4.73 out of 5
4.73
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Complete Data Science & Machine Learning Bootcamp – Python 3

Learn Python, Tensorflow, Deep Learning, Regression, Classification, Neural Networks, Artificial Intelligence & more!
Instructor:
Philipp Muellauer
176 students enrolled
English [Auto-generated]
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

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 35+ 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

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.

Introduction to the Course

1
What is Machine Learning?
2
What is Data Science?
3
Download the Syllabus
4
Top Tips for Succeeding on this Course
5
Course Resources List

Predict Movie Box Office Revenue with Linear Regression

1
Introduction to Linear Regression & Specifying the Problem
2
Gather & Clean the Data
3
Explore & Visualise the Data with Python
4
The Intuition behind the Linear Regression Model
5
Analyse and Evaluate the Results
6
Download the Complete Notebook Here
7
Join the Student Community

Python Programming for Data Science and Machine Learning

1
Windows Users - Install Anaconda
2
Mac Users - Install Anaconda
3
Does LSD Make You Better at Maths?
4
Download the 12 Rules to Learn to Code
5
[Python] - Variables and Types
6
Python Variable Coding Exercise
7
[Python] - Lists and Arrays
8
Python Lists Coding Exercise
9
[Python & Pandas] - Dataframes and Series
10
[Python] - Module Imports
11
[Python] - Functions - Part 1: Defining and Calling Functions
12
Python Functions Coding Exercise - Part 1
13
[Python] - Functions - Part 2: Arguments & Parameters
14
Python Functions Coding Exercise - Part 2
15
[Python] - Functions - Part 3: Results & Return Values
16
Python Functions Coding Exercise - Part 3
17
[Python] - Objects - Understanding Attributes and Methods
18
How to Make Sense of Python Documentation for Data Visualisation
19
Working with Python Objects to Analyse Data
20
[Python] - Tips, Code Style and Naming Conventions
21
Download the Complete Notebook Here

Introduction to Optimisation and the Gradient Descent Algorithm

1
What's Coming Up?
2
How a Machine Learns
3
Introduction to Cost Functions
4
LaTeX Markdown and Generating Data with Numpy
5
Understanding the Power Rule & Creating Charts with Subplots
6
[Python] - Loops and the Gradient Descent Algorithm
7
Python Loops Coding Exercise
8
[Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1)
9
[Python] - Tuples and the Pitfalls of Optimisation (Part 2)
10
Understanding the Learning Rate
11
How to Create 3-Dimensional Charts
12
Understanding Partial Derivatives and How to use SymPy
13
Implementing Batch Gradient Descent with SymPy
14
[Python] - Loops and Performance Considerations
15
Reshaping and Slicing N-Dimensional Arrays
16
Concatenating Numpy Arrays
17
Introduction to the Mean Squared Error (MSE)
18
Transposing and Reshaping Arrays
19
Implementing a MSE Cost Function
20
Understanding Nested Loops and Plotting the MSE Function (Part 1)
21
Plotting the Mean Squared Error (MSE) on a Surface (Part 2)
22
Running Gradient Descent with a MSE Cost Function
23
Visualising the Optimisation on a 3D Surface
24
Download the Complete Notebook Here

Predict House Prices with Multivariable Linear Regression

1
Defining the Problem
2
Gathering the Boston House Price Data
3
Clean and Explore the Data (Part 1): Understand the Nature of the Dataset
4
Clean and Explore the Data (Part 2): Find Missing Values
5
Visualising Data (Part 1): Historams, Distributions & Outliers
6
Visualising Data (Part 2): Seaborn and Probability Density Functions
7
Working with Index Data, Pandas Series, and Dummy Variables
8
Understanding Descriptive Statistics: the Mean vs the Median
9
Introduction to Correlation: Understanding Strength & Direction
10
Calculating Correlations and the Problem posed by Multicollinearity
11
Visualising Correlations with a Heatmap
12
Techniques to Style Scatter Plots
13
A Note for the Next Lesson
14
Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques
15
Understanding Multivariable Regression
16
How to Shuffle and Split Training & Testing Data
17
Running a Multivariable Regression
18
How to Calculate the Model Fit with R-Squared
19
Introduction to Model Evaluation
20
Improving the Model by Transforming the Data
21
How to Interpret Coefficients using p-Values and Statistical Significance
22
Understanding VIF & Testing for Multicollinearity
23
Model Simiplication & Baysian Information Criterion
24
How to Analyse and Plot Regression Residuals
25
Residual Analysis (Part 1): Predicted vs Actual Values
26
Residual Analysis (Part 2): Graphing and Comparing Regression Residuals
27
Making Predictions (Part 1): MSE & R-Squared
28
Making Predictions (Part 2): Standard Deviation, RMSE, and Prediction Intervals
29
Build a Valuation Tool (Part 1): Working with Pandas Series & Numpy ndarrays
30
[Python] - Conditional Statements - Build a Valuation Tool (Part 2)
31
Python Conditional Statement Coding Exercise
32
Build a Valuation Tool (Part 3): Docstrings & Creating your own Python Module
33
Download the Complete Notebook Here

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

1
How to Translate a Business Problem into a Machine Learning Problem
2
Gathering Email Data and Working with Archives & Text Editors
3
How to Add the Lesson Resources to the Project
4
The Naive Bayes Algorithm and the Decision Boundary for a Classifier
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35 hours on-demand video
20 articles
Full lifetime access
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Certificate of Completion