Complete 2022 Data Science & Machine Learning Bootcamp
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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 inperson 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 realworld 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, weâ€™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 inperson programming bootcamp.
We’ll take you stepbystep 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 realworld problems.
In the curriculum, we cover a large number of important data science and machine learning topics, such as:

Data Cleaning and PreProcessing

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. Weâ€™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 realworld 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:
â€śOne 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.â€ť Robert Vance
â€śI’ve spent ÂŁ27,000 on University….. Save some money and buy any course available by Philipp! Great stuff guys.â€ť Terry Woodward
“This course is amazingly immersive and quite allinclusive from endtoend 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
â€śGreat going so far. Like the idea of the quizzes to challenge us as we go along. Explanations are clear and easy to followâ€ť Lenox James
â€śVery good explained course. The tasks and challenges are fun to do learn an do! Would recommend it a thousand times.â€ť Andres Ariza
â€śI enjoy the step by step method they introduce the topics. Anyone with an interest in programming would be able to follow and programâ€ť Isaac Barnor
â€śI 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.â€ť Dale Barnes
â€śThis course has been amazing. Thanks for all the info. I’ll definitely try to put this in use. :)â€ť Devanshika Ghosh
â€śGreat Narration and explanations. Very interactive lectures which make me keep looking forward to the next tutorialâ€ť Bimal Becks
â€śEnglish is not my native language but in this video, Phillip has great pronunciation so I don’t have problem even without subtitles :)â€ť Dreamerx85
â€śClear, precise and easy to follow instructions & explanations!â€ť Andreea Andrei
â€śAn incredible course in a succinct, wellthoughtout, easy to understand package. I wish I had purchased this course first.â€ť Ian
REMEMBERâ€¦ 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 nobrainer, 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.

6Introduction to Linear Regression & Specifying the Problem

7Gather & Clean the Data

8Explore & Visualise the Data with Python

9The Intuition behind the Linear Regression Model

10Analyse and Evaluate the Results

11Download the Complete Notebook Here

12Join the Student Community

13Any Feedback on this Section?

14Windows Users  Install Anaconda

15Mac Users  Install Anaconda

16Does LSD Make You Better at Maths?

17Download the 12 Rules to Learn to Code

18[Python]  Variables and Types

19Python Variable Coding Exercise

20[Python]  Lists and Arrays

21Python Lists Coding Exercise

22[Python & Pandas]  Dataframes and Series

23[Python]  Module Imports

24[Python]  Functions  Part 1: Defining and Calling Functions

25Python Functions Coding Exercise  Part 1

26[Python]  Functions  Part 2: Arguments & Parameters

27Python Functions Coding Exercise  Part 2

28[Python]  Functions  Part 3: Results & Return Values

29Python Functions Coding Exercise  Part 3

30[Python]  Objects  Understanding Attributes and Methods

31How to Make Sense of Python Documentation for Data Visualisation

32Working with Python Objects to Analyse Data

33[Python]  Tips, Code Style and Naming Conventions

34Download the Complete Notebook Here

35Any Feedback on this Section?

36What's Coming Up?

37How a Machine Learns

38Introduction to Cost Functions

39LaTeX Markdown and Generating Data with Numpy

40Understanding the Power Rule & Creating Charts with Subplots

41[Python]  Loops and the Gradient Descent Algorithm

42Python Loops Coding Exercise

43[Python]  Advanced Functions and the Pitfalls of Optimisation (Part 1)

44[Python]  Tuples and the Pitfalls of Optimisation (Part 2)

45Understanding the Learning Rate

46How to Create 3Dimensional Charts

47Understanding Partial Derivatives and How to use SymPy

48Implementing Batch Gradient Descent with SymPy

49[Python]  Loops and Performance Considerations

50Reshaping and Slicing NDimensional Arrays

51Concatenating Numpy Arrays

52Introduction to the Mean Squared Error (MSE)

53Transposing and Reshaping Arrays

54Implementing a MSE Cost Function

55Understanding Nested Loops and Plotting the MSE Function (Part 1)

56Plotting the Mean Squared Error (MSE) on a Surface (Part 2)

57Running Gradient Descent with a MSE Cost Function

58Visualising the Optimisation on a 3D Surface

59Download the Complete Notebook Here

60Any Feedback on this Section?

61Defining the Problem

62Gathering the Boston House Price Data

63Clean and Explore the Data (Part 1): Understand the Nature of the Dataset

64Clean and Explore the Data (Part 2): Find Missing Values

65Visualising Data (Part 1): Historams, Distributions & Outliers

66Visualising Data (Part 2): Seaborn and Probability Density Functions

67Working with Index Data, Pandas Series, and Dummy Variables

68Understanding Descriptive Statistics: the Mean vs the Median

69Introduction to Correlation: Understanding Strength & Direction

70Calculating Correlations and the Problem posed by Multicollinearity

71Visualising Correlations with a Heatmap

72Techniques to Style Scatter Plots

73A Note for the Next Lesson

74Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques

75Understanding Multivariable Regression

76How to Shuffle and Split Training & Testing Data

77Running a Multivariable Regression

78How to Calculate the Model Fit with RSquared

79Introduction to Model Evaluation

80Improving the Model by Transforming the Data

81How to Interpret Coefficients using pValues and Statistical Significance

82Understanding VIF & Testing for Multicollinearity

83Model Simplification & Baysian Information Criterion

84How to Analyse and Plot Regression Residuals

85Residual Analysis (Part 1): Predicted vs Actual Values

86Residual Analysis (Part 2): Graphing and Comparing Regression Residuals

87Making Predictions (Part 1): MSE & RSquared

88Making Predictions (Part 2): Standard Deviation, RMSE, and Prediction Intervals

89Build a Valuation Tool (Part 1): Working with Pandas Series & Numpy ndarrays

90[Python]  Conditional Statements  Build a Valuation Tool (Part 2)

91Python Conditional Statement Coding Exercise

92Build a Valuation Tool (Part 3): Docstrings & Creating your own Python Module

93Download the Complete Notebook Here

94Any Feedback on this Section?