Basics of Machine Learning from edX

"Basics of Machine Learning" is designed to provide participants with a comprehensive understanding of the fundamental concepts and tools of machine learning. The course covers key topics such as probability density estimation, linear regression, classification techniques like linear discriminants, logistic regression, and support vector machines, as well as ensemble methods such as bagging and boosting. Additionally, the course introduces the basics of deep neural networks, laying the groundwork for more advanced learning techniques.

Throughout the course, students will gain a solid foundation in the fundamental approaches of machine learning. By working on practical exercises, participants will cement their understanding of the techniques covered and gain valuable hands-on experience.

By the end of the course, students will have the knowledge and skills required to confidently utilize machine learning tools and techniques in their own projects, providing a strong foundation for further study or professional development in this rapidly evolving field.

What's inside

Learning objectives

Definition of statistical machine learning
Probability density estimation
Definition and behavior of linear discriminant models
Linear regression

Logistic regression
Support vector machines
Ensemble methods
Basics of neural networks

Definition of statistical machine learning
Probability density estimation
Definition and behavior of linear discriminant models
Linear regression
Logistic regression
Support vector machines
Ensemble methods
Basics of neural networks

Syllabus

Week 1: Introduction, Definitions, and Core Principles

In the first week, we will provide an overview of the course and introduce the fundamental concepts of machine learning. Students will learn about the different types of learning, such as supervised, unsupervised, and reinforcement learning, as well as the key steps involved in developing a machine learning model, from data preprocessing to model evaluation and optimization.

Week 2: Probability Density Estimation

In week two, students will delve into probability density estimation, an essential technique for understanding the underlying structure of data. We will cover various methods, such as parametric and non-parametric approaches, and how they can be used for building machine learning models.

Week 3: Linear Discriminants

During the third week, we will focus on linear discriminants and their use in classifying data points. Students will learn about the concept of decision boundaries and how to derive them using linear discriminant functions. We will also discuss how to solve decision problems by minimizing a least-squares objective, the limitations of the resulting linear classifiers, and introduce strategies for handling non-linearly separable data.

Week 4: Linear Regression

In week four, students will be introduced to linear regression, a fundamental technique for modeling continuous data. We will cover the basics of simple and multiple linear regression, discuss the concept of least squares estimation, and explore regularization as a measure against overfitting.

Week 5: Logistic Regression

The fifth week will be dedicated to logistic regression, a powerful technique for binary classification tasks. Students will learn how to derive the logistic regression model, perform iterative optimization using first- and second-order methods, apply regularization, and explore the relations between generative and discriminative methods.

Week 6: Support Vector Machines

In week six, we will explore support vector machines (SVMs), a versatile and very robust algorithm for classification tasks. Students will learn about the key concepts behind SVMs, such as maximum margin and kernel functions, and gain hands-on experience implementing SVMs using popular machine learning libraries.

Week 7: Ensembling Methods

During the seventh week, we will delve into ensemble methods, which combine multiple models to improve the overall performance of a machine learning system. Students will explore popular techniques such as bagging and boosting, and learn how to implement the AdaBoost algorithm.

Week 8: Neural Network Basics

In the final week, students will be introduced to the foundations of deep learning and neural networks. We will cover the basics of artificial neurons, feedforward networks, and backpropagation. This week will provide the groundwork for more advanced topics in deep learning, preparing students for further study or simple practical applications in the field.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops core techniques such as linear regression, logistic regression, SVM, and neural networks

Provides hands-on experience through practical exercises

Introduces the basics of deep neural networks

Requires basic knowledge of mathematics and programming

May require additional software or tools not readily available

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 Basics of Machine Learning with these activities:

Practice Probability Density Estimation Problems

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Solving practice problems will help solidify the concepts of probability density estimation and improve your ability to apply these techniques.

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Find practice problems online or in textbooks and complete them.
Review your solutions and make sure you understand the concepts involved.

Participate in discussion forums

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Engage in discussions with peers to exchange ideas, clarify concepts, and reinforce learning through active recall.

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Read through course materials and identify topics for discussion
Post questions, insights, or examples to the discussion forum
Respond to threads started by peers, providing your perspectives and insights
Review and reflect on the discussions to reinforce understanding

Build a support vector machine (SVM) classifier using TensorFlow

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Create a support vector machine classifier using TensorFlow to gain hands-on experience with implementing ML algorithms.

Browse courses on Support Vector Machines

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Install TensorFlow and the required dependencies
Load and process a dataset appropriate for binary classification
Build a SVM model using TensorFlow's tf.keras.models.Sequential class
Train and evaluate the SVM model
Visualize the decision boundary of the SVM model

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Practice linear regression with Python

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Practice the implementation of linear regression in Python to gain a better understanding of the technique.

Browse courses on Linear Regression

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Install necessary Python libraries (e.g., scikit-learn)
Load and explore a dataset suitable for linear regression
Implement linear regression using scikit-learn's LinearRegression class
Evaluate the performance of the linear regression model

Career center

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

Data Analyst

Data Analysts leverage knowledge of statistics and programming to collect, clean, analyze, and transform complex datasets into information that can be used to inform decision-making. This course provides a solid foundation in the fundamental principles and tools of machine learning, a key skill for Data Analysts. By learning about probability density estimation, linear regression models, and ensemble methods, individuals can develop expertise in extracting meaningful insights from data, a crucial aspect of success in this role.

See salaries and explore the career path for Data Analyst

Data Scientist

Data Scientists use machine learning and statistical methods to extract meaningful insights from data, often to solve complex business problems. As machine learning is a core skill for Data Scientists, this course provides a solid foundation in the fundamentals of machine learning. Through hands-on exercises, learners can gain proficiency in various machine learning techniques, such as probability density estimation, linear regression, and ensemble methods. This course can help individuals build a strong foundation for a successful career in Data Science.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers design, develop, and maintain machine learning models and systems. A strong foundation in machine learning concepts and tools is crucial for success in this role. This course covers a wide range of topics essential for Machine Learning Engineers, including supervised learning techniques, unsupervised learning methods, and deep learning basics. By completing this course, individuals can build a strong foundation for a career in this rapidly growing field.

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Quantitative Analyst

Quantitative Analysts use sophisticated mathematical and statistical methods to analyze financial data and make informed investment decisions. This course provides a solid foundation in the fundamental concepts and tools of machine learning, which is increasingly used in quantitative finance. By gaining proficiency in techniques such as probability density estimation, linear regression, and support vector machines, individuals can enhance their analytical capabilities and increase their potential for success in this competitive field.

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Statistician

Statisticians use statistical methods to collect, analyze, interpret, and present data. Machine learning is a powerful tool that has revolutionized the field of statistics. This course provides a solid foundation in the fundamental principles and tools of machine learning, enabling statisticians to enhance their analytical capabilities and expertise in data analysis. By gaining proficiency in techniques such as probability density estimation, linear regression, and ensemble methods, individuals can stay at the forefront of statistical research and practice.

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Software Engineer

Software Engineers design, develop, and maintain software systems. Machine learning is increasingly used in software development to enhance the capabilities of applications. This course provides a solid foundation in the fundamental principles and tools of machine learning, enabling Software Engineers to integrate machine learning into their software solutions. By gaining proficiency in techniques such as probability density estimation, linear regression, and neural networks, individuals can expand their skillset and increase their value in the job market.

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Financial Analyst

Financial Analysts use financial data and analytical techniques to make investment recommendations and advise clients on financial matters. Machine learning is increasingly used in financial analysis to identify trends, predict outcomes, and make informed decisions. This course provides a solid foundation in the fundamental principles and tools of machine learning, enabling Financial Analysts to enhance their analytical capabilities and stay at the forefront of financial analysis.

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Business Analyst

Business Analysts use data and analytical techniques to solve business problems and improve operational efficiency. Machine learning is a powerful tool for extracting meaningful insights from data, which can greatly benefit Business Analysts. This course provides a solid foundation in the fundamental concepts and tools of machine learning, enabling Business Analysts to leverage machine learning techniques to enhance their analytical capabilities and deliver impactful solutions.

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Operations Research Analyst

Operations Research Analysts use mathematical and analytical techniques to optimize complex systems and processes. Machine learning is increasingly used in operations research to enhance decision-making and improve outcomes. This course provides a solid foundation in the fundamental principles and tools of machine learning, enabling Operations Research Analysts to integrate machine learning into their optimization models and improve the efficiency and effectiveness of their solutions.

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Actuary

Actuaries use mathematical and statistical techniques to assess and manage risk. Machine learning is increasingly used in actuarial science to improve risk assessment and pricing models. This course provides a solid foundation in the fundamental principles and tools of machine learning, enabling Actuaries to integrate machine learning into their risk assessment and management practices.

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Risk Manager

Risk Managers identify, assess, and manage risks that may impact an organization. Machine learning is increasingly used in risk management to identify potential risks and develop mitigation strategies. This course provides a solid foundation in the fundamental principles and tools of machine learning, enabling Risk Managers to enhance their analytical capabilities and improve the effectiveness of their risk management strategies.

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Customer Success Manager

Customer Success Managers are responsible for ensuring that customers are satisfied with their products or services. Machine learning is increasingly used in customer success management to identify customer pain points, predict customer churn, and provide personalized support. This course provides a solid foundation in the fundamental principles and tools of machine learning, enabling Customer Success Managers to enhance their analytical capabilities and improve the effectiveness of their customer support strategies.

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Sales Analyst

Sales Analysts use data and analytical techniques to understand sales patterns and optimize sales strategies. Machine learning is increasingly used in sales analytics to identify sales opportunities, predict customer churn, and personalize sales pitches. This course provides a solid foundation in the fundamental principles and tools of machine learning, enabling Sales Analysts to enhance their analytical capabilities and improve the effectiveness of their sales strategies.

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Product Manager

Product Managers are responsible for the development and management of products. Machine learning is increasingly used in product development to improve product features and user experience. This course provides a solid foundation in the fundamental principles and tools of machine learning, enabling Product Managers to collaborate with engineering teams to integrate machine learning into their products and enhance their value to customers.

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Marketing Analyst

Marketing Analysts use data and analytical techniques to understand customer behavior and optimize marketing campaigns. Machine learning is increasingly used in marketing analytics to identify customer segments, predict customer behavior, and personalize marketing messages. This course provides a solid foundation in the fundamental principles and tools of machine learning, enabling Marketing Analysts to enhance their analytical capabilities and improve the effectiveness of their marketing campaigns.

See salaries and explore the career path for Marketing Analyst