Complete 2022 Data Science Machine Learning Bootcamp

Complete 2022 Data Science Machine Learning Bootcamp

What you’ll learn

You will learn how to program using Python through practical projects
Use data science algorithms to analyse data in real life projects such as spam classification and image recognition
Build a portfolio of data science projects to apply for jobs in the industry
Understand how to use the latest tools in data science, including Tensorflow, Matplotlib, Numpy and many more
Create your own neural networks and understand how to use them to perform deep learning
Understand and apply data visualisation techniques to explore large datasets

Requirements

No programming experience needed! I’ll teach you everything you need to know.
No statistics knowledge required! I’ll teach you everything you need to know.
No calculus knowledge required! as long as you’ve done some high school maths, I’ll take you step by step through the difficult parts.
Also, no paid software required – all projects use free and open source software
All you need is Mac or PC computer with access to the internet

Description

Welcome to the Complete Data Science and Machine Learning Bootcamp, the only course you need to learn Python and get into data science.At over 40+ hours, this Python course is without a doubt the most comprehensive data science and machine learning course available online. Even if you have zero programming experience, this course will take you from beginner to mastery. Here’s why:The course is taught by the lead instructor at the App Brewery, London’s leading in-person programming bootcamp.In the course, you’ll be learning the latest tools and technologies that are used by data scientists at Google, Amazon, or Netflix.This course doesn’t cut any corners, there are beautiful animated explanation videos and real-world projects to build.The curriculum was developed over a period of three years together with industry professionals, researchers and student testing and feedback.To date, we’ve taught over 200,000 students how to code and many have gone on to change their lives by getting jobs in the industry or starting their own tech startup.You’ll save yourself over $12,000 by enrolling, but get access to the same teaching materials and learn from the same instructor and curriculum as our in-person programming bootcamp.We’ll take you step-by-step through video tutorials and teach you everything you need to know to succeed as a data scientist and machine learning professional.The course includes over 40+ hours of HD video tutorials and builds your programming knowledge while solving real-world problems.In the curriculum, we cover a large number of important data science and machine learning topics, such as:Data Cleaning and Pre-ProcessingData Exploration and VisualisationLinear RegressionMultivariable RegressionOptimisation Algorithms and Gradient DescentNaive Bayes ClassificationDescriptive Statistics and Probability TheoryNeural Networks and Deep LearningModel Evaluation and AnalysisServing a Tensorflow ModelThroughout the course, we cover all the tools used by data scientists and machine learning experts, including:Python 3TensorflowPandasNumpyScikit LearnKerasMatplotlibSeabornSciPySymPyBy the end of this course, you will be fluently programming in Python and be ready to tackle any data science project. We’ll be covering all of these Python programming concepts:Data Types and VariablesString ManipulationFunctionsObjectsLists, Tuples and DictionariesLoops and IteratorsConditionals and Control FlowGenerator FunctionsContext Managers and Name ScopingError HandlingBy working through real-world projects you get to understand the entire workflow of a data scientist which is incredibly valuable to a potential employer.Sign up today, and look forward to:178+ HD Video Lectures30+ Code Challenges and ExercisesFully Fledged Data Science and Machine Learning ProjectsProgramming Resources and CheatsheetsOur best selling 12 Rules to Learn to Code eBook$12,000+ data science & machine learning bootcamp course materials and curriculumDon’t just take my word for it, check out what existing students have to say about my courses:“One of the best courses I have taken. Everything is explained well, concepts are not glossed over. There is reinforcement in the challenges that helps solidify understanding. I’m only half way through but I feel like it is some of the best money I’ve ever spent.” -Robert Vance“I’ve spent £27,000 on University….. Save some money and buy any course available by Philipp! Great stuff guys.” -Terry Woodward”This course is amazingly immersive and quite all-inclusive from end-to-end to develop an app! Also gives practicality to apply the lesson straight away and full of fun with bunch of sense of humor, so it’s not boring to follow throughout the whole course. Keep up the good work guys!” – Marvin Septianus“Great going so far. Like the idea of the quizzes to challenge us as we go along. Explanations are clear and easy to follow” -Lenox James“Very good explained course. The tasks and challenges are fun to do learn an do! Would recommend it a thousand times.” -Andres Ariza“I enjoy the step by step method they introduce the topics. Anyone with an interest in programming would be able to follow and program” -Isaac Barnor“I am learning so much with this course; certainly beats reading older Android Ebooks that are so far out of date; Phillippe is so easy any understandable to learn from. Great Course have recommended to a few people.” -Dale Barnes“This course has been amazing. Thanks for all the info. I’ll definitely try to put this in use. :)” -Devanshika Ghosh“Great Narration and explanations. Very interactive lectures which make me keep looking forward to the next tutorial” -Bimal Becks“English is not my native language but in this video, Phillip has great pronunciation so I don’t have problem even without subtitles :)” -Dreamerx85“Clear, precise and easy to follow instructions & explanations!” -Andreea Andrei“An incredible course in a succinct, well-thought-out, easy to understand package. I wish I had purchased this course first.” -IanREMEMBER… I’m so confident that you’ll love this course that we’re offering a FULL money back guarantee for 30 days! So it’s a complete no-brainer, sign up today with ZERO risks and EVERYTHING to gain.So what are you waiting for? Click the buy now button and join the world’s best data science and machine learning course.

Overview

Section 1: Introduction to the Course

Lecture 1 What is Machine Learning?

Lecture 2 What is Data Science?

Lecture 3 Download the Syllabus

Lecture 4 Top Tips for Succeeding on this Course

Lecture 5 Course Resources List

Section 2: Predict Movie Box Office Revenue with Linear Regression

Lecture 6 Introduction to Linear Regression & Specifying the Problem

Lecture 7 Gather & Clean the Data

Lecture 8 Explore & Visualise the Data with Python

Lecture 9 The Intuition behind the Linear Regression Model

Lecture 10 Analyse and Evaluate the Results

Lecture 11 Download the Complete Notebook Here

Lecture 12 Join the Student Community

Lecture 13 Any Feedback on this Section?

Section 3: Python Programming for Data Science and Machine Learning

Lecture 14 Windows Users – Install Anaconda

Lecture 15 Mac Users – Install Anaconda

Lecture 16 Does LSD Make You Better at Maths?

Lecture 17 Download the 12 Rules to Learn to Code

Lecture 18 [Python] – Variables and Types

Lecture 19 [Python] – Lists and Arrays

Lecture 20 [Python & Pandas] – Dataframes and Series

Lecture 21 [Python] – Module Imports

Lecture 22 [Python] – Functions – Part 1: Defining and Calling Functions

Lecture 23 [Python] – Functions – Part 2: Arguments & Parameters

Lecture 24 [Python] – Functions – Part 3: Results & Return Values

Lecture 25 [Python] – Objects – Understanding Attributes and Methods

Lecture 26 How to Make Sense of Python Documentation for Data Visualisation

Lecture 27 Working with Python Objects to Analyse Data

Lecture 28 [Python] – Tips, Code Style and Naming Conventions

Lecture 29 Download the Complete Notebook Here

Lecture 30 Any Feedback on this Section?

Section 4: Introduction to Optimisation and the Gradient Descent Algorithm

Lecture 31 What’s Coming Up?

Lecture 32 How a Machine Learns

Lecture 33 Introduction to Cost Functions

Lecture 34 LaTeX Markdown and Generating Data with Numpy

Lecture 35 Understanding the Power Rule & Creating Charts with Subplots

Lecture 36 [Python] – Loops and the Gradient Descent Algorithm

Lecture 37 [Python] – Advanced Functions and the Pitfalls of Optimisation (Part 1)

Lecture 38 [Python] – Tuples and the Pitfalls of Optimisation (Part 2)

Lecture 39 Understanding the Learning Rate

Lecture 40 How to Create 3-Dimensional Charts

Lecture 41 Understanding Partial Derivatives and How to use SymPy

Lecture 42 Implementing Batch Gradient Descent with SymPy

Lecture 43 [Python] – Loops and Performance Considerations

Lecture 44 Reshaping and Slicing N-Dimensional Arrays

Lecture 45 Concatenating Numpy Arrays

Lecture 46 Introduction to the Mean Squared Error (MSE)

Lecture 47 Transposing and Reshaping Arrays

Lecture 48 Implementing a MSE Cost Function

Lecture 49 Understanding Nested Loops and Plotting the MSE Function (Part 1)

Lecture 50 Plotting the Mean Squared Error (MSE) on a Surface (Part 2)

Lecture 51 Running Gradient Descent with a MSE Cost Function

Lecture 52 Visualising the Optimisation on a 3D Surface

Lecture 53 Download the Complete Notebook Here

Lecture 54 Any Feedback on this Section?

Section 5: Predict House Prices with Multivariable Linear Regression

Lecture 55 Defining the Problem

Lecture 56 Gathering the Boston House Price Data

Lecture 57 Clean and Explore the Data (Part 1): Understand the Nature of the Dataset

Lecture 58 Clean and Explore the Data (Part 2): Find Missing Values

Lecture 59 Visualising Data (Part 1): Historams, Distributions & Outliers

Lecture 60 Visualising Data (Part 2): Seaborn and Probability Density Functions

Lecture 61 Working with Index Data, Pandas Series, and Dummy Variables

Lecture 62 Understanding Descriptive Statistics: the Mean vs the Median

Lecture 63 Introduction to Correlation: Understanding Strength & Direction

Lecture 64 Calculating Correlations and the Problem posed by Multicollinearity

Lecture 65 Visualising Correlations with a Heatmap

Lecture 66 Techniques to Style Scatter Plots

Lecture 67 A Note for the Next Lesson

Lecture 68 Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques

Lecture 69 Understanding Multivariable Regression

Lecture 70 How to Shuffle and Split Training & Testing Data

Lecture 71 Running a Multivariable Regression

Lecture 72 How to Calculate the Model Fit with R-Squared

Lecture 73 Introduction to Model Evaluation

Lecture 74 Improving the Model by Transforming the Data

Lecture 75 How to Interpret Coefficients using p-Values and Statistical Significance

Lecture 76 Understanding VIF & Testing for Multicollinearity

Lecture 77 Model Simplification & Baysian Information Criterion

Lecture 78 How to Analyse and Plot Regression Residuals

Lecture 79 Residual Analysis (Part 1): Predicted vs Actual Values

Lecture 80 Residual Analysis (Part 2): Graphing and Comparing Regression Residuals

Lecture 81 Making Predictions (Part 1): MSE & R-Squared

Lecture 82 Making Predictions (Part 2): Standard Deviation, RMSE, and Prediction Intervals

Lecture 83 Build a Valuation Tool (Part 1): Working with Pandas Series & Numpy ndarrays

Lecture 84 [Python] – Conditional Statements – Build a Valuation Tool (Part 2)

Lecture 85 Build a Valuation Tool (Part 3): Docstrings & Creating your own Python Module

Lecture 86 Download the Complete Notebook Here

Lecture 87 Any Feedback on this Section?

Section 6: Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails: Part 1

Lecture 88 How to Translate a Business Problem into a Machine Learning Problem

Lecture 89 Gathering Email Data and Working with Archives & Text Editors

Lecture 90 How to Add the Lesson Resources to the Project

Lecture 91 The Naive Bayes Algorithm and the Decision Boundary for a Classifier

Lecture 92 Basic Probability

Lecture 93 Joint & Conditional Probability

Lecture 94 Bayes Theorem

Lecture 95 Reading Files (Part 1): Absolute Paths and Relative Paths

Lecture 96 Reading Files (Part 2): Stream Objects and Email Structure

Lecture 97 Extracting the Text in the Email Body

Lecture 98 [Python] – Generator Functions & the yield Keyword

Lecture 99 Create a Pandas DataFrame of Email Bodies

Lecture 100 Cleaning Data (Part 1): Check for Empty Emails & Null Entries

Lecture 101 Cleaning Data (Part 2): Working with a DataFrame Index

Lecture 102 Saving a JSON File with Pandas

Lecture 103 Data Visualisation (Part 1): Pie Charts

Lecture 104 Data Visualisation (Part 2): Donut Charts

Lecture 105 Introduction to Natural Language Processing (NLP)

Lecture 106 Tokenizing, Removing Stop Words and the Python Set Data Structure

Lecture 107 Word Stemming & Removing Punctuation

Lecture 108 Removing HTML tags with BeautifulSoup

Lecture 109 Creating a Function for Text Processing

Lecture 110 A Note for the Next Lesson

Lecture 111 Advanced Subsetting on DataFrames: the apply() Function

Lecture 112 [Python] – Logical Operators to Create Subsets and Indices

Lecture 113 Word Clouds & How to install Additional Python Packages

Lecture 114 Creating your First Word Cloud

Lecture 115 Styling the Word Cloud with a Mask

Lecture 116 Solving the Hamlet Challenge

Lecture 117 Styling Word Clouds with Custom Fonts

Lecture 118 Create the Vocabulary for the Spam Classifier

Lecture 119 Coding Challenge: Check for Membership in a Collection

Lecture 120 Coding Challenge: Find the Longest Email

Lecture 121 Sparse Matrix (Part 1): Split the Training and Testing Data

Lecture 122 Sparse Matrix (Part 2): Data Munging with Nested Loops

Lecture 123 Sparse Matrix (Part 3): Using groupby() and Saving .txt Files

Lecture 124 Coding Challenge Solution: Preparing the Test Data

Lecture 125 Checkpoint: Understanding the Data

Lecture 126 Download the Complete Notebook Here

Lecture 127 Any Feedback on this Section?

Section 7: Train a Naive Bayes Classifier to Create a Spam Filter: Part 2

Lecture 128 Setting up the Notebook and Understanding Delimiters in a Dataset

Lecture 129 Create a Full Matrix

Lecture 130 Count the Tokens to Train the Naive Bayes Model

Lecture 131 Sum the Tokens across the Spam and Ham Subsets

Lecture 132 Calculate the Token Probabilities and Save the Trained Model

Lecture 133 Coding Challenge: Prepare the Test Data

Lecture 134 Download the Complete Notebook Here

Lecture 135 Any Feedback on this Section?

Section 8: Test and Evaluate a Naive Bayes Classifier: Part 3

Lecture 136 Set up the Testing Notebook

Lecture 137 Joint Conditional Probability (Part 1): Dot Product

Lecture 138 Joint Conditional Probablity (Part 2): Priors

Lecture 139 Making Predictions: Comparing Joint Probabilities

Lecture 140 The Accuracy Metric

Lecture 141 Visualising the Decision Boundary

Lecture 142 False Positive vs False Negatives

Lecture 143 The Recall Metric

Lecture 144 The Precision Metric

Lecture 145 The F-score or F1 Metric

Lecture 146 A Naive Bayes Implementation using SciKit Learn

Lecture 147 Download the Complete Notebook Here

Lecture 148 Any Feedback on this Section?

Section 9: Introduction to Neural Networks and How to Use Pre-Trained Models

Lecture 149 The Human Brain and the Inspiration for Artificial Neural Networks

Lecture 150 Layers, Feature Generation and Learning

Lecture 151 Costs and Disadvantages of Neural Networks

Lecture 152 Preprocessing Image Data and How RGB Works

Lecture 153 Importing Keras Models and the Tensorflow Graph

Lecture 154 Making Predictions using InceptionResNet

Lecture 155 Coding Challenge Solution: Using other Keras Models

Lecture 156 Download the Complete Notebook Here

Lecture 157 Any Feedback on this Section?

Section 10: Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow

Lecture 158 Solving a Business Problem with Image Classification

Lecture 159 Installing Tensorflow and Keras for Jupyter

Lecture 160 Gathering the CIFAR 10 Dataset

Lecture 161 Exploring the CIFAR Data

Lecture 162 Pre-processing: Scaling Inputs and Creating a Validation Dataset

Lecture 163 Compiling a Keras Model and Understanding the Cross Entropy Loss Function

Lecture 164 Interacting with the Operating System and the Python Try-Catch Block

Lecture 165 Fit a Keras Model and Use Tensorboard to Visualise Learning and Spot Problems

Lecture 166 Use Regularisation to Prevent Overfitting: Early Stopping & Dropout Techniques

Lecture 167 Use the Model to Make Predictions

Lecture 168 Model Evaluation and the Confusion Matrix

Lecture 169 Model Evaluation and the Confusion Matrix

Lecture 170 Download the Complete Notebook Here

Lecture 171 Any Feedback on this Section?

Section 11: Use Tensorflow to Classify Handwritten Digits

Lecture 172 What’s coming up?

Lecture 173 Getting the Data and Loading it into Numpy Arrays

Lecture 174 Data Exploration and Understanding the Structure of the Input Data

Lecture 175 Data Preprocessing: One-Hot Encoding and Creating the Validation Dataset

Lecture 176 What is a Tensor?

Lecture 177 Creating Tensors and Setting up the Neural Network Architecture

Lecture 178 Defining the Cross Entropy Loss Function, the Optimizer and the Metrics

Lecture 179 TensorFlow Sessions and Batching Data

Lecture 180 Tensorboard Summaries and the Filewriter

Lecture 181 Understanding the Tensorflow Graph: Nodes and Edges

Lecture 182 Name Scoping and Image Visualisation in Tensorboard

Lecture 183 Different Model Architectures: Experimenting with Dropout

Lecture 184 Prediction and Model Evaluation

Lecture 185 Download the Complete Notebook Here

Lecture 186 Any Feedback on this Section?

Section 12: Serving a Tensorflow Model through a Website

Lecture 187 What you’ll make

Lecture 188 Saving Tensorflow Models

Lecture 189 Loading a SavedModel

Lecture 190 Converting a Model to Tensorflow.js

Lecture 191 Introducing the Website Project and Tooling

Lecture 192 HTML and CSS Styling

Lecture 193 Loading a Tensorflow.js Model and Starting your own Server

Lecture 194 Adding a Favicon

Lecture 195 Styling an HTML Canvas

Lecture 196 Drawing on an HTML Canvas

Lecture 197 Data Pre-Processing for Tensorflow.js

Lecture 198 Introduction to OpenCV

Lecture 199 Resizing and Adding Padding to Images

Lecture 200 Calculating the Centre of Mass and Shifting the Image

Lecture 201 Making a Prediction from a Digit drawn on the HTML Canvas

Lecture 202 Adding the Game Logic

Lecture 203 Publish and Share your Website!

Lecture 204 Any Feedback on this Section?

Section 13: Next Steps

Lecture 205 Where next?

Lecture 206 What Modules Do You Want to See?

Lecture 207 Stay in Touch!

If you want to learn to code through building fun and useful projects, then take this course.,If you want to solve real-life problems using data.,If you want to learn how to build machine learning algorithms such as deep learning and neural networks.,If you are a seasoned programmer, take this course to get up to speed quickly with the workflow of a data scientist.,If you want to take ONE COURSE and learn everything you need to know about data science and machine learning then take this course.

Course Information:

Udemy | English | 41h 16m | 22.92 GB
Created by: Philipp Muellauer

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