Data Science and Machine Learning in Python: Linear models from Udemy

Why study data science?

Companies have a problem: they collect and store huge amounts of data on a daily basis. The problem is that they don't have the tools and capabilities to extract knowledge and make decisions from that data. But that is changing. For some years now, the demand for data scientists has grown exponentially. So much so, that the number of people with these skills is not enough to fill all the job openings. A basic search on Glassdoor or Indeed will reveal to you why data scientist salaries have grown so much in recent years.

Why this course?

Almost every course out there is either too theoretical or too practical. University courses don't usually develop the skills needed to tackle data science problems from scratch, nor do they teach you how to use the necessary software fluently. On the other hand, many online courses and bootcamps teach you how to use these techniques without getting a deep understanding of them, going through the theory superficially.

Our course combines the best of each method. On the one hand, we will look at where these methods come from and why they are used, understanding why they work the way they do. On the other, we will program these methods from scratch, using the most popular data science and machine learning libraries in Python. Only when you have understood exactly how each algorithm works, we will learn how to use them with advanced Python libraries.

Course content

Introduction to machine learning and data science.
Simple linear regression. We will learn how to study the relationship between different phenomena.
Multiple linear regression. We will create models with more than one variable to study the behavior of a variable of interest.
Lasso regression. Advanced version of multiple linear regression with the ability to filter the most useful variables.
Ridge regression. A more stable version of multiple linear regression.
Logistic regression. Most popular classification and detection algorithm. It will allow us to study the relationship between different variables and certain object classes.
Poisson regression. Algorithm that will allow us to see how several variables affect the number of times an event occurs.
Central concepts in data science (overfitting vs underfitting, cross-validation, variable preparation, etc).

Any questions? Remember that we have a 30-day full money-back guarantee. No risk for you. That's how convinced we are that you will love the course.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Covers concepts that will help learners connect their studies to the real world

Includes regression models and algorithms that are in high demand in the professional workforce

Provides a deep understanding of machine learning and data science methodologies

Will help learners become proficient in programming and using Python for data science

Course advisors offer a 30-day full money-back guarantee

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 Data Science and Machine Learning in Python: Linear models with these activities:

Review Linear Algebra and Calculus

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Strengthens your foundational understanding of mathematics, which is essential for grasping the concepts and algorithms in this course.

Browse courses on Linear Algebra

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Review key concepts in linear algebra, such as vectors, matrices, and eigenvectors.
Brush up on calculus, focusing on derivatives and integrals.

Read: Introduction to Machine Learning, 4th Edition

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Provides a comprehensive overview of the fundamental concepts and algorithms of machine learning, preparing you for success in this course.

View Machine Learning, revised and updated edition on Amazon

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Read Chapters 1 and 2 to understand the basics of machine learning and supervised learning.
Complete the exercises at the end of each chapter to test your understanding.

Read: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd Edition

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Provides a practical introduction to machine learning using popular libraries, complementing the theoretical concepts covered in the course.

View Hands-On Machine Learning with Scikit-Learn,... on Amazon

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Read Chapters 1-3 to understand the basics of machine learning, data preprocessing, and model evaluation.
Complete the exercises at the end of each chapter to reinforce your understanding.

Five other activities

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Join a Study Group

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Provides a collaborative learning environment where you can discuss concepts, solve problems, and learn from others, enhancing your understanding and retention.

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Find a study group or create your own with classmates.
Meet regularly to discuss course material, work on assignments, and prepare for exams.

Follow the scikit-learn Tutorial

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Provides hands-on experience with the scikit-learn library, the industry-standard tool for machine learning in Python, complementing the theoretical concepts covered in the course.

Browse courses on scikit-learn

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Go through the scikit-learn tutorial.
Follow along with the examples and try out the code snippets.

Build a Simple Linear Regression Model

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Allows you to apply the concepts of simple linear regression to a real-world dataset, reinforcing your understanding and developing practical skills.

Browse courses on Simple Linear Regression

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Choose a dataset with a continuous target variable.
Use scikit-learn to fit a simple linear regression model to the data.
Evaluate the model's performance using metrics like R2 and MSE.

Kaggle Competitions: Beginner Level

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Offers a practical and engaging way to test your skills, learn from others, and gain recognition in the data science community.

Browse courses on Kaggle Competitions

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Join Kaggle and explore beginner-level competitions.
Choose a competition that aligns with your interests and skill level.
Work on the competition, experimenting with different approaches and techniques.

Contribute to Open Source Machine Learning Projects

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Enhances your understanding of real-world machine learning applications, exposes you to different perspectives, and builds your portfolio.

Browse courses on Community Involvement

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Find open source machine learning projects on platforms like GitHub.
Identify issues or areas where you can contribute.
Fork the repository, make your changes, and submit a pull request.

Career center

Learners who complete Data Science and Machine Learning in Python: Linear models will develop knowledge and skills that may be useful to these careers:

Quantitative Analyst

Quantitative Analysts use data to make financial decisions. They collect, clean, and analyze data to identify trends and patterns. They then use these insights to make recommendations to investors. This course will help you develop the skills you need to become a successful Quantitative Analyst. You will learn how to use Python to collect, clean, and analyze data. You will also learn how to use financial models to make investment decisions. This course is essential for anyone who wants to start or advance a career in Quantitative Finance.

See salaries and explore the career path for Quantitative Analyst

Machine Learning Engineer

Machine Learning Engineers build and maintain machine learning models. They work closely with Data Scientists to identify the right problems to solve with machine learning. They then design and implement machine learning algorithms. This course will help you develop the skills you need to become a successful Machine Learning Engineer. You will learn how to use Python to build and maintain machine learning models. You will also learn how to evaluate the performance of machine learning models. This course is essential for anyone who wants to start or advance a career in Machine Learning Engineering.

See salaries and explore the career path for Machine Learning Engineer

Business Analyst

Business Analysts use data to make business decisions. They collect, clean, and analyze data to identify trends and patterns. They then use these insights to make recommendations to businesses. This course will help you develop the skills you need to become a successful Business Analyst. You will learn how to use Python to collect, clean, and analyze data. You will also learn how to use business analysis techniques to make business decisions. This course is essential for anyone who wants to start or advance a career in Business Analysis.

See salaries and explore the career path for Business Analyst

Market Researcher

Market Researchers use data to understand consumer behavior. They collect, clean, and analyze data to identify trends and patterns. They then use these insights to make recommendations to businesses. This course will help you develop the skills you need to become a successful Market Researcher. You will learn how to use Python to collect, clean, and analyze data. You will also learn how to use market research techniques to understand consumer behavior. This course is essential for anyone who wants to start or advance a career in Market Research.

See salaries and explore the career path for Market Researcher

Data Analyst

Data Analysts use data to make informed decisions. They collect, clean, and analyze data to identify trends and patterns. They then use these insights to make recommendations to businesses. This course will help you develop the skills you need to become a successful Data Analyst. You will learn how to use Python to collect, clean, and analyze data. You will also learn how to use data visualization techniques to present your findings. This course is essential for anyone who wants to start or advance a career in Data Analytics.

See salaries and explore the career path for Data Analyst

Data Scientist

Data Scientists use data to solve complex business problems. They collect, clean, and analyze data to identify trends and patterns. They then use these insights to make recommendations and predictions. This course will help you develop the skills you need to become a successful Data Scientist. You will learn how to use Python to collect, clean, and analyze data. You will also learn how to use machine learning algorithms to build models that can predict future events. This course is essential for anyone who wants to start or advance a career in Data Science.

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Data Engineer

Data Engineers build and maintain data pipelines. They collect, clean, and analyze data to make it available to data scientists and other users. This course will help you develop the skills you need to become a successful Data Engineer. You will learn how to use Python to collect, clean, and analyze data. You will also learn how to build and maintain data pipelines. This course is essential for anyone who wants to start or advance a career in Data Engineering.

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Operations Research Analyst

Operations Research Analysts use data to improve business operations. They collect, clean, and analyze data to identify inefficiencies. They then use these insights to make recommendations to businesses. This course will help you develop the skills you need to become a successful Operations Research Analyst. You will learn how to use Python to collect, clean, and analyze data. You will also learn how to use operations research models to improve business operations. This course is essential for anyone who wants to start or advance a career in Operations Research.

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Actuary

Actuaries use data to assess risk. They collect, clean, and analyze data to identify trends and patterns. They then use these insights to make recommendations to insurance companies. This course will help you develop the skills you need to become a successful Actuary. You will learn how to use Python to collect, clean, and analyze data. You will also learn how to use actuarial models to assess risk. This course is essential for anyone who wants to start or advance a career in Actuarial Science.

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Statistician

Statisticians use data to make inferences about the world. They collect, clean, and analyze data to identify trends and patterns. They then use these insights to make predictions and recommendations. This course will help you develop the skills you need to become a successful Statistician. You will learn how to use Python to collect, clean, and analyze data. You will also learn how to use statistical methods to make inferences about the world. This course is essential for anyone who wants to start or advance a career in Statistics.

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Risk Analyst

Risk Analysts use data to assess risk. They collect, clean, and analyze data to identify trends and patterns. They then use these insights to make recommendations to businesses. This course will help you develop the skills you need to become a successful Risk Analyst. You will learn how to use Python to collect, clean, and analyze data. You will also learn how to use risk assessment models to assess risk. This course is essential for anyone who wants to start or advance a career in Risk Analysis.

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Financial Analyst

Financial Analysts use data to make financial decisions. They collect, clean, and analyze data to identify trends and patterns. They then use these insights to make recommendations to investors. This course will help you develop the skills you need to become a successful Financial Analyst. You will learn how to use Python to collect, clean, and analyze data. You will also learn how to use financial models to make investment decisions. This course is essential for anyone who wants to start or advance a career in Financial Analysis.

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Data Scientist Intern

Data Scientist Interns work under the supervision of experienced Data Scientists. They help to collect, clean, and analyze data. They also help to build and maintain machine learning models. This course will help you to develop the skills you need to become a successful Data Scientist Intern. You will learn how to use Python to collect, clean, and analyze data. You will also learn how to build and maintain machine learning models. This course is essential for anyone who wants to start or advance a career in Data Science.

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Data Journalist

Data Journalists use data to tell stories. They collect, clean, and analyze data to identify trends and patterns. They then use these insights to write articles, blog posts, and other forms of media. This course will help you develop the skills you need to become a successful Data Journalist. You will learn how to use Python to collect, clean, and analyze data. You will also learn how to use data visualization techniques to tell stories. This course is essential for anyone who wants to start or advance a career in Data Journalism.

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Software Engineer

Software Engineers use data to build software. They collect, clean, and analyze data to identify user needs. They then use these insights to design and build software. This course will help you develop the skills you need to become a successful Software Engineer. You will learn how to use Python to collect, clean, and analyze data. You will also learn how to use software development tools to build software. This course is essential for anyone who wants to start or advance a career in Software Engineering.

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