function

Lecture 333 Echo Function

Lecture 334 Schedule Function

Lecture 335 Printing a Function Value

Lecture 336 Lambda Function

Lecture 337 Multiple Lambda Function

Lecture 338 Lambda Function with Multiple Functions

Section 87: Lambda and Generating Function

Lecture 339 Lambda Function: Code Example

Lecture 340 Lambda Function: Code Example

Lecture 341 Range and Tuples

Lecture 342 Matrices in Python

Lecture 343 Generating a Function in Python

Lecture 344 Generating a Function: Code Example

Lecture 345 Set of Codes

Section 88: def and Reducing Function in Python

Lecture 346 def of Sum and Square

Lecture 347 Reducing Code in Python

Lecture 348 Function Reducing Tool

Lecture 349 for and if in Range

Lecture 350 res.append in Python

Lecture 351 You've Achieved 75% >> Let's Celebrate Your Progress And Keep Going To 100% >>

Section 89: def Saver, ASCII, Exception, Encoding and Decoding in Python

Lecture 352 def Saver

Lecture 353 Python Module

Lecture 354 isinstance for String and Object

Lecture 355 def fetcher in Python

Lecture 356 Exception in Python

Lecture 357 ASII in Python

Lecture 358 encoding and decoding in Python

Lecture 359 encoding and decoding Lecture 2

Lecture 360 encoding and decoding Lecture 3

Lecture 361 encoding and decoding Lecture 4

Section 90: Get Attributes and Decorator in Python

Lecture 362 getName Coding

Lecture 363 GetAtrr in Python

Lecture 364 GetAtrribute in Python

Lecture 365 Decorator in Python

Lecture 366 Nested Decorator

Lecture 367 Annotation and Decorator

Lecture 368 functools for Decorator

Lecture 369 inspectfunc tool in Python

Section 91: Turtle, Pandas, Compilation, and Data Visualization

Lecture 370 Class Method in Python

Lecture 371 Turtle, Time and Random

Lecture 372 Pandas Library Code

Lecture 373 Compilation in Python

Lecture 374 Data Visualization in Matplotlib

Lecture 375 Scattering: Data Visualization

Lecture 376 Enumerator Function

Section 92: Logging, Data Visualization, and HTTP

Lecture 377 Plotly in MATPLOTLIB

Lecture 378 Plot: Data Visualization

Lecture 379 Logging and Exception

Lecture 380 Printing Vowels

Lecture 381 Map and Operator

Lecture 382 HTTP Server: Practical Python

Lecture 383 Socket Library for HTTP Server

Section 93: Make Calculator, Countdown Time, Size and Path of a File

Lecture 384 Tree Coding

Lecture 385 Tree Coding Lecture 2

Lecture 386 Name and Size of a File

Lecture 387 Countdown Time: Practical Python

Lecture 388 Make a Calculator: Practical Python

Section 94: PyAudio, DataFrame, More Pandas Library & Create a Leap Year

Lecture 389 Leap Year in Python

Lecture 390 PyAudio Lecture 1

Lecture 391 PyAudio Lecture 2

Lecture 392 Creating a Shelve in Python

Lecture 393 Pandas Library: DataFrame

Lecture 394 You've Achieved 100% >> Let's Celebrate! Remember To Share Your Certificate!!

Anyone interested in the field of Machine Learning and key concepts,People who want to understand ML and build models in Python,For those who have interest in Python,For those who want to build their career in programming languages like python