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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.
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
Register for this course and see more details by visiting:
OpenCourser.com/course/zk3qqi/python
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
- 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
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
Get an overview of the NumPy topics we will discuss in this course! Numpy is a key part of data science and machine learning.
Traffic lights
Traffic lights
Read about what's good
what should give you pause 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
Create your own learning path.
Save this course to your list so you can find it easily later.
Save
Save
Reviews summary
Comprehensive python data science & ml
According to learners, this course is a highly comprehensive and practical introduction to using Python for Data Science and Machine Learning. Many students praise the depth of coverage across essential libraries like NumPy, Pandas, Matplotlib, and Seaborn, noting that the hands-on projects and real-world data examples are particularly valuable for building a portfolio and applying skills immediately. The instructor's explanations are frequently described as clear and easy to follow. However, some reviewers mention that the course might assume a stronger Python background than advertised, making the initial sections challenging for true beginners. While largely positive, a few notes suggest that occasional sections could benefit from updates to reflect the latest library versions or techniques, highlighting a minor warning for those seeking the absolute bleeding edge.
Instructor makes complex topics accessible.
"Jose Portilla does a great job explaining complex concepts clearly."
"His teaching style is engaging and easy to follow."
"The way the lectures broke down algorithms was very helpful."
"I appreciated the step-by-step approach to coding demos."
Great for building a portfolio.
"The projects were fantastic for applying what I learned and building my portfolio."
"Really enjoyed working through the practical examples and case studies."
"Doing the hands-on coding and projects solidified my understanding significantly."
"The practical side of this course, especially the capstone, is its biggest strength."
Wide array of topics covered.
"This course covers a huge amount of ground, from basic Python libraries to advanced ML algorithms."
"I felt I got a very comprehensive overview of the entire data science ecosystem in Python."
"The course touches upon almost every major tool and technique needed for a data science role."
"Covers all the essential libraries like Pandas, NumPy, Scikit-learn in detail."
Some parts may need updates.
"A few libraries or methods shown seem slightly older than current best practices."
"In a fast-moving field, keeping everything perfectly up-to-date is hard, but some lectures felt a bit dated."
"Might need to check documentation for the very latest versions of libraries used."
"The core concepts are sound, but specific implementation details can change."
Deeper dives may need external study.
"While comprehensive, some topics are covered lightly and require further reading."
"I needed to look up external resources for a deeper understanding of certain algorithms."
"Good foundation, but not enough to be an expert without additional study."
"The theory sections could sometimes benefit from more detail."
May be challenging for beginners.
"Despite the description, you really need more than just 'some' Python knowledge going in."
"The pace is quite fast, especially if you're not already comfortable with basic Python structures."
"True beginners might struggle without supplementary Python resources first."
"I wish the course had a slower ramp-up for the Python crash course part."
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
Show steps
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'
Show steps
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'
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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.
Review Linear Algebra Fundamentals
Show steps
Strengthen your understanding of linear algebra concepts, which are foundational for many machine learning algorithms.
Browse courses on
Linear Algebra
Show steps
Show steps
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'
Show steps
Deepen your understanding of core Python data science libraries.
View
Python Data Science Handbook: Essential Tools...
on Amazon
Show steps
Show steps
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'
Show steps
Supplement your learning with a comprehensive guide to machine learning using Python.
View
Hands-On Machine Learning with Scikit-Learn,...
on Amazon
Show steps
Show steps
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
Show steps
Enhance your skills by following official Scikit-learn tutorials on various machine learning algorithms and techniques.
Show steps
Show steps
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
Show steps
Apply your knowledge to a real-world problem by building a predictive model using Python and machine learning techniques.
Show steps
Show steps
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
Show steps
Solidify your understanding of machine learning algorithms by implementing them from scratch without relying on libraries.
Show steps
Show steps
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
Show steps
Reinforce your understanding by explaining a machine learning concept in a clear and concise blog post.
Show steps
Show steps
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
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.
Machine Learning Engineer
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.
Data Analyst
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.
Business Intelligence 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.
Research Scientist
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.
Quantitative Analyst
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.
Statistician
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.
Data Engineer
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.
Business Analyst
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.
Market Research 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.
Software Developer
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.
Financial Analyst
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.
Operations Research 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.
Project Manager
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.
Database Administrator
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.
For more career information including salaries, visit:
OpenCourser.com/course/zk3qqi/python
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.
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.
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.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/zk3qqi/python
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