Srbija Posted October 7, 2023 Share #1 Posted October 7, 2023 2 In 1: Python Machine Learning Plus 30 Hour Python Bootcamp Last updated 10/2022 MP4 | Video: h264, 1280x720 | 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 Hidden Content Give reaction to this post to see the hidden content. Hidden Content Give reaction to this post to see the hidden content. Hidden Content Give reaction to this post to see the hidden content. Link to comment
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