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2020-12-17 08:47:02
30-Day Money-Back Guarantee

This course includes:

  • 41 hours on-demand video
  • 31 articles
  • 34 downloadable resources
  • 7 coding exercises
  • Full lifetime access
  • Access on mobile and TV
Development Data Science

Complete 2020 Data Science & Machine Learning Bootcamp

Learn Python, Tensorflow, Deep Learning, Regression, Classification, Neural Networks, Artificial Intelligence & more!
Rating: 4.6 out of 54.6 (2,752 ratings)
21,349 students
Created by Philipp Muellauer, Dr. Angela Yu
Last updated 8/2020
English
English [Auto]
30-Day Money-Back Guarantee

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
Curated for the Udemy for Business collection

Course content

13 sections • 207 lectures • 41h 16m total length

  • Preview04:39
  • What is Data Science?
    04:08
  • Download the Syllabus
    00:21
  • Top Tips for Succeeding on this Course
    01:12
  • Course Resources List
    00:29

  • Preview06:07
  • Gather & Clean the Data
    09:50
  • Explore & Visualise the Data with Python
    22:28
  • The Intuition behind the Linear Regression Model
    07:23
  • Analyse and Evaluate the Results
    15:48
  • Download the Complete Notebook Here
    00:10
  • Join the Student Community
    00:19
  • Any Feedback on this Section?
    00:10

  • Windows Users - Install Anaconda
    06:44
  • Mac Users - Install Anaconda
    06:13
  • Does LSD Make You Better at Maths?
    05:30
  • Download the 12 Rules to Learn to Code
    00:41
  • [Python] - Variables and Types
    14:20
  • Python Variable Coding Exercise
    1 question
  • [Python] - Lists and Arrays
    10:24
  • Python Lists Coding Exercise
    1 question
  • [Python & Pandas] - Dataframes and Series
    24:31
  • [Python] - Module Imports
    29:34
  • [Python] - Functions - Part 1: Defining and Calling Functions
    07:46
  • Python Functions Coding Exercise - Part 1
    1 question
  • [Python] - Functions - Part 2: Arguments & Parameters
    17:19
  • Python Functions Coding Exercise - Part 2
    1 question
  • [Python] - Functions - Part 3: Results & Return Values
    13:37
  • Python Functions Coding Exercise - Part 3
    1 question
  • [Python] - Objects - Understanding Attributes and Methods
    24:17
  • How to Make Sense of Python Documentation for Data Visualisation
    23:10
  • Working with Python Objects to Analyse Data
    22:50
  • [Python] - Tips, Code Style and Naming Conventions
    12:37
  • Download the Complete Notebook Here
    00:10
  • Any Feedback on this Section?
    00:10

  • What's Coming Up?
    02:42
  • How a Machine Learns
    05:25
  • Introduction to Cost Functions
    07:28
  • LaTeX Markdown and Generating Data with Numpy
    15:25
  • Understanding the Power Rule & Creating Charts with Subplots
    14:51
  • [Python] - Loops and the Gradient Descent Algorithm
    37:00
  • Python Loops Coding Exercise
    1 question
  • [Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1)
    37:35
  • [Python] - Tuples and the Pitfalls of Optimisation (Part 2)
    30:05
  • Understanding the Learning Rate
    29:39
  • How to Create 3-Dimensional Charts
    24:38
  • Understanding Partial Derivatives and How to use SymPy
    18:22
  • Implementing Batch Gradient Descent with SymPy
    12:24
  • [Python] - Loops and Performance Considerations
    15:55
  • Reshaping and Slicing N-Dimensional Arrays
    19:13
  • Concatenating Numpy Arrays
    07:38
  • Introduction to the Mean Squared Error (MSE)
    10:49
  • Transposing and Reshaping Arrays
    12:51
  • Implementing a MSE Cost Function
    12:15
  • Understanding Nested Loops and Plotting the MSE Function (Part 1)
    11:48
  • Plotting the Mean Squared Error (MSE) on a Surface (Part 2)
    16:17
  • Running Gradient Descent with a MSE Cost Function
    19:53
  • Visualising the Optimisation on a 3D Surface
    09:33
  • Download the Complete Notebook Here
    00:10
  • Any Feedback on this Section?
    00:10

  • Defining the Problem
    04:45
  • Gathering the Boston House Price Data
    06:59
  • Clean and Explore the Data (Part 1): Understand the Nature of the Dataset
    13:03
  • Clean and Explore the Data (Part 2): Find Missing Values
    17:18
  • Visualising Data (Part 1): Historams, Distributions & Outliers
    12:39
  • Visualising Data (Part 2): Seaborn and Probability Density Functions
    08:30
  • Working with Index Data, Pandas Series, and Dummy Variables
    18:04
  • Understanding Descriptive Statistics: the Mean vs the Median
    10:13
  • Introduction to Correlation: Understanding Strength & Direction
    06:41
  • Calculating Correlations and the Problem posed by Multicollinearity
    14:28
  • Visualising Correlations with a Heatmap
    21:37
  • Techniques to Style Scatter Plots
    17:20
  • A Note for the Next Lesson
    00:11
  • Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques
    24:00
  • Understanding Multivariable Regression
    06:06
  • How to Shuffle and Split Training & Testing Data
    10:21
  • Running a Multivariable Regression
    08:42
  • How to Calculate the Model Fit with R-Squared
    04:19
  • Introduction to Model Evaluation
    02:39
  • Improving the Model by Transforming the Data
    20:22
  • How to Interpret Coefficients using p-Values and Statistical Significance
    09:09
  • Understanding VIF & Testing for Multicollinearity
    21:49
  • Model Simplification & Baysian Information Criterion
    19:35
  • How to Analyse and Plot Regression Residuals
    10:58
  • Residual Analysis (Part 1): Predicted vs Actual Values
    16:30
  • Residual Analysis (Part 2): Graphing and Comparing Regression Residuals
    19:51
  • Making Predictions (Part 1): MSE & R-Squared
    19:29
  • Making Predictions (Part 2): Standard Deviation, RMSE, and Prediction Intervals
    12:48
  • Build a Valuation Tool (Part 1): Working with Pandas Series & Numpy ndarrays
    18:24
  • [Python] - Conditional Statements - Build a Valuation Tool (Part 2)
    19:50
  • Python Conditional Statement Coding Exercise
    1 question
  • Build a Valuation Tool (Part 3): Docstrings & Creating your own Python Module
    27:36
  • Download the Complete Notebook Here
    00:10
  • Any Feedback on this Section?
    00:10

  • How to Translate a Business Problem into a Machine Learning Problem
    07:25
  • Gathering Email Data and Working with Archives & Text Editors
    11:13
  • How to Add the Lesson Resources to the Project
    03:40
  • The Naive Bayes Algorithm and the Decision Boundary for a Classifier
    05:02
  • Basic Probability
    04:34
  • Joint & Conditional Probability
    16:37
  • Bayes Theorem
    12:15
  • Reading Files (Part 1): Absolute Paths and Relative Paths
    09:31
  • Reading Files (Part 2): Stream Objects and Email Structure
    12:22
  • Extracting the Text in the Email Body
    05:26
  • [Python] - Generator Functions & the yield Keyword
    20:02
  • Create a Pandas DataFrame of Email Bodies
    06:09
  • Cleaning Data (Part 1): Check for Empty Emails & Null Entries
    15:43
  • Cleaning Data (Part 2): Working with a DataFrame Index
    08:15
  • Saving a JSON File with Pandas
    06:06
  • Data Visualisation (Part 1): Pie Charts
    13:52
  • Data Visualisation (Part 2): Donut Charts
    08:04
  • Introduction to Natural Language Processing (NLP)
    07:46
  • Tokenizing, Removing Stop Words and the Python Set Data Structure
    15:50
  • Word Stemming & Removing Punctuation
    09:30
  • Removing HTML tags with BeautifulSoup
    09:40
  • Creating a Function for Text Processing
    07:39
  • A Note for the Next Lesson
    00:11
  • Advanced Subsetting on DataFrames: the apply() Function
    12:16
  • [Python] - Logical Operators to Create Subsets and Indices
    12:47
  • Word Clouds & How to install Additional Python Packages
    09:13
  • Creating your First Word Cloud
    11:51
  • Styling the Word Cloud with a Mask
    15:25
  • Solving the Hamlet Challenge
    06:45
  • Styling Word Clouds with Custom Fonts
    12:52
  • Create the Vocabulary for the Spam Classifier
    15:41
  • Coding Challenge: Check for Membership in a Collection
    05:15
  • Coding Challenge: Find the Longest Email
    07:17
  • Sparse Matrix (Part 1): Split the Training and Testing Data
    13:16
  • Sparse Matrix (Part 2): Data Munging with Nested Loops
    21:19
  • Sparse Matrix (Part 3): Using groupby() and Saving .txt Files
    10:52
  • Coding Challenge Solution: Preparing the Test Data
    04:40
  • Checkpoint: Understanding the Data
    11:53
  • Download the Complete Notebook Here
    00:10
  • Any Feedback on this Section?
    00:10

  • Setting up the Notebook and Understanding Delimiters in a Dataset
    10:22
  • Create a Full Matrix
    18:11
  • Count the Tokens to Train the Naive Bayes Model
    16:00
  • Sum the Tokens across the Spam and Ham Subsets
    07:37
  • Calculate the Token Probabilities and Save the Trained Model
    08:04
  • Coding Challenge: Prepare the Test Data
    04:44
  • Download the Complete Notebook Here
    00:10
  • Any Feedback on this Section?
    00:10

  • Set up the Testing Notebook
    03:53
  • Joint Conditional Probability (Part 1): Dot Product
    11:39
  • Joint Conditional Probablity (Part 2): Priors
    09:39
  • Making Predictions: Comparing Joint Probabilities
    08:17
  • The Accuracy Metric
    06:54
  • Visualising the Decision Boundary
    30:54
  • False Positive vs False Negatives
    11:35
  • The Recall Metric
    05:43
  • The Precision Metric
    08:04
  • The F-score or F1 Metric
    04:29
  • A Naive Bayes Implementation using SciKit Learn
    29:42
  • Download the Complete Notebook Here
    00:10
  • Any Feedback on this Section?
    00:10

  • The Human Brain and the Inspiration for Artificial Neural Networks
    08:10
  • Layers, Feature Generation and Learning
    21:10
  • Costs and Disadvantages of Neural Networks
    13:48
  • Preprocessing Image Data and How RGB Works
    13:23
  • Importing Keras Models and the Tensorflow Graph
    09:19
  • Making Predictions using InceptionResNet
    16:43
  • Coding Challenge Solution: Using other Keras Models
    11:27
  • Download the Complete Notebook Here
    00:10
  • Any Feedback on this Section?
    00:10

  • Solving a Business Problem with Image Classification
    03:54
  • Installing Tensorflow and Keras for Jupyter
    05:08
  • Gathering the CIFAR 10 Dataset
    05:05
  • Exploring the CIFAR Data
    15:57
  • Pre-processing: Scaling Inputs and Creating a Validation Dataset
    15:53
  • Compiling a Keras Model and Understanding the Cross Entropy Loss Function
    14:32
  • Interacting with the Operating System and the Python Try-Catch Block
    19:50
  • Fit a Keras Model and Use Tensorboard to Visualise Learning and Spot Problems
    12:19
  • Use Regularisation to Prevent Overfitting: Early Stopping & Dropout Techniques
    23:21
  • Use the Model to Make Predictions
    26:26
  • Model Evaluation and the Confusion Matrix
    09:00
  • Model Evaluation and the Confusion Matrix
    33:25
  • Download the Complete Notebook Here
    00:10
  • Any Feedback on this Section?
    00:10

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 a 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 35 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.


Who this course is for:

  • 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.

Featured review

David Martinez
David Martinez
29 courses
9 reviews
Rating: 4.5 out of 59 months ago
It is a very good course, it specifies a lot things, it would be amazing if it would have big projects that you would have to deliver or something, or just homeworks that gather all the previous content, not just challenges, but the idea of challenges is amazing because it refreshes your memory a little bit, it is an amazing course that in fact is one of the best courses in machine learning out there, specially because they also teach the math behind every little thing

Instructors

Philipp Muellauer
Data Scientist | Android Developer | Teacher
Philipp Muellauer
  • 4.5 Instructor Rating
  • 9,866 Reviews
  • 55,527 Students
  • 2 Courses

I’m Philipp, I’m a data scientist and mobile developer with a passion for teaching. I’m the lead instructor at the London App Brewery for machine learning and Android development, fluent in Python, Java, Swift, Dart, and VBA. I’ve taught thousands of students in-person in our London classroom and lead our corporate training, used by companies such as Google, Amazon and Twitter. I'm always thinking about how to make difficult concepts easy to understand, what kind of projects would make a fun tutorial, and how I can help you succeed through my courses.

Dr. Angela Yu
Developer and Lead Instructor
Dr. Angela Yu
  • 4.7 Instructor Rating
  • 243,075 Reviews
  • 708,432 Students
  • 8 Courses

I'm Angela, I'm a developer with a passion for teaching. I'm the lead instructor at the London App Brewery, London's leading Programming Bootcamp. I've helped hundreds of thousands of students learn to code and change their lives by becoming a developer. I've been invited by companies such as Twitter, Facebook and Google to teach their employees.

My first foray into programming was when I was just 12 years old, wanting to build my own Space Invader game. Since then, I've made hundred of websites, apps and games. But most importantly, I realised that my greatest passion is teaching.

I spend most of my time researching how to make learning to code fun and make hard concepts easy to understand. I apply everything I discover into my bootcamp courses. In my courses, you'll find lots of geeky humour but also lots of explanations and animations to make sure everything is easy to understand.

I'll be there for you every step of the way.

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