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Introduction to Machine Learning

A Cloud Guru

Hello, Cloud Gurus! Have you recently been thrown into your first machine learning project and need to get up to speed? Perhaps you’re struggling with all the mathematical jargon in other machine learning introductory courses? Let machine learning Guru Scott Pletcher guide you through the sometimes intimidating world of machine learning in an entertaining and very non-scary way. Many “introductory” ML courses attempt to explain concepts using differential equations and cryptic Greek symbols--but not this course. This course is specifically designed for people without deep math backgrounds, and Scott cuts through the jargon with simile and metaphor to equip you with concepts and understanding you can put to work immediately. Machine learning has certainly garnered lots of attention in recent years as organizations struggle to remain competitive in the Information Age arms race. Start-ups, established companies, and cloud providers are rapidly releasing new features and services aimed at ML practitioners. Unfortunately, the ability to effectively use these services to create genuine and repeatable business value is somewhat limited by the availability of skilled practitioners. Compounding matters, much of the available ML training in the market presumes a level of advanced mathematics knowledge which can be intimidating to novices. In this course, you’ll learn: * Distinction between artificial intelligence, machine learning, data science, and statistical analysis. * The various types of machine learning with real-world examples such as regression, classification, decision trees, and deep learning. We’ll even train a self-driving car! * How to evaluate and frame business problems for potential machine learning applications. * Brief survey of available tools, datasets, and resources common in the machine learning space. * Limitations, potential pitfalls, and ethical considerations around machine learning. This is an introductory course so don’t expect to walk away solving math theorems like in Good Will Hunting. However, do expect to come away with much greater machine learning confidence and understanding, pulling back that complexity curtain to unveil how all this modern-day magic really works. (Spoiler alert: it’s really not magic at all…) So Cloud Gurus, let’s take that first step and get started on your machine learning journey.

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Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Outlines the difference between related but distinct fields such as data science and artificial intelligence
Utilizes a conversational and engaging tone, making complex machine learning concepts more accessible
Applicable both in industry and academia, providing real-world examples to enhance understanding
Avoids using technical jargon and Greek symbols, catering specifically to beginners in machine learning
Designed for those with limited mathematical backgrounds, focusing on intuitive explanations and practical applications
Covers a broad range of machine learning topics, including regression, classification, and decision trees

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Career center

Learners who complete Introduction to Machine Learning will develop knowledge and skills that may be useful to these careers:
Data Scientist
A Data Scientist takes raw data and transforms it into usable knowledge and insights. Their work has been critical for advancements in machine learning and data analysis. Relatedly, an understanding of machine learning principles is essential for modern Data Scientists. This course helps establish a strong foundation in machine learning and will prove helpful to aspiring Data Scientists.
Machine Learning Engineer
A Machine Learning Engineer applies machine learning principles to solve real-world problems. This course introduces several common machine learning algorithms and discusses real-world applications for each. It also includes discussion of relevant tools and resources. As such, this course may be useful to those interested in a career as a Machine Learning Engineer.
Data Analyst
A Data Analyst cleans and organizes data, and may also analyze it and present findings to stakeholders. Machine learning has become an essential tool for data analysis and visualization and is often used in a Data Analyst's work. Therefore, this foundational course in machine learning could be helpful to aspiring Data Analysts.
Software Engineer
A Software Engineer designs, builds, and tests software systems. Machine learning is increasingly incorporated into software solutions, meaning that foundational knowledge of the principles and applications of machine learning is essential for Software Engineers in this day and age. This course helps establish that foundation.
Data Engineer
A Data Engineer prepares and processes large sets of data. For that data to be usable for machine learning and other purposes, the Data Engineer must be familiar with machine learning principles. This course offers an excellent foundation. Previous exposure to concepts such as regression and deep learning is not required.
Statistician
A Statistician collects, analyzes, interprets, and presents data. While statistics and machine learning are distinct fields, they intersect in many ways. As a result, machine learning knowledge is indispensable for many statisticians. This beginner-friendly course lays the groundwork for those interested in exploring both fields.
Research Scientist
A Research Scientist conducts research and develops new technologies. Many Research Scientists work in fields such as machine learning and artificial intelligence. For those with advanced degrees, this course is an excellent primer on the fundamentals of machine learning.
Business Analyst
A Business Analyst analyzes an organization's needs and recommends solutions to business problems. Machine learning is quickly revolutionizing the way business problems are solved. As a result, an understanding of machine learning has become beneficial for Business Analysts. This course is a solid introduction to machine learning concepts for Business Analysts.
Financial Analyst
A Financial Analyst makes investment recommendations. Machine learning is beginning to make an impact on investment decisions. Accordingly, Financial Analysts with machine learning knowledge are likely to see career benefits. This course will be useful to Financial Analysts who want to keep up with the times.
Consultant
A Consultant advises businesses on how to improve performance. Increasingly, Consultants are working with businesses to implement machine learning solutions. Aspiring Consultants with machine learning knowledge will be highly sought-after.
Product Manager
A Product Manager develops and manages products. As machine learning is increasingly incorporated into products, Product Managers will need to be familiar with the basics of machine learning. This course provides a foundation for Product Managers to build on.
Marketing Manager
A Marketing Manager promotes products and services. Machine learning tools are becoming more common in marketing, from data analysis to content creation. A basic understanding of machine learning, such as the one this course offers, will help Marketing Managers stand out from the competition.
Sales Manager
A Sales Manager leads a sales team. Sales Managers with knowledge of machine learning are better equipped to understand and use data to improve sales performance. This introductory course in machine learning is an excellent place to start.
Systems Analyst
A Systems Analyst designs, implements, and maintains computer systems. Many organizations are moving to cloud-based systems that incorporate machine learning for data processing, security, and more. Systems Analysts with machine learning knowledge are in high demand.
Operations Research Analyst
An Operations Research Analyst develops and implements mathematical models and techniques to solve real-world problems, often for businesses. Machine learning is an important tool for Operations Research Analysts. This beginner-friendly course will be helpful to Operations Research Analysts with limited machine learning experience.

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 Introduction to Machine Learning.
Provides hands-on experience with popular machine learning libraries such as Scikit-Learn, Keras, and TensorFlow. It offers practical examples and exercises, making it a valuable resource for learners who want to apply machine learning techniques to real-world problems.
Provides a clear and concise introduction to machine learning concepts and algorithms, using Python as the programming language. It covers a wide range of topics, from supervised and unsupervised learning to deep learning and reinforcement learning.
Provides a comprehensive overview of machine learning from a probabilistic perspective. It covers a wide range of topics, from Bayesian inference to graphical models and deep learning. This book is suitable for learners with a strong background in mathematics and statistics.
Provides a comprehensive overview of deep learning concepts and algorithms. It covers a wide range of topics, from convolutional neural networks to recurrent neural networks and generative adversarial networks. This book is suitable for learners with a strong background in mathematics and computer science.
Provides a comprehensive overview of statistical learning methods with a focus on sparsity. It covers a wide range of topics, from the lasso to the elastic net and group lasso. This book is suitable for learners with a strong background in mathematics and statistics.
Provides a practical introduction to machine learning for non-experts. It covers a wide range of topics, from data preparation to model evaluation and deployment. This book is suitable for learners with little or no prior knowledge of machine learning.
Provides a comprehensive overview of statistical learning methods, with a focus on data mining, inference, and prediction. It covers a wide range of topics, from linear regression to tree-based methods and support vector machines.
Provides a comprehensive overview of pattern recognition and machine learning. It covers a wide range of topics, from Bayesian inference to graphical models and deep learning.
Provides a comprehensive overview of linear algebra, with a focus on applications in machine learning and data science. It covers a wide range of topics, from matrix theory to optimization and statistical learning.
Provides a comprehensive overview of convex optimization, with a focus on applications in machine learning and data science. It covers a wide range of topics, from linear programming to semidefinite programming and distributed optimization.
Provides a comprehensive overview of information theory, inference, and learning algorithms. It covers a wide range of topics, from entropy and mutual information to Bayesian inference and reinforcement learning.
Provides a comprehensive overview of reinforcement learning, with a focus on the theoretical foundations of the field. It covers a wide range of topics, from Markov decision processes to temporal difference learning and deep reinforcement learning.
Provides a comprehensive overview of natural language processing, with a focus on Python as the programming language. It covers a wide range of topics, from text classification to machine translation and information retrieval.
Provides a comprehensive overview of computer vision, with a focus on algorithms and applications. It covers a wide range of topics, from image formation to object recognition and scene understanding.

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