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Anna Koop

This course takes you from understanding the fundamentals of a machine learning project. Learners will understand and implement supervised learning techniques on real case studies to analyze business case scenarios where decision trees, k-nearest neighbours and support vector machines are optimally used. Learners will also gain skills to contrast the practical consequences of different data preparation steps and describe common production issues in applied ML.

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This course takes you from understanding the fundamentals of a machine learning project. Learners will understand and implement supervised learning techniques on real case studies to analyze business case scenarios where decision trees, k-nearest neighbours and support vector machines are optimally used. Learners will also gain skills to contrast the practical consequences of different data preparation steps and describe common production issues in applied ML.

To be successful, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode).

This is the second course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute.

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What's inside

Syllabus

Classification using Decision Trees and k-NN
Welcome to Supervised Learning, Tip to Tail! This week we'll go over the basics of supervised learning, particularly classification, as well as teach you about two classification algorithms: decision trees and k-NN. You'll get started programming on the platform through Jupyter notebooks and start to familiarize yourself with all the issues that arise when using machine learning for classification.
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Functions for Fun and Profit
Welcome to the second week of the course! In this week you'll learn all about regression algorithms, the other side of supervised learning. We'll introduce you to the idea of finding lines, optimization criteria, and all the associated issues. Through regression we'll see the interactions between model complexity and accuracy, and you'll get a first taste of how regression and classification might relate.
Regression for Classification: Support Vector Machines
This week we'll be diving straight in to using regression for classification. We'll describe all the fundamental pieces that make up the support vector machine algorithms, so that you can understand how many seemingly unrelated machine learning algorithms tie together. We'll introduce you to logistic regression, neural networks, and support vector machines, and show you how to implement two of those.
Contrasting Models
Now at the tail end of the course, we're going to go over how to know how well your model is actually performing and what you can do to get even better performance from it. We'll review assessment questions particular to regression and classification, and introduce some other tools that really help you analyze your model performance. The topics covered this week aim to give you confidence in your models, so you're ready to unlock the power of machine learning for your business goals.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Offers hands-on labs, interactive materials, and projects
Helps learners understand and implement supervised learning
Employs real case studies, enhancing practical relevance
Covers essential data preparation steps and tackles common ML issues
Suitable for beginners with basic programming and math knowledge
Introduces industry-standard techniques with a focus on practicality

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Reviews summary

Relevant supervised ml algorithms

Learners say that Machine Learning Algorithms: Supervised Learning Tip to Tail is an instructional course that provides a foundation in Supervised Machine Learning. The course briefly covers commonly used supervised learning models and processes. According to students, Anna Koop, the instructor, is knowledgeable and has a clear teaching style. Most reviewers mention that the course is high-level and lacks hands-on practice exercises, but some don't mind. Others note that the video lectures are fast-paced and include complex theory that can be difficult for beginners to grasp. Despite these issues, learners say this course is a great option to gain a general understanding of the different supervised machine learning algorithms before diving into more advanced topics.
Provides a broad understanding of supervised machine learning algorithms.
"Excellent course for an overview of different ML algorithms."
"This is an excellent course which goes into some depth on the different ML models and underlying complexity but it avoids getting bogged down into the details too much."
"The course is made from a perspective of giving insights in process and not too many mathematical details."
Video lectures are delivered quickly with complex theory.
"The content was good but the videos went too fast and too much theory was involved."
"Dr. Koop speaks very very fast though."
Lacks hands-on practice exercises.
"This course is a great overview of ML concepts."
"I did not give 5 stars because the labs need to be improved."
"I am aware of the vastness of the field, and maybe that's why they kept the course instructor centered, but I still expect courses to push learners more in trying out the methods themselves and learning by doing."

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 Algorithms: Supervised Learning Tip to Tail with these activities:
Review linear algebra fundamentals
Linear algebra forms the mathematical foundation for many machine learning algorithms.
Browse courses on Linear Algebra
Show steps
  • Revisit basic linear algebra concepts (e.g., vectors, matrices, transformations)
  • Practice solving linear equations and matrix manipulations
Attend a workshop on applied machine learning
Hands-on workshops offer an immersive environment for practical application and expert guidance.
Browse courses on Machine Learning
Show steps
  • Research and identify relevant workshops
  • Register for a workshop that aligns with your learning goals
  • Actively participate in the workshop activities and discussions
Practice decision trees from scratch
Reinforce understanding of decision tree concepts by implementing them without external libraries.
Browse courses on Decision Trees
Show steps
  • Review the theory of decision trees
  • Write a function to calculate the entropy of a dataset
  • Write a function to split a dataset using a given attribute
  • Write a function to build a decision tree recursively
Five other activities
Expand to see all activities and additional details
Show all eight activities
Create a comprehensive study guide
Organizing and reviewing course materials reinforces knowledge and facilitates effective recall.
Browse courses on Supervised Learning
Show steps
  • Gather all relevant course materials (e.g., notes, assignments, slides)
  • Identify key concepts and organize them logically
  • Summarize and condense the information into a concise and structured format
Join a study group for supervised learning
Engaging with peers through discussions and shared problem-solving fosters deeper understanding.
Browse courses on Supervised Learning
Show steps
  • Find or create a study group with like-minded individuals
  • Establish regular meeting times and create a study schedule
  • Collaborate on solving problems, discussing concepts, and sharing resources
Develop a machine learning model using k-NN
Practical experience in building a k-NN model will solidify your understanding and prepare you for real-world applications.
Browse courses on K-Nearest Neighbors
Show steps
  • Choose a dataset and define the problem statement
  • Implement the k-NN algorithm
  • Evaluate the model's performance using metrics
  • Tune the hyperparameters of the model (e.g., k)
Tutorial on SVM hyperparameter tuning
SVM hyperparameter tuning can be complex but it's crucial for optimal performance. This tutorial will break it down.
Browse courses on Support Vector Machines
Show steps
  • Understand the different SVM hyperparameters
  • Learn about cross-validation and grid search
  • Implement hyperparameter tuning using a library or framework
Contribute to an open-source machine learning library
Practical experience in contributing to open-source projects enhances your understanding of real-world machine learning development.
Browse courses on Machine Learning
Show steps
  • Identify an open-source machine learning library that aligns with your interests
  • Read the documentation and familiarize yourself with the codebase
  • Identify a feature or improvement that you can contribute
  • Implement your contribution and submit a pull request

Career center

Learners who complete Machine Learning Algorithms: Supervised Learning Tip to Tail will develop knowledge and skills that may be useful to these careers:
Data Scientist
Data Scientists implement and build machine learning algorithms to solve complex business problems. This course may be useful, as it provides a foundation in supervised learning algorithms, including decision trees, k-NN, and support vector machines. These algorithms are commonly used in data science for classification and regression tasks.
Data Analyst
Data Analysts use data to make informed decisions and solve business problems. This course may be useful, as it provides a foundation in supervised learning algorithms, which are commonly used in data analysis for tasks such as classification and prediction.
Machine Learning Engineer
Machine Learning Engineers design, build, and maintain machine learning models. This course may be useful, as it provides a foundation in supervised learning algorithms, which are commonly used in machine learning for tasks such as classification and prediction.
Statistician
Statisticians use data to make informed decisions and solve problems. This course may be useful, as it provides a foundation in supervised learning algorithms, which are commonly used in statistics for tasks such as classification and prediction.
Software Engineer
Software Engineers design, develop, and maintain software systems. This course may be useful, as it provides a foundation in supervised learning algorithms, which can be used to improve the performance of software systems.
Data Engineer
Data Engineers build and maintain data pipelines to make data available for analysis. This course may be useful, as it provides a foundation in supervised learning algorithms, which can be used to improve the efficiency of data pipelines.
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to make investment decisions. This course may be useful, as it provides a foundation in supervised learning algorithms, which are commonly used in quantitative analysis for tasks such as risk assessment and portfolio optimization.
Actuary
Actuaries use mathematical and statistical models to assess risk and uncertainty. This course may be useful, as it provides a foundation in supervised learning algorithms, which can be used to improve the accuracy of risk assessments.
Market Research Analyst
Market Research Analysts conduct research to identify and understand consumer needs. This course may be useful, as it provides a foundation in supervised learning algorithms, which can be used to improve the accuracy of market research surveys.
Human Factors Engineer
Human Factors Engineers design products and systems to be safe and使いやすい. This course may be useful, as it provides a foundation in supervised learning algorithms, which can be used to improve the accuracy of human factors studies.
User Experience Researcher
User Experience Researchers design and conduct research to improve the user experience of products and services. This course may be useful, as it provides a foundation in supervised learning algorithms, which can be used to improve the accuracy of user research studies.
Business Analyst
Business Analysts identify and solve business problems. This course may be useful, as it provides a foundation in supervised learning algorithms, which can be used to improve the efficiency of business processes.
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve business problems. This course may be useful, as it provides a foundation in supervised learning algorithms, which can be used to improve the efficiency of operations.
Financial Analyst
Financial Analysts analyze financial data to make investment recommendations. This course may be useful, as it provides a foundation in supervised learning algorithms, which can be used to improve the accuracy of financial forecasts.
Risk Analyst
Risk Analysts identify and assess risks to an organization. This course may be useful, as it provides a foundation in supervised learning algorithms, which can be used to improve the accuracy of risk assessments.

Reading list

We've selected 17 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 Algorithms: Supervised Learning Tip to Tail.
Provides a comprehensive overview of statistical learning, covering a wide range of topics such as supervised learning, unsupervised learning, and ensemble methods. It valuable resource for anyone looking to gain a deep understanding of the theoretical foundations of statistical learning.
Provides a comprehensive overview of pattern recognition and machine learning, covering a wide range of topics such as supervised learning, unsupervised learning, and Bayesian inference. It valuable resource for anyone looking to gain a deep understanding of the theoretical foundations of machine learning.
Provides a comprehensive overview of deep learning, covering a wide range of topics such as neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for anyone looking to gain a deep understanding of deep learning.
This hands-on guide to machine learning with Python covers a wide range of supervised learning algorithms, including decision trees, SVM, and neural networks. It provides clear explanations and detailed examples, making it a valuable resource for anyone looking to gain practical experience in machine learning.
Provides a probabilistic perspective on machine learning, covering a wide range of topics such as Bayesian inference, graphical models, and reinforcement learning. It valuable resource for anyone looking to gain a deeper understanding of the theoretical foundations of machine learning.
Provides a practical overview of data mining and machine learning, covering a wide range of topics such as data preprocessing, feature selection, and model evaluation. It valuable resource for anyone looking to gain practical experience in data mining and machine learning.
Provides a hands-on introduction to machine learning with Python, covering a wide range of topics such as data preprocessing, feature engineering, and model evaluation. It valuable resource for anyone looking to gain practical experience in machine learning with Python.
Provides a gentler introduction to statistical learning than The Elements of Statistical Learning, covering a wide range of topics such as supervised learning, unsupervised learning, and ensemble methods. It valuable resource for anyone looking to get started with statistical learning.
Provides a practical introduction to machine learning for hackers, covering a wide range of topics such as data preprocessing, feature engineering, and model evaluation. It valuable resource for anyone looking to get started with machine learning without a formal background in the field.
Provides a gentle introduction to machine learning for beginners, covering a wide range of topics such as supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for anyone looking to get started with machine learning.
Provides an algorithmic perspective on machine learning. It valuable resource for researchers and practitioners who want to learn more about the algorithms that are used in machine learning.
Textbook on machine learning. It provides a comprehensive overview of the field, covering both theoretical and practical aspects.
Is an introductory guide to machine learning. It valuable resource for beginners who want to learn more about the field.

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