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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 in-person 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:
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Programming with Python
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NumPy with Python
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Deep dive into Pandas for Data Analysis
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Full understanding of Matplotlib Programming Library
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Deep dive into seaborn for data visualizations
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Machine Learning with SciKit Learn, including:
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Linear Regression
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Regularization
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Lasso Regression
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Ridge Regression
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Elastic Net
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K Nearest Neighbors
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K Means Clustering
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Decision Trees
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Random Forests
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Natural Language Processing
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Support Vector Machines
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Hierarchal Clustering
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DBSCAN
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PCA
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Model Deployment
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and much, much more!
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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
OPTIONAL: Python Crash Course
Machine Learning Pathway Overview
Pandas
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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.
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14NumPy Arrays
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15Coding Exercise Check-in: Creating NumPy Arrays
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16NumPy Indexing and Selection
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17Coding Exercise Check-in: Selecting Data from Numpy Array
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18NumPy Operations
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19Check-In: Operations on NumPy Array
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20NumPy Exercises
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21Numpy Exercises - Solutions
Matplotlib
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22Introduction to Pandas
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23Series - Part One
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24Check-in: Labeled Index in Pandas Series
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25Series - Part Two
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26DataFrames - Part One - Creating a DataFrame
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27DataFrames - Part Two - Basic Properties
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28DataFrames - Part Three - Working with Columns
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29DataFrames - Part Four - Working with Rows
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30Pandas - Conditional Filtering
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31Pandas - Useful Methods - Apply on Single Column
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32Pandas - Useful Methods - Apply on Multiple Columns
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33Pandas - Useful Methods - Statistical Information and Sorting
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34Missing Data - Overview
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35Missing Data - Pandas Operations
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36GroupBy Operations - Part One
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37GroupBy Operations - Part Two - MultiIndex
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38Combining DataFrames - Concatenation
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39Combining DataFrames - Inner Merge
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40Combining DataFrames - Left and Right Merge
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41Combining DataFrames - Outer Merge
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42Pandas - Text Methods for String Data
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43Pandas - Time Methods for Date and Time Data
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44Pandas Input and Output - CSV Files
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45Pandas Input and Output - HTML Tables
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46Pandas Input and Output - Excel Files
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47Pandas Input and Output - SQL Databases
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48Pandas Pivot Tables
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49Pandas Project Exercise Overview
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50Pandas Project Exercise Solutions
Seaborn Data Visualizations
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51Introduction to Matplotlib
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52Matplotlib Basics
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53Matplotlib - Understanding the Figure Object
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54Matplotlib - Implementing Figures and Axes
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55Matplotlib - Figure Parameters
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56Matplotlib - Subplots Functionality
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57Matplotlib Styling - Legends
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58Matplotlib Styling - Colors and Styles
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59Advanced Matplotlib Commands (Optional)
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60Matplotlib Exercise Questions Overview
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61Matplotlib Exercise Questions - Solutions
Data Analysis and Visualization Capstone Project Exercise
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62Introduction to Seaborn
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63Scatterplots with Seaborn
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64Distribution Plots - Part One - Understanding Plot Types
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65Distribution Plots - Part Two - Coding with Seaborn
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66Categorical Plots - Statistics within Categories - Understanding Plot Types
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67Categorical Plots - Statistics within Categories - Coding with Seaborn
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68Categorical Plots - Distributions within Categories - Understanding Plot Types
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69Categorical Plots - Distributions within Categories - Coding with Seaborn
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70Seaborn - Comparison Plots - Understanding the Plot Types
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71Seaborn - Comparison Plots - Coding with Seaborn
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72Seaborn Grid Plots
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73Seaborn - Matrix Plots
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74Seaborn Plot Exercises Overview
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75Seaborn Plot Exercises Solutions
Machine Learning Concepts Overview
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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