Last updated 10/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 14.17 GB | Duration: 44h 46m
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
Homepage
https://www.udemy.com/course/machine-learning-in-python/
Download From 1DL
DOWNLOAD FROM 1DL.NET
DOWNLOAD FROM 1DL.NET
DOWNLOAD FROM 1DL.NET
DOWNLOAD FROM 1DL.NET
DOWNLOAD FROM 1DL.NET
DOWNLOAD FROM 1DL.NET
DOWNLOAD FROM 1DL.NET
DOWNLOAD FROM RAPIDGATOR.NET
DOWNLOAD FROM RAPIDGATOR.NET
DOWNLOAD FROM RAPIDGATOR.NET
DOWNLOAD FROM RAPIDGATOR.NET
DOWNLOAD FROM RAPIDGATOR.NET
DOWNLOAD FROM RAPIDGATOR.NET
DOWNLOAD FROM RAPIDGATOR.NET
DOWNLOAD FROM UPLOADGIG.COM
DOWNLOAD FROM UPLOADGIG.COM
DOWNLOAD FROM UPLOADGIG.COM
DOWNLOAD FROM UPLOADGIG.COM
DOWNLOAD FROM UPLOADGIG.COM
DOWNLOAD FROM UPLOADGIG.COM
DOWNLOAD FROM UPLOADGIG.COM
DOWNLOAD FROM NITROFLARE.COM
DOWNLOAD FROM NITROFLARE.COM
DOWNLOAD FROM NITROFLARE.COM
DOWNLOAD FROM NITROFLARE.COM
DOWNLOAD FROM NITROFLARE.COM
DOWNLOAD FROM NITROFLARE.COM
DOWNLOAD FROM NITROFLARE.COM