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Machine Learning with Python

SAEED AGHABOZORGI and Joseph Santarcangelo

Get ready to dive into the world of Machine Learning (ML) by using Python! This course is for you whether you want to advance your Data Science career or get started in Machine Learning and Deep Learning.

This course will begin with a gentle introduction to Machine Learning and what it is, with topics like supervised vs unsupervised learning, linear & non-linear regression, simple regression and more.

You will then dive into classification techniques using different classification algorithms, namely K-Nearest Neighbors (KNN), decision trees, and Logistic Regression. You’ll also learn about the importance and different types of clustering such as k-means, hierarchical clustering, and DBSCAN.

With all the many concepts you will learn, a big emphasis will be placed on hands-on learning. You will work with Python libraries like SciPy and scikit-learn and apply your knowledge through labs. In the final project you will demonstrate your skills by building, evaluating and comparing several Machine Learning models using different algorithms.

By the end of this course, you will have job ready skills to add to your resume and a certificate in machine learning to prove your competency.

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What's inside

Syllabus

Introduction to Machine Learning

In this module, you will learn about applications of Machine Learning in different fields such as health care, banking, telecommunication, and so on. You’ll get a general overview of Machine Learning topics such as supervised vs unsupervised learning, and the usage of each algorithm. Also, you understand the advantage of using Python libraries for implementing Machine Learning models.

Regression

In this module, you will get a brief intro to regression. You learn about Linear, Non-linear, Simple and Multiple regression, and their applications. You apply all these methods on two different datasets, in the lab part. Also, you learn how to evaluate your regression model, and calculate its accuracy.

Classification

In this module, you will learn about classification technique. You practice with different classification algorithms, such as KNN, Decision Trees, Logistic Regression and SVM. Also, you learn about pros and cons of each method, and different classification accuracy metrics.

Linear Classification

Clustering

In this module, you will learn about clustering specifically k-means clustering. You learn how the k-means clustering algorithm works and how to use k-means clustering for customer segmentation.

Final Exam and Project

In this module, you will do a project based of what you have learned so far. You will submit a report of your project for peer evaluation.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Provides an introduction to machine learning that is suitable for learners with little to no prior experience with the topic

Covers a range of machine learning algorithms and techniques, including supervised and unsupervised learning, classification, and clustering

Provides hands-on experience with machine learning libraries such as SciPy and scikit-learn

Guided by instructors, Joseph Santarcangelo and SAEED AGHABOZORGI, who have a strong reputation in the field of machine learning

Focuses on building practical skills that are in high demand in the industry

Designed for learners who want to advance their data science career or get started in machine learning and deep learning

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Reviews summary

Machine learning with python

Learners say this intermediate-level online course provides a comprehensive introduction to machine learning with Python, offering clear explanations, hands-on labs, and a challenging final project. According to students, the course covers: * **Core concepts:** Supervised and unsupervised learning, linear and non-linear regression, classification algorithms, decision trees, logistic regression, support vector machines, recommendation engines * **Model evaluation:** Accuracy, precision, recall, F1 score, MSE, RMSE, MAE, log loss, gradient descent, cost function * **Hands-on implementation:** Using libraries like NumPy, pandas, sciPy, scikit-learn for data manipulation, model building, and evaluation Students praise the video lectures for their clarity and the ungraded labs for their helpfulness in understanding the material. However, some note that the final project is challenging and requires a good understanding of the concepts covered in the course, and they suggest supplementing the course with other resources for a more thorough understanding of the material. Overall, learners say this course is a valuable resource for those interested in learning the basics of machine learning with Python, and they recommend it to beginners and experienced data scientists alike.

This course effectively introduces essential concepts like linear regression, logistic regression, decision trees, and support vector machines, providing a solid foundation in supervised learning.

"This course effectively introduces essential concepts like linear regression, logistic regression, decision trees, and support vector machines, providing a solid foundation in supervised learning."

The course delves into various machine learning domains, including classification, regression, and clustering, exposing learners to a wide range of applications.

"The course delves into various machine learning domains, including classification, regression, and clustering, exposing learners to a wide range of applications."

Interactive exercises and graded quizzes help reinforce concepts and assess understanding, making the learning process more engaging and effective.

"Interactive exercises and graded quizzes help reinforce concepts and assess understanding, making the learning process more engaging and effective."

Ungraded labs provide opportunities to practice implementing machine learning algorithms, enhancing comprehension and solidifying the learning process.

"Ungraded labs provide opportunities to practice implementing machine learning algorithms, enhancing comprehension and solidifying the learning process."

The final project challenges learners to apply their acquired knowledge to a real-world scenario, fostering practical problem-solving skills.

"The final project challenges learners to apply their acquired knowledge to a real-world scenario, fostering practical problem-solving skills."

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 Machine Learning with Python with these activities:

Review the book 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow'

Show steps

This book provides a comprehensive overview of Machine Learning algorithms and their implementation using popular Python libraries.

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

Show steps

Read the book and take notes
Work through the exercises and examples
Review the key concepts and algorithms

Reach out to experts or professionals in the Machine Learning field

Show steps

Connecting with experts can provide valuable insights, guidance, and support in your Machine Learning journey.

Browse courses on Machine Learning

Show steps

Identify experts or professionals in the field
Reach out to them via email or social media
Request a meeting or mentorship opportunity

Join a study group or online forum for Machine Learning

Show steps

Participating in peer sessions will provide opportunities to collaborate with others, ask questions, and gain different perspectives on Machine Learning concepts.

Browse courses on Machine Learning

Show steps

Find a study group or online forum
Introduce yourself and participate in discussions
Ask questions and help others

Seven other activities

Expand to see all activities and additional details

Show all ten activities

Clustering Techniques Tutorial

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Expand your knowledge of clustering algorithms through guided tutorials.

Browse courses on Clustering

Show steps

Identify different clustering techniques and their applications.
Follow step-by-step tutorials to implement clustering algorithms in Python.
Interpret the results of clustering algorithms and evaluate their effectiveness.

Practice Python coding challenges

Show steps

Coding challenges will provide essential practice in applying the concepts learned in the course.

Browse courses on Python

Show steps

Find online Python coding challenges
Solve the problems using Python
Review your solutions

Linear Regression Practice

Show steps

Enhance your proficiency in linear regression techniques through repetitive exercises.

Browse courses on Linear Regression

Show steps

Solve linear regression problems using the sklearn library in Python.
Practice implementing linear regression algorithms from scratch.
Apply linear regression models to real-world datasets.

Contribute to open-source Machine Learning projects

Show steps

Contributing to open-source projects will provide hands-on experience and allow you to learn from others in the Machine Learning community.

Browse courses on Machine Learning

Show steps

Find open-source Machine Learning projects on platforms like GitHub
Identify a project that interests you and aligns with your skills
Contribute code, documentation, or other resources to the project

Classification Model Development

Show steps

Solidify your understanding of classification models by creating and evaluating your own.

Browse courses on Classification

Show steps

Gather and prepare a dataset suitable for classification.
Develop classification models using various algorithms, such as K-NN, decision trees, and logistic regression.
Evaluate the performance of your models using appropriate metrics.
Compare and contrast the performance of different classification models.

Follow tutorials on advanced Machine Learning algorithms

Show steps

Tutorials will provide in-depth explanations and examples of advanced Machine Learning algorithms.

Browse courses on Machine Learning Algorithms

Show steps

Identify the advanced Machine Learning algorithms you want to learn
Find tutorials or online courses on those algorithms
Follow the tutorials and complete the exercises

Create a blog post or article on a Machine Learning topic

Show steps

Creating content will reinforce your understanding of Machine Learning concepts and improve your communication skills.

Browse courses on Machine Learning

Show steps

Choose a Machine Learning topic to write about
Research the topic and gather information
Write the blog post or article
Publish and promote your content

Career center

Learners who complete Machine Learning with Python will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers leverage the power of algorithms, models, and data to make better predictions. Their expertise spans statistical analysis, model training, and the communication of findings. To enter this competitive, in-demand field, it is essential to have a solid foundation in machine learning techniques and their practical application. This course provides that foundation by introducing you to essential supervised and unsupervised learning algorithms, regression techniques, and clustering methods. Through hands-on exercises, you will gain proficiency in Python libraries like SciPy and scikit-learn, equipping you with the skills necessary to build and deploy powerful machine learning models.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists are the alchemists of the 21st century, transforming vast amounts of data into actionable insights. They use machine learning algorithms to uncover patterns, predict outcomes, and make data-driven decisions. This course lays the groundwork for a successful career as a Data Scientist by providing a comprehensive introduction to machine learning. You will learn how to use Python libraries like SciPy and scikit-learn to implement machine learning models, evaluate their performance, and communicate your findings effectively.

See salaries and explore the career path for Data Scientist

Data Analyst

Data Analysts are the unsung heroes of the digital age, turning raw data into actionable insights that drive decision-making. They use machine learning techniques to identify trends, patterns, and anomalies in data, enabling businesses to gain a competitive edge. This course provides a solid foundation for aspiring Data Analysts by introducing them to essential machine learning concepts and algorithms. Through hands-on exercises and practical examples, you will learn how to leverage machine learning to extract valuable insights from data.

See salaries and explore the career path for Data Analyst

Quantitative Analyst

Quantitative Analysts are the financial industry's secret weapon, using mathematical and statistical models to assess risk, predict market trends, and make investment decisions. To succeed in this highly competitive field, a strong foundation in machine learning is essential. This course provides that foundation by introducing you to supervised and unsupervised learning algorithms, regression techniques, and clustering methods. Through practical examples and hands-on exercises, you will gain the skills necessary to build and deploy machine learning models to solve real-world financial problems.

See salaries and explore the career path for Quantitative Analyst

Software Engineer

Software Engineers are the architects of the digital world, building and maintaining the software systems that power our lives. Machine learning is increasingly becoming an essential tool for Software Engineers, enabling them to develop more intelligent and efficient applications. This course provides a solid foundation in machine learning for Software Engineers by introducing them to essential algorithms, techniques, and best practices. Through hands-on exercises and practical examples, you will learn how to integrate machine learning into your software development process.

See salaries and explore the career path for Software Engineer

Business Analyst

Business Analysts are the bridge between the business world and the world of technology, helping organizations understand and solve their business problems. Machine learning is becoming increasingly important for Business Analysts, enabling them to make data-driven decisions and identify opportunities for growth. This course provides a comprehensive introduction to machine learning for Business Analysts by introducing them to essential algorithms, techniques, and best practices. Through hands-on exercises and practical examples, you will learn how to leverage machine learning to gain insights from data and drive business decisions.

See salaries and explore the career path for Business Analyst

Product Manager

Product Managers are the visionaries behind successful products, shaping their development and ensuring they meet customer needs. Machine learning is increasingly becoming an essential tool for Product Managers, enabling them to understand user behavior, personalize the user experience, and make data-driven decisions. This course provides a solid foundation in machine learning for Product Managers by introducing them to essential algorithms, techniques, and best practices. Through hands-on exercises and practical examples, you will learn how to leverage machine learning to build better products and drive user engagement.

See salaries and explore the career path for Product Manager

Marketing Analyst

Marketing Analysts are the storytellers of the marketing world, using data to understand customer behavior and drive marketing campaigns. Machine learning is becoming increasingly important for Marketing Analysts, enabling them to segment customers, personalize marketing messages, and measure the effectiveness of marketing campaigns. This course provides a comprehensive introduction to machine learning for Marketing Analysts by introducing them to essential algorithms, techniques, and best practices. Through hands-on exercises and practical examples, you will learn how to leverage machine learning to gain insights from data and drive marketing success.

See salaries and explore the career path for Marketing Analyst

Operations Research Analyst

Operations Research Analysts are the problem-solvers of the business world, using mathematical and analytical techniques to improve efficiency and optimize operations. Machine learning is becoming increasingly important for Operations Research Analysts, enabling them to develop more sophisticated models and solve more complex problems. This course provides a solid foundation in machine learning for Operations Research Analysts by introducing them to essential algorithms, techniques, and best practices. Through hands-on exercises and practical examples, you will learn how to leverage machine learning to improve decision-making and drive operational excellence.

See salaries and explore the career path for Operations Research Analyst

Financial Analyst

Financial Analysts are the gatekeepers of the financial world, providing insights and recommendations to investors and businesses. Machine learning is becoming increasingly important for Financial Analysts, enabling them to analyze financial data, identify trends, and make more informed investment decisions. This course provides a comprehensive introduction to machine learning for Financial Analysts by introducing them to essential algorithms, techniques, and best practices. Through hands-on exercises and practical examples, you will learn how to leverage machine learning to gain insights from financial data and drive investment success.

See salaries and explore the career path for Financial Analyst

Risk Analyst

Risk Analysts are the watchdogs of the financial world, identifying and mitigating risks to protect organizations and investors. Machine learning is becoming increasingly important for Risk Analysts, enabling them to develop more sophisticated models and assess risks more accurately. This course provides a solid foundation in machine learning for Risk Analysts by introducing them to essential algorithms, techniques, and best practices. Through hands-on exercises and practical examples, you will learn how to leverage machine learning to identify and manage risks more effectively.

See salaries and explore the career path for Risk Analyst

Healthcare Analyst

Healthcare Analysts are the detectives of the healthcare world, using data to identify trends, improve patient care, and reduce costs. Machine learning is becoming increasingly important for Healthcare Analysts, enabling them to develop more accurate predictive models and identify new opportunities for improving patient outcomes. This course provides a comprehensive introduction to machine learning for Healthcare Analysts by introducing them to essential algorithms, techniques, and best practices. Through hands-on exercises and practical examples, you will learn how to leverage machine learning to gain insights from healthcare data and drive better patient care.

See salaries and explore the career path for Healthcare Analyst

Insurance Analyst

Insurance Analysts are the gatekeepers of the insurance industry, evaluating risks and determining premiums. Machine learning is becoming increasingly important for Insurance Analysts, enabling them to develop more accurate predictive models and assess risks more precisely. This course provides a solid foundation in machine learning for Insurance Analysts by introducing them to essential algorithms, techniques, and best practices. Through hands-on exercises and practical examples, you will learn how to leverage machine learning to improve underwriting decisions and drive profitability.

See salaries and explore the career path for Insurance Analyst

Statistician

Statisticians are the data detectives of the world, using statistical methods to analyze data, draw conclusions, and make predictions. Machine learning is becoming increasingly important for Statisticians, enabling them to develop more sophisticated models and analyze more complex data. This course provides a solid foundation in machine learning for Statisticians by introducing them to essential algorithms, techniques, and best practices. Through hands-on exercises and practical examples, you will learn how to leverage machine learning to gain insights from data and drive better decision-making.

See salaries and explore the career path for Statistician

Data Engineer

Data Engineers are the architects of the data world, building and maintaining the infrastructure that stores and processes data. Machine learning is becoming increasingly important for Data Engineers, enabling them to develop more efficient data pipelines and build more scalable data systems. This course provides a solid foundation in machine learning for Data Engineers by introducing them to essential algorithms, techniques, and best practices. Through hands-on exercises and practical examples, you will learn how to leverage machine learning to improve data management and drive data-driven insights.

See salaries and explore the career path for Data Engineer

Reading list

We've selected 14 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 Machine Learning with Python.

The Elements of Statistical Learning

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A comprehensive textbook on statistical learning, covering a wide range of topics including machine learning.

The Elements of Statistical Learning: Data Mining,...

Hardcover

$$$

The Elements of Statistical Learning: Data Mining,...

Hardcover

$$$

The Elements of Statistical Learning: Data Mining,...

Kindle Edition

$$$

Hands-On Machine Learning with Scikit-Learn, Keras,...

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A comprehensive guide to machine learning with Python, using popular libraries like Scikit-Learn, Keras, and TensorFlow. Covers a wide range of topics, from data preprocessing to model evaluation.

Machine Learning with Python

Three deals to help you save

What's inside

Syllabus

Good to know

Save this course

Reviews summary

Machine learning with python

Activities

Career center

Reading list

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