Python And Machine Learning For Complete Beginners

Free Download Python And Machine Learning For Complete Beginners
Published 3/2023
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Language: English | Size: 28.61 GB | Duration: 43h 31m
Become Part of the Artificial Intelligence Revolution

Free Download What you’ll learn
Learn how to program in Python
Discover machine learning
Use artificial intelligence in your programs
Learn how to analyse data and make predictions
Requirements
Only basic computer knowledge needed
Basic algebra knowledge useful, but not required
Description
This course teaches you computer programming in Python from scratch, and also the basics of machine learning in Python.With this course you can become part of the Artificial Intelligence revolution.You’ll learn:How to write programs in PythonThe basics of desktop programming in PythonObject-oriented programming and functional programming techniquesHow to use machine learning techniques in your codeThe basics of visualising and analysing dataNumpy, Pandas, Matplotlib, scikit-learn, Keras and morePowerful prediction and classification techniques "naive Bayes" and decision trees.How to use ML techniques to make predictions about data series, spot clusters in data, automatically classify data samples and recognise handwritten digits.Whether you’re a complete beginner with coding or already know some Python or another language, this course can help give you modern computer skills to the point where you could apply for Python jobs, where available.Python is one of the most popular programming languages today and is especially popular because of its support for machine learning and artificial intelligence.This courses takes you all the way from writing your first "hello world" Python program to being able to write complex programs incorporating artificial intelligence techniques in which your software can automatically learn how to complete tasks.I’ll type all code right in front of you and explain how it works, breaking down programming and mathematical concepts into simple steps, and with suggested exercises throughout.
Overview
Section 1: Getting Started
Lecture 1 Introduction
Lecture 2 How to Use This Course
Lecture 3 Installing Python
Lecture 4 Installing Powershell
Lecture 5 Python Virtual Environments
Lecture 6 Visual Studio Code: A Free Lightweight Editor
Lecture 7 Hello World
Lecture 8 The Shebang or Hashbang
Lecture 9 Where to Find the Source Code
Lecture 10 Visual Studio Code Tips
Lecture 11 Variables
Lecture 12 An Interactive Program
Lecture 13 Builtin Functions
Lecture 14 Numeric Variables
Lecture 15 Numeric Expressions
Lecture 16 Python Types
Lecture 17 Performing Calculations
Lecture 18 Converting Temperatures
Section 2: Loops and Conditions
Lecture 19 A Program Inspired by "WarGames"
Lecture 20 Boolean Variables
Lecture 21 The "If" Statement
Lecture 22 If Else
Lecture 23 Constants
Lecture 24 If-Else-If
Lecture 25 Comparison Operators
Lecture 26 Fridge Exercise
Lecture 27 Solving the Fridge Exercise
Lecture 28 Improving the Fridge Solution
Lecture 29 "For" Loops
Lecture 30 Ranges
Lecture 31 Indentation
Lecture 32 The "Break" Keyword
Lecture 33 The "Continue" Keyword
Lecture 34 A Password Exercise
Lecture 35 A Solution to the Password Exercise
Lecture 36 Boolean Operators
Lecture 37 Boolean Operators Exercise
Lecture 38 A Solution to the Boolean Operators Exercise
Lecture 39 Another Solutiion to the Boolean Operators Exercise
Lecture 40 "While" Loops
Section 3: Structure Code with Functions
Lecture 41 Your First Function
Lecture 42 Multiple Functions
Lecture 43 Function Arguments
Lecture 44 The "id" Function
Lecture 45 Changing Parameter Variables
Lecture 46 Return Values
Lecture 47 Passing Multiple Arguments
Lecture 48 Calculating Factorials Exercise
Lecture 49 A Solution to the Factorial Exercise
Lecture 50 Default Arguments
Lecture 51 Keyword Arguments
Lecture 52 Variable Length Arguments
Lecture 53 Variable Length Keyword Arguments
Lecture 54 Arguments and Parameters Summary
Lecture 55 A Solution to the Arguments Exercise
Lecture 56 Multiple Return Values
Lecture 57 A Solution to the BMI Exercise
Section 4: Containers: Lists, Tuples, Sets and Dictionaries
Lecture 58 Tuples
Lecture 59 Packing and Unpacking Tuples
Lecture 60 Tuple Slicing
Lecture 61 Tuple Functions and Operators
Lecture 62 Lists
Lecture 63 Joining Lists
Lecture 64 Modifying Lists
Lecture 65 Extended Slicing
Lecture 66 Extending and Inserting Into Lists
Lecture 67 Removing List Items
Lecture 68 List Comprehensions: Flexibly Creating Lists
Lecture 69 List Comprehension Conditions
Lecture 70 List Comprehension "if-else"
Lecture 71 List Database Exercise
Lecture 72 List Exercise Tips
Lecture 73 Structuring a Solution to the List Exercise
Lecture 74 Completing the List Exercise Solution
Lecture 75 About Data Validation
Lecture 76 Sets: Collections of Unique Objects
Lecture 77 Adding to Sets and Updating Sets
Lecture 78 Removing Items from Sets
Lecture 79 The Union and Intersection of Sets
Lecture 80 Difference Updates
Lecture 81 A Set Exercise
Lecture 82 A Solution to the Set Exercise
Lecture 83 Python Dictionaries
Lecture 84 Adding Items to Dictionaries
Lecture 85 Iterating Over Dictionaries
Lecture 86 Dictionary Views
Lecture 87 Deleting Dictionary Items
Lecture 88 The Dictionary "Get" Method
Lecture 89 Default Dictionaries
Lecture 90 Dictionary Comprehensions
Lecture 91 A Dictionary Exercise
Lecture 92 A Solution to the Dictionary Exercise
Lecture 93 Casefolding and "None"
Lecture 94 Enumerating and Zipping
Lecture 95 Improving the Dictionary Exercise Solution
Lecture 96 Hashing Algorithms
Lecture 97 Containers Summary
Lecture 98 Time Complexity and Big O
Lecture 99 Lists of Lists
Lecture 100 Iterating Over Lists of Lists
Lecture 101 Dictionaries of Lists
Lecture 102 A Dictionaries of Sets Exercise
Lecture 103 The First Part of A Solution to the Dictionaries of Sets Exercise
Lecture 104 The Second Part of the Solution to the Dictionaries of Sets Exercise
Lecture 105 Global Variables
Lecture 106 Selecting Items at Random
Lecture 107 Modular Arithmetic and the Modulus Operator
Lecture 108 An Exercise Using Multiple Containers
Lecture 109 The First Part of a Solution to the Containers Exercise
Lecture 110 The Second Part of the Solution to the Containers Exercise
Section 5: Formatting Strings
Lecture 111 A Review of Strings
Lecture 112 Formatting Strings
Lecture 113 The Format Method
Lecture 114 F-Strings
Lecture 115 Raw Strings
Section 6: Regular Expressions
Lecture 116 A Simple Regular Expression
Lecture 117 Matching Multiple Characters
Lecture 118 The Ternary Operator
Lecture 119 Greedy Matching
Lecture 120 Matching Numbers and Words
Lecture 121 Capture Groups
Lecture 122 Matching Specific Numbers of Characters
Lecture 123 Character Classes
Lecture 124 A Solution to the Email Address-Matching Exercise
Lecture 125 Using "Not" in Character Classes
Lecture 126 Escaping Regexes
Lecture 127 Comments and Space in Regular Expressions
Lecture 128 Referring to Capture Groups in Regexes
Lecture 129 Capture Groups and Non-Capture Groups
Lecture 130 Matching Newlines
Lecture 131 Matching Ends of Lines
Lecture 132 The "Search" Function
Lecture 133 The "Findall" Function
Lecture 134 Matching Starts of Lines
Lecture 135 Splitting Strings
Lecture 136 Replacing Text
Lecture 137 Alternatives in Regexes
Lecture 138 A "Budget" Exercise
Lecture 139 The First Part of a Solution to the Budget Exercise
Lecture 140 The Second Part of the Solution to the Budget Exercise
Lecture 141 Ignoring Case in Regular Expressions
Lecture 142 Compiling Regular Expressions
Lecture 143 Zero-Width Lookahead Assertions
Lecture 144 Some More Useful Regex Sequences
Lecture 145 Summary of Regular Expressions
Section 7: Handling Errors
Lecture 146 Tracebacks
Lecture 147 Try-Except
Lecture 148 Catching Specific Errors
Lecture 149 Error Messages
Lecture 150 Raising Errors
Lecture 151 The KeyboardInterrupt Error
Lecture 152 The Finally Clause
Lecture 153 An Exercise with Errors
Lecture 154 A Solution to the Errors Exercise
Lecture 155 An Exercise on Calculating Pi
Lecture 156 A Solution to the Pi Exercise
Lecture 157 Using Assertions
Section 8: Object-Oriented Programming
Lecture 158 Classes
Lecture 159 Constructors
Lecture 160 The Mysterious ‘Self’ Variable
Lecture 161 Object Properties
Lecture 162 Creating String Representations of Objects
Lecture 163 Encapsulation
Lecture 164 An Object-Oriented Word Game
Lecture 165 Choosing Words
Lecture 166 Guessing Letters
Lecture 167 Displaying Letters
Lecture 168 Completing the Word Game
Lecture 169 Getters and Setters
Lecture 170 Inheritance
Lecture 171 Overriding Methods
Lecture 172 Polymorphism
Lecture 173 Super Constructors
Lecture 174 Class Properties
Lecture 175 Automatically Assigning IDs to Objects
Lecture 176 Class Methods
Lecture 177 Object and Classes
Lecture 178 An Exercise in Object Orientation
Lecture 179 First Part of a Solution to the Object Orientation Exercise
Lecture 180 Second Part of the Solution to the Object Orientation Exercise
Lecture 181 Third Part of the Solution to the Object Orientation Exercise
Lecture 182 Class Hierarchies
Lecture 183 Multiple Inheritance
Lecture 184 The Diamond Problem
Lecture 185 Mixins
Lecture 186 The Property Class
Section 9: Conway’s Game of Life
Lecture 187 Introducing Conway’s Game of Life
Lecture 188 A Basic GUI App
Lecture 189 Using Frames
Lecture 190 Refactoring Into an "OO" Structure
Lecture 191 Laying Out Widgets with Grids
Lecture 192 A Canvas Class
Lecture 193 Getting Widget Sizes
Lecture 194 Drawing Cells
Lecture 195 A Cell Class
Lecture 196 Toggling Cell States
Lecture 197 Handling Button Clicks
Lecture 198 Selecting Neighbouring Cells
Lecture 199 Wrapping Cell Selection
Lecture 200 The Game of Life Rules
Lecture 201 Implementing the Game of Life Rules
Lecture 202 Clearing the Grid
Lecture 203 Randomising Cell Selection
Section 10: Modules: Packaging Code
Lecture 204 A Basic Module
Lecture 205 Conditionally Running ‘Main’
Lecture 206 Importing Parts of Modules
Lecture 207 Packages
Lecture 208 A Games Package
Lecture 209 Using Functions in Dictionaries
Lecture 210 A Solution to the Games Menu Exercise
Lecture 211 Package Initialisation
Lecture 212 How Python Locates Modules
Lecture 213 Inspecting Modules
Lecture 214 Subpackages
Lecture 215 Package Attributes
Lecture 216 Referencing Parallel Modules
Lecture 217 Installing Modules
Section 11: Operators
Lecture 218 A Clock Class Exercise
Lecture 219 A Solution to the Clock Exercise
Lecture 220 Implementing ‘Add’
Lecture 221 Implementing Unary Operators
Lecture 222 Flags
Lecture 223 Bitwise ‘Or’
Lecture 224 Bitwise Flags
Lecture 225 Bitwise ‘And’
Lecture 226 A Flags Exercise
Lecture 227 A Solution to the Flags Exercise
Lecture 228 Bitwise ‘xor’ and ‘not’
Lecture 229 Bit Shift Operators
Lecture 230 Hexadecimal Numbers
Lecture 231 A Solution to the Hexadecimal Colours Exercise
Section 12: Functional Programming
Lecture 232 Introducing Functional Programming
Lecture 233 Recursion
Lecture 234 Passing Functions to Functions
Lecture 235 Iterators
Lecture 236 Powers of Two Iterator
Lecture 237 Mapping
Lecture 238 Lambda Functions
Lecture 239 Defining Functions in Loops
Lecture 240 Lambda Exercise Solution
Lecture 241 Sorting
Lecture 242 "Next" and "Iter"
Lecture 243 Generating Characters
Lecture 244 Generators
Lecture 245 An Exercise with Generators
Lecture 246 Generators Exercise Solution
Lecture 247 General Generators Syntax
Lecture 248 Generators as Loops
Lecture 249 Game of Life Exercise Solution
Lecture 250 The Itertools Module
Lecture 251 Function Generators
Lecture 252 Powers of Two Generator Solution
Lecture 253 Filtering
Lecture 254 Reducing
Lecture 255 A Functional Word Exercise
Lecture 256 Solution to the Word Exercise
Lecture 257 A Functional Parsing Exercise
Lecture 258 Solution to the Functional Parsing Exercise
Section 13: Reading and Writing Files
Lecture 259 The Mall Customers Database
Lecture 260 Reading Files
Lecture 261 Ensuring Files Are Closed
Lecture 262 Examining "With"
Lecture 263 Iterating Over Files
Lecture 264 Writing Files
Lecture 265 Files Exercise Solution
Lecture 266 Appending to Files
Lecture 267 Handling Binary Text Data
Lecture 268 Binary Files
Lecture 269 Serialization
Lecture 270 Serializing Integers
Lecture 271 Deserializing Integers
Lecture 272 Saving and Loading Integers
Lecture 273 Numbers Versus Bytes
Lecture 274 Python Arrays
Lecture 275 Saving Arrays
Lecture 276 Pickling
Lecture 277 JSON
Lecture 278 File Dialogs
Lecture 279 Game of Life Menus
Lecture 280 Game of Life Save and Load
Lecture 281 Testing the Game of Life Updates
Lecture 282 The OS Module
Lecture 283 A Word Count Exercise
Lecture 284 Splitting Text Into Words
Lecture 285 Counting Words
Section 14: Numpy: Numerical Python
Lecture 286 Numpy Arrays
Lecture 287 Creating Numpy Arrays
Lecture 288 Numpy Arithmetic
Lecture 289 Numpy Slicing
Lecture 290 2D Indexing
Lecture 291 Numpy Views
Lecture 292 Advanced Indexing
Lecture 293 Matrices
Lecture 294 Matrix Multiplication
Lecture 295 Numpy Functions
Lecture 296 An Exercise with Numpy
Lecture 297 Numpy Exercise Solution First Part
Lecture 298 Numpy Exercise Solution Second Part
Lecture 299 Tiling
Lecture 300 Masks
Lecture 301 Combining Boolean Arrays
Lecture 302 Filtering Numpy Arrays
Lecture 303 Variance and Standard Deviation
Lecture 304 Variance Exercise
Lecture 305 Bessel’s Correction
Lecture 306 Scaling and Variance
Lecture 307 Loading CSV in Numpy
Section 15: Graphs and Plotting
Lecture 308 Pyplot Basics
Lecture 309 Styles
Lecture 310 Configuring Matplotlib
Lecture 311 More Config Options
Lecture 312 A Word Length Exercise
Lecture 313 Word Length Plot Solution First Part
Lecture 314 Word Length Plot Solution Second Part
Lecture 315 Creating Bar Charts
Lecture 316 Creating Pie Charts
Lecture 317 Pie Chart Exercise Solution
Lecture 318 Scatter Plots
Lecture 319 Histograms
Lecture 320 Multiple Graphs in One Plot
Lecture 321 Subplots
Lecture 322 Subplots Exercise Solution
Lecture 323 3D Plots
Section 16: Pandas: Python’s Equivalent of Spreadsheets
Lecture 324 Introduction
Lecture 325 Referencing Cells
Lecture 326 Loc and Iloc
Lecture 327 Changing Values in Pandas
Lecture 328 Pandas Functions
Lecture 329 Pandas Series
Lecture 330 Matplot and Pandas
Lecture 331 Sorting in Pandas
Lecture 332 Correlations
Lecture 333 Grouping
Lecture 334 Grouped Types
Lecture 335 Group Aggregate Functions
Lecture 336 Filtering
Lecture 337 Multiple Groups
Lecture 338 Plotting Groups
Lecture 339 Binning
Lecture 340 A Groupby Exercise
Lecture 341 Groupby Exercise Solution First Part
Lecture 342 Groupby Exercise Solution Second Part
Lecture 343 Zipf’s Law Exercise
Lecture 344 Zipf’s Law Exercise Solution
Section 17: Regression: Fitting and Predicting Curves
Lecture 345 Introduction to Regression
Lecture 346 Linear Regression Data
Lecture 347 Configuring Tick Labels
Lecture 348 The Equation of a Line
Lecture 349 Linear Regression with Statsmodels
Lecture 350 Why Add Constants
Lecture 351 R Squared
Lecture 352 Calculating R Squared
Lecture 353 Train-Test Split
Lecture 354 Predictions with Linear Regression
Lecture 355 Linear Regression Exercise
Lecture 356 Plotting Grapes Exercise Solution
Lecture 357 Predicting the Weights of Grapes
Lecture 358 Removing Outliers
Lecture 359 Multiple Linear Regression
Lecture 360 A Multiple Linear Regression Model with Scikit-Learn
Lecture 361 About Polynomial Regression
Lecture 362 Polynomial Features
Lecture 363 A Polynomial Regression Model
Lecture 364 A Surprising Result
Lecture 365 Binomial Logistic Regression and Causation
Lecture 366 Categorical Dummy Values
Lecture 367 The Logistic Equation
Lecture 368 A Scikit-Learn Logistic Regression Model
Lecture 369 Multiple Logistic Regression
Lecture 370 Getting Predictions with Logistic Regression
Lecture 371 Confusion Matrices
Lecture 372 Scaling and Normalisation
Lecture 373 Normalising Split Data
Lecture 374 Using StandardScaler
Lecture 375 A Confusion Matrix Exercise
Lecture 376 Confusion Matrix Exercise Solution, First Part
Lecture 377 Confusion Matrix Exercise Solution, Second Part
Section 18: Clustering: Analysing Clustered Data
Lecture 378 Introducing Clustering
Lecture 379 K-Means Clustering
Lecture 380 Centroids and Inertia
Lecture 381 The Elbow Method
Lecture 382 K-Means Exercise Solution
Lecture 383 Exercise Further Analysis
Lecture 384 The Iris Flower Dataset
Lecture 385 Loading the Iris Flower Dataset
Lecture 386 Seaborn Plots
Lecture 387 K-Means Iris Exercise
Lecture 388 Iris Exercise Solution
Lecture 389 Permutations Exercise
Lecture 390 Permutations Exercise Solution
Lecture 391 Normalized Mutual Information
Lecture 392 Dendrograms
Lecture 393 The Linkage Table
Lecture 394 Clustering Iris Flower Data
Lecture 395 Scikit-Learn Agglomerative Clustering
Lecture 396 Linkage and Affinity
Lecture 397 Fit, Predict, Transform
Lecture 398 Nearest Neighbors
Lecture 399 Spherically Symmmetric Data
Lecture 400 DBSCAN
Lecture 401 Determining Epsilon
Lecture 402 Using DBSCAN
Lecture 403 DBSCAN Moons Exercise
Lecture 404 DBSCAN Moons Exercise Solution
Lecture 405 Silhouette Scores
Lecture 406 Nearest Neighbors Classification
Lecture 407 Using KNeighborsClassifier
Section 19: Naive Bayes: Making Predictions on the Basis of Probabilities
Lecture 408 Bayes’ Theorem
Lecture 409 Naive Bayes
Lecture 410 Applying Bayes to Classification
Lecture 411 An Email Dataset
Lecture 412 Loading the Email Dataset
Lecture 413 Counting Words in Emails
Lecture 414 Listing Common Words
Lecture 415 The Predictor Matrix
Lecture 416 Naive Bayes Classifiers
Lecture 417 Naive Bayes Exercise
Lecture 418 Naive Bayes Exercise Solution
Lecture 419 Classifying Irises with Naive Bayes
Section 20: Decision Trees
Lecture 420 Introducing Decision Trees
Lecture 421 Gini Impurity
Lecture 422 Calculating Gini Impurity
Lecture 423 Gini Impurity Examples
Lecture 424 Decision Tree Exercise
Lecture 425 A Solution to the Decision Tree Exercise
Lecture 426 Seaborn Iris Plots
Lecture 427 Plotting Decision Trees
Section 21: Principal Component Analysis
Lecture 428 Introducing PCA
Lecture 429 Data for PCA
Lecture 430 How PCA Works
Lecture 431 Transforming Data with PCA
Lecture 432 Explained Variance Ratios
Lecture 433 Iris Flower PCA Analysis
Lecture 434 PCA Components
Lecture 435 Classifying Irises with PCA
Lecture 436 PCA Tips
Lecture 437 PCA Exercise
Lecture 438 A Solution to the PCA Exercise
Lecture 439 The MNIST Dataset
Lecture 440 Fetching MNIST From OpenML
Lecture 441 Loading MNIST with Keras
Lecture 442 Character Recognition
Lecture 443 Configuring Logistic Regression
Lecture 444 Displaying Images
Section 22: Artificial Neural Networks (ANNs)
Lecture 445 An Artificial Neuron
Lecture 446 Activation Functions
Lecture 447 Minimising Loss
Lecture 448 Preparing Iris Data
Lecture 449 A Basic ANN
Lecture 450 Dropout, and Tweaking the Network
Lecture 451 A Neural Net Character Recognition Exercise
Lecture 452 Preparing the MNIST Data
Lecture 453 An ANN for Recognising Digits
Lecture 454 Improving the ANN
Lecture 455 Comparing Subarrays
Lecture 456 Displaying Misclassified Images
Lecture 457 Saving and Loading ANNs
Lecture 458 Machine Learning Pipelines
Lecture 459 A Standalone Pretrained Classifier
Lecture 460 The California Housing Dataset
Lecture 461 Regression with Neural Networks
Lecture 462 Improving ANN Regression
Lecture 463 Analysing the Results
Lecture 464 Detecting Overfitting
Section 23: Conclusion
Lecture 465 Conclusion
Complete beginners with computer programming,Existing programmers who want to improve their Python knowledge or learn Python,Python programmers who want to learn how to use AI/ML in their programs.

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