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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.

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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.
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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'
This book provides a comprehensive overview of Machine Learning algorithms and their implementation using popular Python libraries.
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
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
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
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
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
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
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
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
Tutorials will provide in-depth explanations and examples of advanced 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
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.

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.
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.
A comprehensive textbook on machine learning in Chinese.
A classic textbook on machine learning, providing a rigorous mathematical foundation for the field.
Provides a comprehensive overview of statistical learning methods, including machine learning algorithms.
Covers the fundamentals of deep learning, including neural networks, convolutional neural networks, and recurrent neural networks.
Provides a wide range of recipes for solving machine learning problems using Python. Covers fundamental concepts as well as more advanced topics.
An introductory guide to machine learning with Python, suitable for beginners.

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