Python And Machine Learning For Complete Beginners

Become Part of the Artificial Intelligence Revolution

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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