Classification - Fundamentals & Practical Applications from Coursera

Classification problems are one of the most common scenarios we face in data science. This course will help you understand and apply common algorithms to make predictions and drive decision-making in business. Whether you’re an aspiring data scientist, studying analytics, or have a focus on business intelligence, this course will give you a comprehensive overview of classification problems, solutions, and interpretations.

From Logistic Regression to KNN and SVM models, you’ll learn how to implement techniques in Excel and Python and how to create loops to run models in parallel. Since model evaluation is so important, we’ll dedicate a whole chapter to interpreting model outputs with evaluation metrics and the confusion matrix. With this, you’ll learn about false negatives, and false positives, and consider the impacts these may have on specific business scenarios. Finally, we’ll give you a brief insight into more advanced classification techniques such as feature importance, SHAP values, and PDP plots.

Upon completing this course, you will be able to:

• Distinguish between classic classification techniques including their implicit assumptions and practical use-cases

• Perform simple logistic regression calculations in Excel & RegressIt

• Create basic classification models in Python using statsmodels and sklearn modules

• Evaluate and interpret the performance of classification model outputs and parameters

Whether you’re an aspiring data scientist, studying analytics, or have a focus on business intelligence, this classification course will serve as your comprehensive introduction to this fascinating subject. You’ll learn all the key terminology to allow you to talk data science with your teams, benign implementing analysis, and understand how data science can help your business.

What's inside

Syllabus

Getting Started

Classification problems are one of the most common scenarios we face in data science. This course will help us understand and apply common algorithms to make predictions and drive decision-making in business. From Logistic Regression to KNN and SVM models, we'll learn how to implement techniques in Excel and Python and how to create loops to run models in parallel. Since model evaluation is so important, we’ll dedicate a whole chapter to interpreting model outputs with evaluation metrics and the confusion matrix. With this, we’ll learn about false negatives, and false positives, and consider the impacts these may have on specific business scenarios. Finally, we’ll have a brief insight into more advanced classification techniques such as feature importance, SHAP values, and PDP plots.

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what should give you pause

and possible dealbreakers

Covers logistic regression, KNN, and SVM models, which are fundamental algorithms used in classification tasks

Teaches implementation in both Excel and Python, providing flexibility for learners with different backgrounds

Explores model evaluation metrics and the confusion matrix, which are essential for understanding model performance

Provides an overview of feature importance, SHAP values, and PDP plots, which are more advanced classification techniques

Requires learners to use Excel and Python, which may require learners to purchase software licenses

Reviews summary

Classification fundamentals and practical intro

According to learners, this course provides a solid introduction to classification problems and common algorithms. Students find the explanations on topics like Logistic Regression, KNN, and SVM to be clear and well-structured. The course is particularly praised for its focus on model evaluation metrics and the confusion matrix, which is highlighted as very useful. While covering both Excel and Python implementations is seen as a strength by many, some learners note that the Python sections could be more in-depth. Overall, it's considered a valuable starting point for those looking to understand classification for data analysis or business intelligence roles.

Covers implementation in Excel and Python.

"Using Excel first helped visualize the basics before Python."

"The Python examples were good for getting started with code."

"I found the Excel part a bit basic, I was mainly here for Python."

"Could use more Python code examples and less focus on Excel."

Explanations of core classification concepts are clear.

"The way classification problems are introduced is very clear."

"I finally understand logistic regression thanks to this course."

"The explanations of KNN and SVM were easy to follow for a beginner."

Excellent course for beginners in classification.

"This course is perfect if you are new to classification."

"Gave me a solid foundation to build upon in data science."

"Highly recommend this for anyone just starting out."

"It's a great first step into machine learning classification."

Strong emphasis on model evaluation and metrics.

"The section on evaluation metrics was incredibly helpful."

"Understanding the confusion matrix and false positives/negatives is key."

"I appreciate the detail given to interpreting model performance."

May be too basic for intermediate learners.

"As someone with some background, I found it quite basic."

"It covers the fundamentals well but doesn't go very deep."

"Good intro, but you'll need other resources for advanced topics."

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 Classification - Fundamentals & Practical Applications with these activities:

Review Statistics Fundamentals

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Reinforce your understanding of fundamental statistical concepts to better grasp the underlying principles of classification algorithms.

Browse courses on Statistical Significance

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Review key statistical concepts.
Practice solving basic statistics problems.

Read 'An Introduction to Statistical Learning'

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Gain a deeper understanding of the statistical learning methods used in classification.

View An Introduction to Statistical Learning: with... on Amazon

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Read the chapters on classification methods.
Work through the examples and exercises.

Implement Logistic Regression in Python

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Solidify your understanding of logistic regression by implementing it from scratch using Python.

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Write a function to calculate the sigmoid function.
Write a function to calculate the cost function.
Implement gradient descent to optimize the parameters.

Four other activities

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Read 'Pattern Recognition and Machine Learning'

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Expand your knowledge of classification algorithms and techniques.

View Pattern Recognition and Machine Learning... on Amazon

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Read the chapters on classification methods.
Work through the examples and exercises.

Create a Blog Post on Model Evaluation Metrics

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Deepen your understanding of model evaluation by explaining different metrics in a blog post.

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Research different model evaluation metrics.
Write a clear and concise explanation of each metric.
Provide examples of how to use each metric.

Build a Classification Model for a Real-World Dataset

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Apply your knowledge by building a classification model for a real-world dataset.

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Find a suitable dataset.
Preprocess the data.
Train and evaluate a classification model.

Create a Presentation on Advanced Classification Techniques

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Further explore advanced classification techniques and present your findings.

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Research advanced classification techniques.
Prepare a presentation summarizing your findings.
Present your findings to a peer or group.

Career center

Learners who complete Classification - Fundamentals & Practical Applications will develop knowledge and skills that may be useful to these careers:

Data Scientist

A data scientist uses statistical methods and machine learning to analyze data, identify trends, and create predictive models, and this course directly builds skills needed to succeed in this role. This 'Classification - Fundamentals & Practical Applications' course helps build a foundation in classification techniques, which are core to a data scientist’s tasks. The course’s focus on applying algorithms, creating models in Python, and interpreting model outputs with metrics and confusion matrices are directly applicable to the work of a data scientist. The course explores logistic regression, KNN, SVM models, and feature importance – all of which are heavily utilized in the field. Someone interested in becoming a data scientist should take this course because it provides a comprehensive overview of concepts and practical skills that are fundamental in this career.

See salaries and explore the career path for Data Scientist

Business Intelligence Analyst

A business intelligence analyst uses data to identify trends and create reports to support business decisions, and this course builds skills needed to succeed in this role. The 'Classification - Fundamentals & Practical Applications' course introduces classification problems, solutions, and their interpretations, all of which are vital for a business intelligence analyst. The course's focus on understanding different classification algorithms, implementation in Excel and Python, and creating predictive models are all directly applicable to business intelligence projects. The discussions on interpreting model outputs, false positives, and false negatives are particularly helpful for a business intelligence analyst who needs to extract insights from data and understand the limitations of different models. Someone looking to become a business intelligence analyst should take this course because it’s a comprehensive introduction to key classification techniques that can help them analyze business data more thoroughly.

See salaries and explore the career path for Business Intelligence Analyst

Machine Learning Engineer

A machine learning engineer is responsible for developing, implementing, and maintaining machine learning systems. The 'Classification - Fundamentals & Practical Applications' course provides a strong foundation in key machine learning concepts, particularly classification models. An aspiring machine learning engineer will learn how to implement models in Python, evaluate model performance, and understand the impact of various model parameters. The course explores a range of classification algorithms, including logistic regression, KNN, and SVM, which are essential for anyone in this role. With this course, one will also learn about feature importance, SHAP values, and PDP plots, all of which are utilized by machine learning engineers for model explanation and improvement. Someone who is interested in this field should take this course as a useful introduction to the practical side of machine learning.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

A data analyst examines data to identify trends, insights, and patterns to inform decision making. Many of their projects will likely be solved with the concepts taught in this course, 'Classification - Fundamentals & Practical Applications.’ This course provides a deep understanding of classification techniques, which are very important in a data analyst’s daily work. An aspiring data analyst will gain hands-on experience in creating models in both Excel and Python, and in evaluating and interpreting model outputs and metrics. The course covers essential concepts and practical applications of logistic regression, KNN, and SVM models, allowing someone in this role to perform their work more effectively. Therefore, someone hoping to become a data analyst may find this course a useful starting point.

See salaries and explore the career path for Data Analyst

Quantitative Analyst

A quantitative analyst uses mathematical and statistical models to analyze financial markets and make predictions. The 'Classification - Fundamentals & Practical Applications' course provides important knowledge of classification algorithms helpful to a quantitative analyst. The course's focus on logistic regression and other classification models helps with the creation of models for quantitative finance. The skills learned in Python implementation, model evaluation, and understanding metrics like false positives and negatives are directly applicable to quantitative modeling and analysis. Someone looking to become quantitative analysts should take this course to gain a practical introduction to building classification models that can be used to create trading strategies or evaluate risk.

See salaries and explore the career path for Quantitative Analyst

Statistician

A statistician develops and applies statistical theories and methods to collect, analyze, and interpret data, and this course may be useful in this role. The 'Classification - Fundamentals & Practical Applications' course introduces fundamental classification techniques that statisticians use for modeling. An aspiring statistician will benefit from the course's exploration of logistic regression, KNN, and SVM models, which are important tools for statistical analysis. The emphasis on model evaluation, understanding confusion matrices, and interpreting model outputs are highly beneficial for statisticians wanting to use classification models. While this is considered a more fundamental course, someone hoping to become a statistician may still find it helpful when first starting out.

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Marketing Analyst

A marketing analyst uses data to evaluate marketing campaigns and consumer behavior. The 'Classification - Fundamentals & Practical Applications' course may be useful for the work of a marketing analyst. This course’s introduction to classification techniques, such as logistic regression, may help when analyzing customer data and predicting outcomes. Skills learned in model evaluation, Python implementation, and understanding metrics such as false positives and negatives can be useful to a marketing analyst. Someone interested in a marketing analytics role may find this course a valuable introduction to applying classification techniques to marketing data.

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Financial Analyst

A financial analyst evaluates financial data and makes recommendations for investments or financial planning, and this course may be helpful in that role. The 'Classification - Fundamentals & Practical Applications' course provides useful information on classification techniques, such as logistic regression, which can be applied to financial analysis. The skills learned in Excel and Python implementation, model evaluation, and understanding important metrics, such as false positives and negatives, can be helpful for someone in this profession. Someone looking to become a financial analyst may find that this course is a general introduction to using classification techniques in financial settings.

See salaries and explore the career path for Financial Analyst

Research Scientist

A research scientist conducts experiments and analyzes data to gain new insights. The 'Classification - Fundamentals & Practical Applications' course may be useful for a research scientist dealing with data classification. The course’s introduction to different classification algorithms such as logistic regression, KNN, and SVM models, can help researchers working with datasets. The course’s emphasis on model evaluation, interpreting model outputs, and understanding concepts like false positives and negatives are valuable skills for a research scientist. Someone looking to become a research scientist may find this course helpful in acquiring a basic understanding of classification models.

See salaries and explore the career path for Research Scientist

Operations Research Analyst

An operations research analyst uses mathematical models and analysis to help organizations optimize their operations, and this course may be useful for them. The 'Classification - Fundamentals & Practical Applications' course introduces important classification techniques which may be applied to areas such as supply chain logistics, resource allocation, or capacity planning. The course's focus on models like logistic regression and the associated evaluation metrics, combined with hands-on implementation in Excel and Python, may be helpful when building and testing quantitative models. Therefore, someone looking to become an operations research analyst may find this course a useful introduction to using classification techniques in business operations.

See salaries and explore the career path for Operations Research Analyst

Risk Analyst

A risk analyst assesses potential risks and develops mitigation strategies for businesses. The 'Classification - Fundamentals & Practical Applications' may be useful to a risk analyst. The course provides an introduction to concepts like logistic regression, which can be applied to analyze risk scenarios. The course’s teachings on how to interpret model outputs and understand concepts such as false positives and negatives may be useful for calculating risk probabilities. Someone hoping to become a risk analyst may find this course provides a practical introduction to using classification techniques in risk management.

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Actuary

An actuary analyzes the financial costs of risk and uncertainty for insurance and financial plans. The 'Classification - Fundamentals & Practical Applications' course may be useful for someone in this role. The course introduces classification techniques such as logistic regression, which is sometimes used in actuarial models. Additionally, the course’s focus on model evaluation and understanding metrics like false positives and negatives can be useful for someone working with risk modeling. Therefore, an aspiring actuary may find this is a useful introduction to some classification techniques that they may use in their work.

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Management Consultant

A management consultant advises organizations on ways to improve their operations or strategies. The 'Classification - Fundamentals & Practical Applications' course may be useful for gaining an understanding of a data-driven approach to problem-solving. While not directly in the scope of management consulting, this course introduces important classification techniques used by other data professionals. Skills such as understanding model metrics, which are a part of the course, may be helpful when interpreting business information. Someone looking to become a management consultant may find that this course offers a general introduction to the types of analyses that are sometimes used to inform business decisions.

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Project Manager

A project manager plans, coordinates, and oversees projects from start to finish. The 'Classification - Fundamentals & Practical Applications' course may be useful for understanding how data-driven techniques might inform a project plan. While the course focuses on data classification, it introduces important terminology and concepts that are used by data professionals. The skills learned in this course may be useful for project managers who are working on projects where data analyses are being performed. Therefore, an aspiring project manager may find this course provides an introduction to how data is analyzed and how to communicate more effectively with data focused team members.

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Market Research Analyst

A market research analyst studies market conditions to examine potential sales of a product or service. The 'Classification - Fundamentals & Practical Applications' course may be useful for learning about data analysis. Although it is not directly in the scope of market research, this course introduces the main concepts of data classification, which is common in other data professions. The course's teachings on model evaluation and understanding metrics like false positives or negatives could be helpful when interpreting market data. Someone hoping to become a market research analyst may find that this course is a general introduction to analyzing data using various models.

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Classification - Fundamentals & Practical Applications

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Classification fundamentals and practical intro

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