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Python for Machine Learning & Data Science Masterclass

Jose Portilla and Pierian Training

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.

Read more

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:

  • 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

Enroll now

What's inside

Learning objectives

  • You will learn how to use data science and machine learning with python.
  • You will create data pipeline workflows to analyze, visualize, and gain insights from data.
  • You will build a portfolio of data science projects with real world data.
  • You will be able to analyze your own data sets and gain insights through data science.
  • Master critical data science skills.
  • Understand machine learning from top to bottom.
  • Replicate real-world situations and data reports.
  • Learn numpy for numerical processing with python.
  • Conduct feature engineering on real world case studies.
  • Learn pandas for data manipulation with python.
  • Create supervised machine learning algorithms to predict classes.
  • Learn matplotlib to create fully customized data visualizations with python.
  • Create regression machine learning algorithms for predicting continuous values.
  • Learn seaborn to create beautiful statistical plots with python.
  • Construct a modern portfolio of data science and machine learning resume projects.
  • Learn how to use scikit-learn to apply powerful machine learning algorithms.
  • Get set-up quickly with the anaconda data science stack environment.
  • Learn best practices for real-world data sets.
  • Understand the full product workflow for the machine learning lifecycle.
  • Explore how to deploy your machine learning models as interactive apis.
  • Show more
  • Show less

Syllabus

In this section we will get you set up with everything you need for the course!
Welcome to the Course!
COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP!
Read more
Anaconda Python and Jupyter Install and Setup
Note on Environment Setup - Please read me!
Environment Setup
Need a quick refresher on Python? No problem! Welcome the crash course for basic Python
OPTIONAL: Python Crash Course
Python Crash Course - Part One
Python Crash Course - Part Two
Python Crash Course - Part Three
Python Crash Course - Exercise Questions
Python Crash Course - Exercise Solutions
Let's quickly get an overview about data science and machine learning in general!
Machine Learning Pathway
Let's learn about Numpy - a numerical processing library for Python that many other python libraries are based on.

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

NumPy Arrays
Coding Exercise Check-in: Creating NumPy Arrays
NumPy Indexing and Selection
Coding Exercise Check-in: Selecting Data from Numpy Array
NumPy Operations
Check-In: Operations on NumPy Array
NumPy Exercises
Numpy Exercises - Solutions
Let's learn how to use Pandas for data wrangling and analysis!
Introduction to Pandas
Series - Part One
Check-in: Labeled Index in Pandas Series
Series - Part Two
DataFrames - Part One - Creating a DataFrame
DataFrames - Part Two - Basic Properties
DataFrames - Part Three - Working with Columns
DataFrames - Part Four - Working with Rows
Pandas - Conditional Filtering
Pandas - Useful Methods - Apply on Single Column
Pandas - Useful Methods - Apply on Multiple Columns
Pandas - Useful Methods - Statistical Information and Sorting
Missing Data - Overview
Missing Data - Pandas Operations
GroupBy Operations - Part One
GroupBy Operations - Part Two - MultiIndex
Combining DataFrames - Concatenation
Combining DataFrames - Inner Merge
Combining DataFrames - Left and Right Merge
Combining DataFrames - Outer Merge
Pandas - Text Methods for String Data
Pandas - Time Methods for Date and Time Data
Pandas Input and Output - CSV Files
Pandas Input and Output - HTML Tables
Pandas Input and Output - Excel Files
Pandas Input and Output - SQL Databases
Pandas Pivot Tables
Pandas Project Exercise Overview
Pandas Project Exercise Solutions
Learn how to create visualizations for our data with Matplotlib and Python!
Introduction to Matplotlib
Matplotlib Basics
Matplotlib - Understanding the Figure Object
Matplotlib - Implementing Figures and Axes
Matplotlib - Figure Parameters
Matplotlib - Subplots Functionality
Matplotlib Styling - Legends
Matplotlib Styling - Colors and Styles
Advanced Matplotlib Commands (Optional)
Matplotlib Exercise Questions Overview
Matplotlib Exercise Questions - Solutions
Learn to use the popular Python library Seaborn for creating amazing data visualizations!
Introduction to Seaborn
Scatterplots with Seaborn
Distribution Plots - Part One - Understanding Plot Types
Distribution Plots - Part Two - Coding with Seaborn
Categorical Plots - Statistics within Categories - Understanding Plot Types
Categorical Plots - Statistics within Categories - Coding with Seaborn
Categorical Plots - Distributions within Categories - Understanding Plot Types
Categorical Plots - Distributions within Categories - Coding with Seaborn
Seaborn - Comparison Plots - Understanding the Plot Types
Seaborn - Comparison Plots - Coding with Seaborn
Seaborn Grid Plots
Seaborn - Matrix Plots
Seaborn Plot Exercises Overview
Seaborn Plot Exercises Solutions
Let's test your new python data science and data visualization skills with a full project!
Capstone Project Overview
Capstone Project Solutions - Part One
Capstone Project Solutions - Part Two
Capstone Project Solutions - Part Three
Let's learn about critical overview concepts concerning Machine Learning. No coding here! All important theory behind Machine Learning!
Introduction to Machine Learning Overview Section
Why Machine Learning?
Types of Machine Learning Algorithms
Supervised Machine Learning Process
Companion Book - Introduction to Statistical Learning
Let's begin our first Machine Learning algorithm with Python, Linear Regression. We'll cover many additional topics as well!
Introduction to Linear Regression Section
Linear Regression - Algorithm History
Linear Regression - Understanding Ordinary Least Squares
Linear Regression - Cost Functions
Linear Regression - Gradient Descent
Python coding Simple Linear Regression

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Covers advanced machine learning algorithms like DBSCAN and regularization methods, which are essential for staying current in the field
Includes model deployment, which is a crucial step in the machine learning lifecycle and often overlooked in introductory courses
Designed for learners who already know some Python, allowing them to quickly apply their existing skills to data science and machine learning tasks
Explores feature engineering on real-world case studies, a practical skill that is highly valued in the data science field
Includes a Python crash course, which may be helpful for learners who need a refresher on basic Python concepts
Uses Scikit-learn to apply powerful machine learning algorithms, which is a standard library in the field

Save this course

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Python for Machine Learning & Data Science Masterclass with these activities:
Review Linear Algebra Fundamentals
Strengthen your understanding of linear algebra concepts, which are foundational for many machine learning algorithms.
Browse courses on Linear Algebra
Show steps
  • Review matrix operations such as addition, subtraction, and multiplication.
  • Study vector spaces and linear transformations.
  • Practice solving systems of linear equations.
Read 'Python Data Science Handbook'
Deepen your understanding of core Python data science libraries.
View Python Data Science Handbook: Essential Tools... on Amazon
Show steps
  • Focus on chapters covering NumPy, Pandas, and Matplotlib.
  • Work through the examples and exercises in each chapter.
  • Experiment with different datasets and visualizations.
Read 'Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow'
Supplement your learning with a comprehensive guide to machine learning using Python.
View Hands-On Machine Learning with Scikit-Learn,... on Amazon
Show steps
  • Read the chapters relevant to the current course topics.
  • Work through the code examples and exercises.
  • Experiment with different parameters and datasets.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow Scikit-learn Tutorials
Enhance your skills by following official Scikit-learn tutorials on various machine learning algorithms and techniques.
Show steps
  • Visit the Scikit-learn website and find the tutorials section.
  • Choose a tutorial that covers a topic you want to learn more about.
  • Follow the tutorial step-by-step and run the code examples.
Build a Predictive Model for a Real-World Dataset
Apply your knowledge to a real-world problem by building a predictive model using Python and machine learning techniques.
Show steps
  • Find a suitable dataset from Kaggle or another source.
  • Preprocess the data using Pandas.
  • Train a machine learning model using Scikit-learn.
  • Evaluate the model's performance and fine-tune parameters.
Implement Machine Learning Algorithms from Scratch
Solidify your understanding of machine learning algorithms by implementing them from scratch without relying on libraries.
Show steps
  • Choose a machine learning algorithm, such as linear regression or decision trees.
  • Implement the algorithm using NumPy and Python.
  • Test the implementation on a sample dataset.
  • Compare the results with the output of Scikit-learn.
Create a Blog Post Explaining a Machine Learning Concept
Reinforce your understanding by explaining a machine learning concept in a clear and concise blog post.
Show steps
  • Choose a machine learning concept, such as regularization or clustering.
  • Research the concept and gather relevant information.
  • Write a blog post explaining the concept in simple terms.
  • Include examples and visualizations to illustrate the concept.

Career center

Learners who complete Python for Machine Learning & Data Science Masterclass will develop knowledge and skills that may be useful to these careers:
Data Scientist
A Data Scientist analyzes data to extract meaningful insights and solve complex problems. This position is the closest direct fit for the skills taught in this course. The course teaches Python with relevant libraries such as Pandas for data manipulation and NumPy for numerical processing, which are indispensable for a data scientist. It also covers data visualization with Matplotlib and Seaborn, which is crucial for exploratory data analysis and presenting findings. Most importantly, it delves into machine learning algorithms using Scikit-learn, a must-know for a data scientist. This course can help prepare a learner to perform the core tasks of a data scientist, from data cleaning to model building. The course also introduces the deployment of machine learning models, an essential part of a data scientist's toolchain.
See salaries and explore the career path for Data Scientist
Machine Learning Engineer
A Machine Learning Engineer builds and maintains machine learning systems. This role requires a strong understanding of machine learning algorithms, data processing, and software engineering practices, which this course provides. The course covers a wide variety of machine learning algorithms, including linear regression, regularization, clustering, and natural language processing. It also focuses on the practical application of these algorithms using Python libraries like Scikit-learn, NumPy, and Pandas, which are essential tools for a Machine Learning Engineer. Furthermore, the course teaches data visualization with Matplotlib and Seaborn, vital for understanding model performance and communicating results. A learner can use this course to build a comprehensive skill set needed for a machine learning engineering role, from data analysis to model deployment.
See salaries and explore the career path for Machine Learning Engineer
Data Analyst
A Data Analyst is responsible for collecting, processing, and presenting data to inform business decisions. A Data Analyst will use this course to build a strong foundation in Python for data manipulation, analysis, and visualization. The course's deep dives into Pandas and NumPy teach data cleaning and transformations, while Matplotlib and Seaborn provide the tools to create insightful charts and graphs. While the course covers machine learning, a Data Analyst will also be able to use these tools to understand data patterns and identify areas for improvement. This course may be helpful for any individual seeking a career as a data analyst, particularly because it places a large emphasis on using Python-based tools such as Numpy and Pandas.
See salaries and explore the career path for Data Analyst
Business Intelligence Analyst
A Business Intelligence Analyst uses data to help organizations make better strategic choices. This course helps build the foundation for the role by teaching data analysis using Python. The course's coverage of Pandas for data manipulation and Matplotlib and Seaborn for visualization directly applies to the tasks of a Business Intelligence Analyst. The course introduces many techniques for data processing that are integral to understanding the meaning behind metrics. This course may be useful to a Business Intelligence Analyst because it provides hands-on experience with Python-based analytics tools and helps build a foundation of data science.
See salaries and explore the career path for Business Intelligence Analyst
Research Scientist
A Research Scientist conducts experiments, analyzes data, and publishes findings. A Research Scientist leverages the tools of data science to explore information and uncover hidden patterns. The course provides a deep dive into Python programming for data analysis, including libraries like NumPy, Pandas, Matplotlib, and Seaborn, which are critical for data exploration and presentation. The course’s machine learning modules provide valuable tools for advanced data analysis and modeling. The course's coverage of the machine learning life cycle, from building to deployment, is also an advantage to a Research Scientist. This course may be useful for a research scientist looking to expand their skill set.
See salaries and explore the career path for Research Scientist
Quantitative Analyst
A Quantitative Analyst, often working in finance, develops and implements mathematical and statistical models. This role requires a strong background in programming, data analysis, and statistics, all of which are included in this course. The course's focus on Python, along with libraries like Numpy and Pandas, is a necessity for quantitative finance. The course explains many machine learning algorithms and techniques, which can be applied to build models. This course may be useful because it introduces regression, regularization, and clustering, all of which a quantitative analyst uses on a regular basis. The data wrangling and visualization skills learned in this course also help with understanding model output.
See salaries and explore the career path for Quantitative Analyst
Statistician
A Statistician collects, analyzes, and interprets quantitative data. Although this course does not focus on statistical theory, it does equip a statistician with the means to conduct data analysis. The course teaches visualization techniques with Matplotlib and Seaborn. It also teaches how to wrangle datasets using Pandas. The course’s emphasis on machine learning algorithms may be useful, as these are used for advanced statistical analysis. This course may be helpful to a statistician because it demonstrates how to practically leverage Python-based data analysis tools.
See salaries and explore the career path for Statistician
Data Engineer
A Data Engineer builds and maintains the infrastructure for data storage and processing. A Data Engineer would find this course helpful. While the course mainly focuses on data analysis and machine learning, the skills in Python programming and data manipulation using Pandas and NumPy are a good foundation for a Data Engineer. This course may be helpful to a data engineer because the data handling that is taught can help to build proficiency with data pipelines. This course may be useful to a data engineer.
See salaries and explore the career path for Data Engineer
Business Analyst
A Business Analyst identifies business needs and proposes solutions by using data. While this course is primarily focused on data science and machine learning with Python, a Business Analyst can use the skills to better understand data. The course's focus on data manipulation with Pandas, visualization with Matplotlib and Seaborn, are directly applicable to the work of a Business Analyst. While the course includes machine learning, a business analyst can benefit from the overall data analysis workflow. This course may be useful to a Business Analyst who wants to gain experience with Python tools.
See salaries and explore the career path for Business Analyst
Market Research Analyst
A Market Research Analyst studies consumer behavior and market trends to support sales and marketing initiatives. Although this course is primarily focused on data science, a Market Research Analyst may find the course helpful. The course focuses on data analysis skills with Python libraries such as Pandas and Numpy. The course also teaches data visualization using Matplotlib and Seaborn, which this role will use to present findings. This course may be helpful to a Market Research Analyst, who will benefit from the skills in Python-based data analysis.
See salaries and explore the career path for Market Research Analyst
Software Developer
A Software Developer writes the code that powers computer applications and systems. Although this course does not directly focus on general software development principles, it does teach Python programming, which is a core skill for a software developer. The course's coverage of libraries like NumPy and Pandas for data handling are also valuable. Additionally, the machine learning aspects of the course may be useful for developers working on machine learning applications. This course may be useful to a software developer who wishes to expand their Python expertise, particularly into data science.
See salaries and explore the career path for Software Developer
Financial Analyst
A Financial Analyst provides guidance to businesses by analyzing financial data. This course can help a financial analyst because it provides training with Python tools needed to perform data-driven analysis. This course does not focus on financial concepts. The course introduces ways to wrangle and explore data. These data analysis skills are helpful to a Financial Analyst. This course may be useful to a financial analyst who seeks to use Python-based tools.
See salaries and explore the career path for Financial Analyst
Operations Research Analyst
An Operations Research Analyst applies mathematical techniques to help organizations operate more efficiently. Although this role usually requires an advanced degree in operations research or a related field, this course can be useful for its emphasis on Python-based data analysis techniques. The course dives into methods using Pandas for data wrangling and NumPy for numerical processing. It also explores techniques for data visualization with Matplotlib and Seaborn. This course may be helpful to an Operations Research Analyst who would like to use Python.
See salaries and explore the career path for Operations Research Analyst
Project Manager
A Project Manager plans, executes, and closes projects to meet business goals. While this course focuses primarily on data science, a project manager can benefit from understanding the data-driven decisions process. The course teaches the full workflow for the machine learning lifecycle, and this knowledge can help a Project Manager better understand the needs of a project that involves data science. This course may be helpful to a project manager who works with teams that utilize Python-based data analysis tools.
See salaries and explore the career path for Project Manager
Database Administrator
A Database Administrator designs, implements, and maintains databases. This course will not teach database design or database theory, but it does introduce ways to interface with databases using Python. This course helps a learner develop programming skills using the Python language. This course may be helpful to a Database Administrator because it will allow them to use Python scripts to work with data. This course may be useful for a Database Administrator.
See salaries and explore the career path for Database Administrator

Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Python for Machine Learning & Data Science Masterclass.
Cover image
Hands-On Machine Learning with Scikit-Learn, Keras,...
Save
Provides a comprehensive overview of machine learning concepts and techniques using Python libraries like Scikit-learn and TensorFlow. It covers a wide range of topics, from basic algorithms to deep learning, making it an excellent resource for both beginners and experienced practitioners. The book includes practical examples and exercises that allow you to apply what you learn to real-world problems. It is commonly used as a textbook in machine learning courses.
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Kindle Edition
$$
Cover image
Python Data Science Handbook
Save
Comprehensive guide to the essential Python data science tools: NumPy, Pandas, Matplotlib, and Scikit-Learn. It provides a clear and concise introduction to each library, along with practical examples and exercises. It is particularly useful for understanding data manipulation, visualization, and basic machine learning techniques. This book valuable reference for anyone working with data in Python.
Python Data Science Handbook: Essential Tools for...
Paperback
$$$

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Effort
44 total hours
Via
Udemy
Instructors
Jose Portilla
Pierian Training
Language
English
Good to know
Covers advanced machine learning algorithms like DBSCAN and regularization methods, which are essential for staying current in the field
Includes model deployment, which is a crucial step in the machine learning lifecycle and often overlooked in introductory courses
Designed for learners who already know some Python, allowing them to quickly apply their existing skills to data science and machine learning tasks
Explores feature engineering on real-world case studies, a practical skill that is highly valued in the data science field
Includes a Python crash course, which may be helpful for learners who need a refresher on basic Python concepts
Uses Scikit-learn to apply powerful machine learning algorithms, which is a standard library in the field
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