Nothing proves how entwined education and technology have become quite like distance learning. It’s possible to gain a working knowledge of, or a degree or certification in, just about any subject using little more than an internet connection.
Technology benefits education, but the reverse is also true. Connecting students across the globe with accessible course materials helps prepare the next generations of data scientists, programmers and machine learning (ML) specialists.
Lynda.com is a well-established resource for online course materials. If you want to know how to stake your claim in the world of machine learning, you can do it with the following five Learning Paths on Lynda right now.
Each one contains several courses. From relative beginners to those looking for industry-ready skills, these Learning Paths — or even parts of them — will help you master machine learning and find your niche within the discipline. Students can also learn about emerging trends shaping tomorrow’s technologies
Need a solid place to start? Machine learning initiates enrolled in this Path will learn the fundamental differences between ML and AI. From there, each course looks at a different aspect of machine learning subject matter expertise to empower professionals to make smart technology decisions in the work environment.
The courses in this Learning Path include highlights such as:
- AI Accountability Essential Training
- Artificial Intelligence Foundations: Neural Networks
- Cognitive Technologies: The Real Opportunities for Business
This Learning Path is a great tour of duty for machine learning professionals who want to learn how ML works, straight from authoritative voices. It’s not all theory, however — students will be able to design their own machine learning algorithms, along with other practical skills with industrial and enterprise relevance.
Here are some of the courses included in this 17-hour, nine-class Learning Path:
- Machine Learning and AI Foundations: Decision Trees
- Deploying Scalable Machine Learning for Data Science
- Building a Recommendation System With Python Machine Learning & AI
As a professional discipline, machine learning is constantly expanding. This Learning Path recognizes the need for ongoing education in such a cutting-edge field. Students will explore a range of courses designed to augment and update their fundamental data science, AI and ML skills through studies of top platforms and practical analysis exercises.
Some of the courses available in this Learning Path include:
- Spark for Machine Learning & AI
- Amazon Web Services Machine Learning Essential Training
- Machine Learning for Marketing: Essential Training
Ready to shift gears and get into some of the most bleeding-edge applications of machine learning? When ML makes headlines, it’s usually due to breakthroughs in neural networks. Students enrolled in this Learning Path will learn all about deep learning and neural networks along with their applications, including natural language processing and machine vision.
Students can explore the following courses when they enroll in this Learning Path:
- Deep Learning: Image and Face Recognition
- Neural Networks and Convolutional Neural Networks Essential Training
- Accelerating TensorFlow With the Google Machine Learning Engine
The Bureau of Labor Statistics includes data science with other professions, but it still reports an average $118,370 salary for “computer and information research scientists.” In Become a Data Scientist, students experience an immersive, practical and challenging study of machine learning as it applies to IT, statistics, systems engineering, and data science and data mining.
The selection of courses in this 17-hour Learning Path includes choices like:
- Data Science & Analytics Career Paths & Certifications: First Steps
- Learning Data Governance
- Data Science Foundations: Data Mining
Start a New Career or Advance Your Existing One
Clearly, Lynda and machine learning make a great match. Taking courses online is a fantastic way to drill down on the fundamentals or learn about brand-new tools and techniques as soon as they start making waves.
With any luck, you’ve seen something here that sparks your imagination or piques your interest in exploring machine learning further. Ready to learn how to get machines to learn?
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