Python for Machine Learning & Data Science Masterclass
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This is the most complete course online for learning about Python, Data Science, and Machine Learning. Join Jose Portilla’s over 3 million students to learn about the future today!
What is in the course?
Welcome to the most complete course on learning Data Science and Machine Learning on the internet! After teaching over 2 million students I’ve worked for over a year to put together what I believe to be the best way to go from zero to hero for data science and machine learning in Python!
This course is designed for the student who already knows some Python and is ready to dive deeper into using those Python skills for Data Science and Machine Learning. The typical starting salary for a data scientists can be over $150,000 dollars, and we’ve created this course to help guide students to learning a set of skills to make them extremely hirable in today’s workplace environment.
We’ll cover everything you need to know for the full data science and machine learning tech stack required at the world’s top companies. Our students have gotten jobs at McKinsey, Facebook, Amazon, Google, Apple, Asana, and other top tech companies! We’ve structured the course using our experience teaching both online and inperson to deliver a clear and structured approach that will guide you through understanding not just how to use data science and machine learning libraries, but why we use them. This course is balanced between practical real world case studies and mathematical theory behind the machine learning algorithms.
We cover advanced machine learning algorithms that most other courses don’t! Including advanced regularization methods and state of the art unsupervised learning methods, such as DBSCAN.
This comprehensive course is designed to be on par with Bootcamps that usually cost thousands of dollars and includes the following topics:

Programming with Python

NumPy with Python

Deep dive into Pandas for Data Analysis

Full understanding of Matplotlib Programming Library

Deep dive into seaborn for data visualizations

Machine Learning with SciKit Learn, including:

Linear Regression

Regularization

Lasso Regression

Ridge Regression

Elastic Net

K Nearest Neighbors

K Means Clustering

Decision Trees

Random Forests

Natural Language Processing

Support Vector Machines

Hierarchal Clustering

DBSCAN

PCA

Model Deployment

and much, much more!

As always, we’re grateful for the chance to teach you data science, machine learning, and python and hope you will join us inside the course to boost your skillset!
Jose and Pierian Data Inc. Team

13Introduction to NumPy
Get an overview of the NumPy topics we will discuss in this course! Numpy is a key part of data science and machine learning.

14NumPy Arrays

15Coding Exercise Checkin: Creating NumPy Arrays

16NumPy Indexing and Selection

17Coding Exercise Checkin: Selecting Data from Numpy Array

18NumPy Operations

19CheckIn: Operations on NumPy Array

20NumPy Exercises

21Numpy Exercises  Solutions

22Introduction to Pandas

23Series  Part One

24Checkin: Labeled Index in Pandas Series

25Series  Part Two

26DataFrames  Part One  Creating a DataFrame

27DataFrames  Part Two  Basic Properties

28DataFrames  Part Three  Working with Columns

29DataFrames  Part Four  Working with Rows

30Pandas  Conditional Filtering

31Pandas  Useful Methods  Apply on Single Column

32Pandas  Useful Methods  Apply on Multiple Columns

33Pandas  Useful Methods  Statistical Information and Sorting

34Missing Data  Overview

35Missing Data  Pandas Operations

36GroupBy Operations  Part One

37GroupBy Operations  Part Two  MultiIndex

38Combining DataFrames  Concatenation

39Combining DataFrames  Inner Merge

40Combining DataFrames  Left and Right Merge

41Combining DataFrames  Outer Merge

42Pandas  Text Methods for String Data

43Pandas  Time Methods for Date and Time Data

44Pandas Input and Output  CSV Files

45Pandas Input and Output  HTML Tables

46Pandas Input and Output  Excel Files

47Pandas Input and Output  SQL Databases

48Pandas Pivot Tables

49Pandas Project Exercise Overview

50Pandas Project Exercise Solutions

51Introduction to Matplotlib

52Matplotlib Basics

53Matplotlib  Understanding the Figure Object

54Matplotlib  Implementing Figures and Axes

55Matplotlib  Figure Parameters

56Matplotlib  Subplots Functionality

57Matplotlib Styling  Legends

58Matplotlib Styling  Colors and Styles

59Advanced Matplotlib Commands (Optional)

60Matplotlib Exercise Questions Overview

61Matplotlib Exercise Questions  Solutions

62Introduction to Seaborn

63Scatterplots with Seaborn

64Distribution Plots  Part One  Understanding Plot Types

65Distribution Plots  Part Two  Coding with Seaborn

66Categorical Plots  Statistics within Categories  Understanding Plot Types

67Categorical Plots  Statistics within Categories  Coding with Seaborn

68Categorical Plots  Distributions within Categories  Understanding Plot Types

69Categorical Plots  Distributions within Categories  Coding with Seaborn

70Seaborn  Comparison Plots  Understanding the Plot Types

71Seaborn  Comparison Plots  Coding with Seaborn

72Seaborn Grid Plots

73Seaborn  Matrix Plots

74Seaborn Plot Exercises Overview

75Seaborn Plot Exercises Solutions