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Development Data Science Python

2021 Python for Machine Learning & Data Science Masterclass

Learn about Data Science and Machine Learning with Python! Including Numpy, Pandas, Matplotlib, Scikit-Learn and more!
Rating: 4.7 out of 54.7 (1,496 ratings)
18,048 students
Created by Jose Portilla
Last updated 2/2021
English
English [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • Machine Learning with Python
  • Data Science with Python
Curated for the Udemy for Business collection

Course content

20 sections • 193 lectures • 36h 37m total length

  • EARLY BIRD INFO
    00:21
  • Preview04:17
  • Anaconda Python and Jupyter Install and Setup
    13:49
  • Note on Environment Setup - Please read me!
    00:34
  • Environment Setup
    09:08

  • OPTIONAL: Python Crash Course
    00:18
  • Python Crash Course - Part One
    16:07
  • Python Crash Course - Part Two
    12:07
  • Python Crash Course - Part Three
    11:19
  • Python Crash Course - Exercise Questions
    01:29
  • Python Crash Course - Exercise Solutions
    09:26

  • Preview10:16

  • Introduction to NumPy
    02:14
  • NumPy Arrays
    22:41
  • Coding Exercise Check-in: Creating NumPy Arrays
    1 question
  • NumPy Indexing and Selection
    11:06
  • Coding Exercise Check-in: Selecting Data from Numpy Array
    1 question
  • NumPy Operations
    08:14
  • Check-In: Operations on NumPy Array
    1 question
  • NumPy Exercises
    01:18
  • Numpy Exercises - Solutions
    07:05

  • Introduction to Pandas
    04:40
  • Series - Part One
    09:28
  • Check-in: Labeled Index in Pandas Series
    1 question
  • Series - Part Two
    10:41
  • DataFrames - Part One - Creating a DataFrame
    19:27
  • DataFrames - Part Two - Basic Properties
    08:18
  • DataFrames - Part Three - Working with Columns
    13:57
  • DataFrames - Part Four - Working with Rows
    14:30
  • Pandas - Conditional Filtering
    17:41
  • Pandas - Useful Methods - Apply on Single Column
    13:47
  • Pandas - Useful Methods - Apply on Multiple Columns
    17:23
  • Pandas - Useful Methods - Statistical Information and Sorting
    15:48
  • Missing Data - Overview
    11:58
  • Missing Data - Pandas Operations
    18:32
  • GroupBy Operations - Part One
    15:49
  • GroupBy Operations - Part Two - MultiIndex
    14:18
  • Combining DataFrames - Concatenation
    10:24
  • Combining DataFrames - Inner Merge
    12:04
  • Combining DataFrames - Left and Right Merge
    06:07
  • Combining DataFrames - Outer Merge
    10:38
  • Pandas - Text Methods for String Data
    16:05
  • Pandas - Time Methods for Date and Time Data
    21:00
  • Pandas Input and Output - CSV Files
    10:20
  • Pandas Input and Output - HTML Tables
    14:41
  • Pandas Input and Output - Excel Files
    07:20
  • Pandas Input and Output - SQL Databases
    18:19
  • Preview21:15
  • Pandas Project Exercise Overview
    05:26
  • Pandas Project Exercise Solutions
    26:31

  • Introduction to Matplotlib
    04:06
  • Matplotlib Basics
    12:35
  • Matplotlib - Understanding the Figure Object
    07:32
  • Matplotlib - Implementing Figures and Axes
    14:31
  • Matplotlib - Figure Parameters
    04:56
  • Preview19:17
  • Matplotlib Styling - Legends
    07:02
  • Matplotlib Styling - Colors and Styles
    14:29
  • Advanced Matplotlib Commands (Optional)
    03:52
  • Matplotlib Exercise Questions Overview
    06:10
  • Matplotlib Exercise Questions - Solutions
    16:39

  • Introduction to Seaborn
    03:54
  • Scatterplots with Seaborn
    18:19
  • Distribution Plots - Part One - Understanding Plot Types
    09:35
  • Distribution Plots - Part Two - Coding with Seaborn
    16:14
  • Categorical Plots - Statistics within Categories - Understanding Plot Types
    05:40
  • Categorical Plots - Statistics within Categories - Coding with Seaborn
    09:15
  • Categorical Plots - Distributions within Categories - Understanding Plot Types
    13:20
  • Categorical Plots - Distributions within Categories - Coding with Seaborn
    17:57
  • Seaborn - Comparison Plots - Understanding the Plot Types
    05:32
  • Seaborn - Comparison Plots - Coding with Seaborn
    09:47
  • Seaborn Grid Plots
    13:39
  • Seaborn - Matrix Plots
    13:18
  • Seaborn Plot Exercises Overview
    06:44
  • Seaborn Plot Exercises Solutions
    14:33

  • Preview12:48
  • Capstone Project Solutions - Part One
    17:15
  • Capstone Project Solutions - Part Two
    14:50
  • Capstone Project Solutions - Part Three
    19:49

  • Introduction to Machine Learning Overview Section
    05:13
  • Why Machine Learning?
    09:15
  • Types of Machine Learning Algorithms
    07:47
  • Supervised Machine Learning Process
    13:41
  • Companion Book - Introduction to Statistical Learning
    02:52

  • Introduction to Linear Regression Section
    01:39
  • Preview09:22
  • Linear Regression - Understanding Ordinary Least Squares
    15:43
  • Linear Regression - Cost Functions
    08:12
  • Linear Regression - Gradient Descent
    11:59
  • Python coding Simple Linear Regression
    19:37
  • Overview of Scikit-Learn and Python
    08:26
  • Linear Regression - Scikit-Learn Train Test Split
    15:48
  • Linear Regression - Scikit-Learn Performance Evaluation - Regression
    15:44
  • Linear Regression - Residual Plots
    13:57
  • Linear Regression - Model Deployment and Coefficient Interpretation
    17:46
  • Polynomial Regression - Theory and Motivation
    07:59
  • Polynomial Regression - Creating Polynomial Features
    10:54
  • Polynomial Regression - Training and Evaluation
    09:44
  • Bias Variance Trade-Off
    10:34
  • Polynomial Regression - Choosing Degree of Polynomial
    13:37
  • Polynomial Regression - Model Deployment
    06:07
  • Regularization Overview
    06:39
  • Feature Scaling
    09:59
  • Introduction to Cross Validation
    12:53
  • Regularization Data Setup
    08:37
  • Preview14:29
  • L2 Regularization - Ridge Regression - Python Implementation
    17:42
  • L1 Regularization - Lasso Regression - Background and Implementation
    15:01
  • L1 and L2 Regularization - Elastic Net
    18:07
  • Linear Regression Project - Data Overview
    04:29

Requirements

  • Basic Python Knowledge (capable of functions)

Description

Early Bird Release for the full upcoming 2021 Python for Machine Learning and Data Science Masterclass!

Please note! This is currently in an Early Bird Beta access, meaning we are still going to be continually adding content to the course (even though we are already at over 20 hours of content!) Since we're still adding content and taking student feedback as we complete the course through the start of 2021, students who enroll now will get access to a wide variety of benefits!

What do you get with Early Bird Access?

You will get exclusive access to weekly live video streams where we will go through interactive machine learning projects! You'll be able to directly ask questions during the streams that will coincide with section launches corresponding to new machine learning algorithms added to the course content! These weekly streams will also include live Q&A with the instructor of the course, Jose Portilla. We will also be taking in student feedback to shape certain upcoming streaming projects. These streams will only be accessible to early bird students, and will be removed once the course is fully complete and launched!

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 comprehensive course is designed to be on par with bootcamps that usually cost thousands of dollars, the final course will include 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

    • Manifold Learning

    • 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

Who this course is for:

  • Beginner Python developers curious about Machine Learning and Data Science with Python

Featured review

Eric Esajian
Eric Esajian
45 courses
27 reviews
Rating: 5.0 out of 52 months ago
Jose is pro, and has perfected online teaching. I've taken basically all his courses, and I am never disappointed. He is thorough, clear, and concise in his teaching and this course is no different. Every Python course he puts out on Udemy, I will continue to take! Thanks, Eric

Instructor

Jose Portilla
Head of Data Science, Pierian Data Inc.
Jose Portilla
  • 4.6 Instructor Rating
  • 717,782 Reviews
  • 2,196,401 Students
  • 31 Courses

  Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and years of experience as a professional instructor and trainer for Data Science and programming. He has publications and patents in various fields such as microfluidics, materials science, and data science technologies. Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data science to help other people learn the power of programming the ability to analyze data, as well as present the data in clear and beautiful visualizations. Currently he works as the Head of Data Science for Pierian Data Inc. and provides in-person data science and python programming training courses to employees working at top companies, including General Electric, Cigna, The New York Times, Credit Suisse, McKinsey and many more. Feel free to contact him on LinkedIn for more information on in-person training sessions or group training sessions in Las Vegas, NV.

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