Complete 2020 Data Science & Machine Learning Bootcamp
4.4 (1,760 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
13,422 students enrolled

Complete 2020 Data Science & Machine Learning Bootcamp

Learn Python, Tensorflow, Deep Learning, Regression, Classification, Neural Networks, Artificial Intelligence & more!
4.4 (1,760 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
13,422 students enrolled
Last updated 1/2020
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Current price: $139.99 Original price: $199.99 Discount: 30% off
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This course includes
  • 41 hours on-demand video
  • 31 articles
  • 34 downloadable resources
  • 7 coding exercises
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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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
Course content
Expand all 207 lectures 41:16:35
+ Introduction to the Course
5 lectures 10:50
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
+ Predict Movie Box Office Revenue with Linear Regression
8 lectures 01:02:16
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
+ Python Programming for Data Science and Machine Learning
17 lectures 03:39:54
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
+ Introduction to Optimisation and the Gradient Descent Algorithm
24 lectures 06:12:07
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
+ Predict House Prices with Multivariable Linear Regression
33 lectures 07:04:37
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
+ Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails: Part 1
40 lectures 06:28:35
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
+ Train a Naive Bayes Classifier to Create a Spam Filter: Part 2
8 lectures 01:05:19
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
+ Test and Evaluate a Naive Bayes Classifier: Part 3
13 lectures 02:11:10
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
+ Introduction to Neural Networks and How to Use Pre-Trained Models
9 lectures 01:34:21
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
+ Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow
14 lectures 03:05:11
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.