Complete 2026 Data Science & Machine Learning Bootcamp
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Introduction to the Course
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Predict Movie Box Office Revenue with Linear RegressionIntroduction to Linear Regression & Specifying the Problem0sGather & Clean the Data0sExplore & Visualise the Data with Python0sThe Intuition behind the Linear Regression Model0sAnalyse and Evaluate the Results0sDownload the Complete Notebook HereJoin the Student CommunityAny Feedback on this Section?
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Python Programming for Data Science and Machine LearningWindows Users – Install Anaconda0sMac Users – Install Anaconda0sDoes LSD Make You Better at Maths?0sDownload the 12 Rules to Learn to Code[Python] – Variables and Types0s[Python] – Lists and Arrays0s[Python & Pandas] – Dataframes and Series0s[Python] – Module Imports0s[Python] – Functions – Part 1: Defining and Calling Functions0s[Python] – Functions – Part 2: Arguments & Parameters0s[Python] – Functions – Part 3: Results & Return Values0s[Python] – Objects – Understanding Attributes and Methods0sHow to Make Sense of Python Documentation for Data Visualisation0sWorking with Python Objects to Analyse Data0s[Python] – Tips, Code Style and Naming Conventions0sDownload the Complete Notebook HereAny Feedback on this Section?
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Introduction to Optimisation and the Gradient Descent AlgorithmWhat’s Coming Up?0sHow a Machine Learns0sIntroduction to Cost Functions0sLaTeX Markdown and Generating Data with Numpy0sUnderstanding the Power Rule & Creating Charts with Subplots0s[Python] – Loops and the Gradient Descent Algorithm0s[Python] – Advanced Functions and the Pitfalls of Optimisation (Part 1)0s[Python] – Tuples and the Pitfalls of Optimisation (Part 2)0sUnderstanding the Learning Rate0sHow to Create 3-Dimensional Charts0sUnderstanding Partial Derivatives and How to use SymPy0sImplementing Batch Gradient Descent with SymPy0s[Python] – Loops and Performance Considerations0sReshaping and Slicing N-Dimensional Arrays0sConcatenating Numpy Arrays0sIntroduction to the Mean Squared Error (MSE)0sTransposing and Reshaping Arrays0sImplementing a MSE Cost Function0sUnderstanding Nested Loops and Plotting the MSE Function (Part 1)0sPlotting the Mean Squared Error (MSE) on a Surface (Part 2)0sRunning Gradient Descent with a MSE Cost Function0sVisualising the Optimisation on a 3D Surface0sDownload the Complete Notebook HereAny Feedback on this Section?
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Predict House Prices with Multivariable Linear RegressionDefining the Problem0sGathering the Boston House Price Data0sClean and Explore the Data (Part 1): Understand the Nature of the Dataset0sClean and Explore the Data (Part 2): Find Missing Values0sVisualising Data (Part 1): Historams, Distributions & Outliers0sVisualising Data (Part 2): Seaborn and Probability Density Functions0sWorking with Index Data, Pandas Series, and Dummy Variables0sUnderstanding Descriptive Statistics: the Mean vs the Median0sIntroduction to Correlation: Understanding Strength & Direction0sCalculating Correlations and the Problem posed by Multicollinearity0sVisualising Correlations with a Heatmap0sTechniques to Style Scatter Plots0sA Note for the Next LessonWorking with Seaborn Pairplots & Jupyter Microbenchmarking Techniques0sUnderstanding Multivariable Regression0sHow to Shuffle and Split Training & Testing Data0sRunning a Multivariable Regression0sHow to Calculate the Model Fit with R-Squared0sIntroduction to Model Evaluation0sImproving the Model by Transforming the Data0sHow to Interpret Coefficients using p-Values and Statistical Significance0sUnderstanding VIF & Testing for Multicollinearity0sModel Simplification & Baysian Information Criterion0sHow to Analyse and Plot Regression Residuals0sResidual Analysis (Part 1): Predicted vs Actual Values0sResidual Analysis (Part 2): Graphing and Comparing Regression Residuals0sMaking Predictions (Part 1): MSE & R-Squared0sMaking Predictions (Part 2): Standard Deviation, RMSE, and Prediction Intervals0sBuild a Valuation Tool (Part 1): Working with Pandas Series & Numpy ndarrays0s[Python] – Conditional Statements – Build a Valuation Tool (Part 2)0sBuild a Valuation Tool (Part 3): Docstrings & Creating your own Python Module0sDownload the Complete Notebook HereAny Feedback on this Section?
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Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1How to Translate a Business Problem into a Machine Learning Problem0sGathering Email Data and Working with Archives & Text Editors0sHow to Add the Lesson Resources to the Project0sThe Naive Bayes Algorithm and the Decision Boundary for a Classifier0sBasic Probability0sJoint & Conditional Probability0sBayes Theorem0sReading Files (Part 1): Absolute Paths and Relative Paths0sReading Files (Part 2): Stream Objects and Email Structure0sExtracting the Text in the Email Body0s[Python] – Generator Functions & the yield Keyword0sCreate a Pandas DataFrame of Email Bodies0sCleaning Data (Part 1): Check for Empty Emails & Null Entries0sCleaning Data (Part 2): Working with a DataFrame Index0sSaving a JSON File with Pandas0sData Visualisation (Part 1): Pie Charts0sData Visualisation (Part 2): Donut Charts0sIntroduction to Natural Language Processing (NLP)0sTokenizing, Removing Stop Words and the Python Set Data Structure0sWord Stemming & Removing Punctuation0sRemoving HTML tags with BeautifulSoup0sCreating a Function for Text Processing0sA Note for the Next LessonAdvanced Subsetting on DataFrames: the apply() Function0s[Python] – Logical Operators to Create Subsets and Indices0sWord Clouds & How to install Additional Python Packages0sCreating your First Word Cloud0sStyling the Word Cloud with a Mask0sSolving the Hamlet Challenge0sStyling Word Clouds with Custom Fonts0sCreate the Vocabulary for the Spam Classifier0sCoding Challenge: Check for Membership in a Collection0sCoding Challenge: Find the Longest Email0sSparse Matrix (Part 1): Split the Training and Testing Data0sSparse Matrix (Part 2): Data Munging with Nested Loops0sSparse Matrix (Part 3): Using groupby() and Saving .txt Files0sCoding Challenge Solution: Preparing the Test Data0sCheckpoint: Understanding the Data0sDownload the Complete Notebook HereAny Feedback on this Section?
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Train a Naive Bayes Classifier to Create a Spam Filter Part 2Setting up the Notebook and Understanding Delimiters in a Dataset0sCreate a Full Matrix0sCount the Tokens to Train the Naive Bayes Model0sSum the Tokens across the Spam and Ham Subsets0sCalculate the Token Probabilities and Save the Trained Model0sCoding Challenge: Prepare the Test Data0sDownload the Complete Notebook HereAny Feedback on this Section?
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Test and Evaluate a Naive Bayes Classifier Part 3Set up the Testing Notebook0sJoint Conditional Probability (Part 1): Dot Product0sJoint Conditional Probablity (Part 2): Priors0sMaking Predictions: Comparing Joint Probabilities0sThe Accuracy Metric0sVisualising the Decision Boundary0sFalse Positive vs False Negatives0sThe Recall Metric0sThe Precision Metric0sThe F-score or F1 Metric0sA Naive Bayes Implementation using SciKit Learn0sDownload the Complete Notebook HereAny Feedback on this Section?
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Introduction to Neural Networks and How to Use Pre-Trained ModelsThe Human Brain and the Inspiration for Artificial Neural Networks0sLayers, Feature Generation and Learning0sCosts and Disadvantages of Neural Networks0sPreprocessing Image Data and How RGB Works0sImporting Keras Models and the Tensorflow Graph0sMaking Predictions using InceptionResNet0sCoding Challenge Solution: Using other Keras Models0sDownload the Complete Notebook HereAny Feedback on this Section?
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Build an Artificial Neural Network to Recognise Images using Keras & TensorflowSolving a Business Problem with Image Classification0sInstalling Tensorflow and Keras for Jupyter0sGathering the CIFAR 10 Dataset0sExploring the CIFAR Data0sPre-processing: Scaling Inputs and Creating a Validation Dataset0sCompiling a Keras Model and Understanding the Cross Entropy Loss Function0sInteracting with the Operating System and the Python Try-Catch Block0sFit a Keras Model and Use Tensorboard to Visualise Learning and Spot Problems0sUse Regularisation to Prevent Overfitting: Early Stopping & Dropout Techniques0sUse the Model to Make Predictions0sModel Evaluation and the Confusion Matrix0sModel Evaluation and the Confusion Matrix0sDownload the Complete Notebook HereAny Feedback on this Section?
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Use Tensorflow to Classify Handwritten DigitsWhat’s coming up?0sGetting the Data and Loading it into Numpy Arrays0sData Exploration and Understanding the Structure of the Input Data0sData Preprocessing: One-Hot Encoding and Creating the Validation Dataset0sWhat is a Tensor?0sCreating Tensors and Setting up the Neural Network Architecture0sDefining the Cross Entropy Loss Function, the Optimizer and the Metrics0sTensorFlow Sessions and Batching Data0sTensorboard Summaries and the Filewriter0sUnderstanding the Tensorflow Graph: Nodes and Edges0sName Scoping and Image Visualisation in Tensorboard0sDifferent Model Architectures: Experimenting with Dropout0sPrediction and Model Evaluation0sDownload the Complete Notebook HereAny Feedback on this Section?
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Serving a Tensorflow Model through a WebsiteWhat you’ll make0sSaving Tensorflow Models0sLoading a SavedModel0sConverting a Model to Tensorflow.js0sIntroducing the Website Project and Tooling0sHTML and CSS Styling0sLoading a Tensorflow.js Model and Starting your own Server0sAdding a Favicon0sStyling an HTML Canvas0sDrawing on an HTML Canvas0sData Pre-Processing for Tensorflow.js0sIntroduction to OpenCV0sResizing and Adding Padding to Images0sCalculating the Centre of Mass and Shifting the Image0sMaking a Prediction from a Digit drawn on the HTML Canvas0sAdding the Game Logic0sPublish and Share your Website!0sAny Feedback on this Section?
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Next Steps
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-Processing
Data Exploration and Visualisation
Linear Regression
Multivariable Regression
Optimisation Algorithms and Gradient Descent
Naive Bayes Classification
Descriptive Statistics and Probability Theory
Neural Networks and Deep Learning
Model Evaluation and Analysis
Serving a Tensorflow Model
Throughout the course, we cover all the tools used by data scientists and machine learning experts, including:
Python 3
Tensorflow
Pandas
Numpy
Scikit Learn
Keras
Matplotlib
Seaborn
SciPy
SymPy
By 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 Variables
String Manipulation
Functions
Objects
Lists, Tuples and Dictionaries
Loops and Iterators
Conditionals and Control Flow
Generator Functions
Context Managers and Name Scoping
Error Handling
By 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 Lectures
30+ Code Challenges and Exercises
Fully Fledged Data Science and Machine Learning Projects
Programming Resources and Cheatsheets
Our best selling 12 Rules to Learn to Code eBook
$12,000+ data science & machine learning bootcamp course materials and curriculum
Don’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.” -Ian
REMEMBER… 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.
What's included
- 41 hours on-demand video
- 7 coding exercises
- 31 articles
- 34 downloadable resources
- Access on mobile and TV
- Certificate of completion