10 Best Machine Learning Courses & Classes Online On LinkedIn Learning In 2023
Discover the top 10 machine learning courses and classes offered on LinkedIn Learning in 2023, tailored to fit varying levels of expertise, and unlock countless career opportunities in the rapidly expanding fields of AI and data science.
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Embarking on the journey to master machine learning and wondering where to start? Fret not, as we've delved deep into a plethora of online LinkedIn courses to bring you the top ten that cater to varying levels of expertise. Our extensive research analyzed 1,775 popular machine learning courses from various providers, amassing a whopping 45,557,096 enrolled students who contributed 2,878,933 ratings and reviews. The result? A comprehensive list of the best courses unraveling the world of machine learning, tailored just for you.
To ensure you receive the most valuable and engaging content, we meticulously evaluated each course, taking into account ratings, reviews, enrollments, and learner feedback. Furthermore, we assessed factors such as the comprehensiveness of the curriculum, release date, affordability, and our own extensive experience and expertise, leaving no stone unturned in our quest to bring you the most suitable selection. Rest assured, with each course on this carefully curated list, you'll be one step closer to mastering the domains of AI and data science, whether you're a beginner or a skilled professional.
This course delves into the fascinating field of artificial intelligence (AI), specifically focusing on machine learning and its applications. Machine learning is a highly active and dynamic domain within AI, enabling computers to learn and adapt without explicit programming. By utilizing statistical methods, these innovative algorithms empower machines to classify images, sounds, and videos, answer questions, discover new medications, and even compose music.
In this comprehensive course, students will explore the various types of machine learning, including supervised, unsupervised, and reinforcement learning. Utilizing popular algorithms such as decision trees, clustering, and regression analysis, participants will uncover patterns in large data sets. Additionally, the course addresses potential pitfalls and challenges encountered when embarking on machine learning endeavors, providing invaluable guidance for those seeking to excel in this dynamic and rapidly expanding field.
This course is optimal for individuals seeking to gain knowledge on a wide range of machine learning algorithms and their applications, all while staying updated with the latest developments in the field. It equips students with industry-relevant skills in machine learning and AI, empowering them to explore career opportunities and solve real-world problems using advanced techniques.
This comprehensive course provides a detailed overview of analyzing SQL Server data with Python. By leveraging a powerful combination of tools, such as high-performance Python libraries and the Machine Learning Services add-on, students will be able to streamline their analysis directly within SQL Server. The course covers essential topics like statistical analysis, graphic generation, and processing tabular data using Python scripts. The instructor, database expert Adam Wilbert, demonstrates how to create visual representations of data, such as scatterplots and bar charts, to maximize understanding and interpretation.
In addition to providing a strong foundation in data analysis techniques, the course also teaches students how to convert Python scripts into stored procedures and set up standalone machine learning services for executing scripts without negatively impacting SQL Server performance. This allows for a more efficient integration of Python and SQL Server, resulting in optimized workflows and improved overall performance. By the end of the course, students will have a strong understanding of how to effectively analyze SQL Server data using Python and machine learning services, giving them valuable skills to use in their professional careers.
This course is ideal for those who want to streamline analysis of SQL Server data with Python, and learn to create visual representations, integrate Python and SQL Server efficiently, and implement machine learning services for optimized workflows.
This engaging and informative course delves into the captivating world of artificial intelligence (AI), providing a comprehensive understanding of its fundamental concepts and various approaches. Students will explore the history of AI, as computer-enhanced AI has been in existence since the 1950s, and how recent advancements in technology have breathed new life into the field. Thanks to improved sensors and powerful processors, machines can now better see, hear, understand location, make intricate decisions, sort possibilities, plan outcomes, and learn from errors. The potential of these advancements is immense, as are the implications surrounding them.
Throughout the course, participants will gain a solid grasp of the critical distinctions between "strong" and "weak" AI, examining how AI has changed perceptions of intelligence and the amount of trust placed in machines. Instructor Doug Rose offers insights into the different AI strategies, including machine learning and deep learning, as well as the practical applications for AI-enhanced technologies. Additionally, learners will discover how to effectively integrate AI with other technologies, such as big data, and how to circumvent common challenges that arise when programming AI systems. This course serves as an excellent foundation for anyone interested in the fascinating and rapidly evolving field of artificial intelligence.
This course is excellent for gaining a comprehensive understanding of artificial intelligence's fundamental concepts, various approaches, and practical applications, staying updated with the latest advancements, and building a strong foundation in AI.
This comprehensive course is designed for those interested in getting started with machine learning, particularly using Mathematica 11. The course provides a solid understanding of machine learning tasks and offers guidance on separating training data from test data. Furthermore, students are equipped with the skills necessary to import data from a file, preparing data for machine learning tasks such as replacing values near zero with zero, and sorting elements using a rule. Overall, the course offers a strong foundation in data analysis using Mathematica 11's machine learning capabilities.
Delivered by Curt Frye, the course delves into various aspects of machine learning, ensuring that students develop a well-rounded understanding. The curriculum covers determining functions responsible for generating data and performing supervised machine learning tasks. This allows students to explore the full potential of Mathematica 11 and its features. By the end of the course, learners will have mastered the essential skills required to efficiently and accurately analyze data using machine learning tools in Mathematica 11.
This course excels at teaching learners how to efficiently analyze data and perform machine learning tasks using Mathematica 11, ultimately ensuring they develop a well-rounded understanding of its capabilities.
This comprehensive training program delves into natural language processing (NLP) concepts, equipping learners with advanced data cleaning and vectorization techniques, and teaches how to build machine learning classifiers. As the abundance of publicly available data increases, particularly in the form of unstructured text data, understanding how to process and analyze this data is essential. Ideal for individuals with prior Python experience and an interest in NLP, the course aims to provide a deep understanding of complex problem-solving using machine learning.
Instructed by Derek Jedamski, the course begins with a quick overview of basic NLP concepts, followed by an in-depth exploration of advanced data cleaning and vectorization techniques. Subsequently, participants learn how to build machine learning classifiers through practical examples. During this section, Derek demonstrates the development of two distinct types of machine learning models, along with the evaluation and testing of various model variations. Throughout the course, learners acquire the skills and knowledge necessary to tackle real-world challenges by successfully implementing NLP and machine learning techniques.
This course excels at teaching advanced data cleaning and vectorization techniques, along with building machine learning classifiers in the context of natural language processing. Ideal for individuals with prior Python experience and an interest in NLP, it aims to provide a deep understanding of complex problem-solving using machine learning.
This comprehensive course focuses on developing a strong foundation in machine learning, allowing learners to generate impactful insights from data. Equipped with the necessary skills, students will be able to efficiently solve an array of machine learning challenges. By delving into the foundations that underpin the field, the course addresses topics such as exploratory data analysis, model evaluation techniques, and ensuring their models generalize well to unseen examples. The course is designed to provide a competitive edge to students by teaching them how to customize and optimize models specifically for unique problems, all within the context of AWS machine learning tools.
In the first installment of a two-part series, Applied Machine Learning: Foundations emphasizes a multi-faceted understanding of the machine learning process, rather than concentrating on specific algorithms. The instructor, Derek Jedamski, guides students through an engaging learning experience, imparting valuable tools and techniques to address a wide range of issues across the machine learning spectrum. By the end of the course, students are expected to have a solid grasp of the core concepts and methodologies necessary to adapt their skills to a variety of problem-solving scenarios, ultimately giving them an edge in an increasingly competitive landscape.
This course is excellent for developing a strong foundation in machine learning, allowing learners to generate impactful insights from data and efficiently solve an array of machine learning challenges with optimized models.
In this comprehensive course, you will learn the foundations of machine learning and artificial intelligence by delving into decision trees using the IBM SPSS Modeler. Covering essential topics such as CHAID and C&RT algorithms, the course aims to help you expand your data science skillset and become proficient in working with decision trees. By exploring various tree algorithms and understanding their applications, you will be better equipped to analyze complex data and make more informed decisions in your work.
Throughout the course, you will gain hands-on experience with the IBM SPSS Modeler through various demonstrations that illustrate how decision trees are used in practice. By learning to reverse engineer decision trees, you will be able to identify specific variables that play a crucial role in your machine learning models. With a strong foundation in machine learning and decision trees, you will be well-prepared to tackle more advanced data science projects and continue growing in this rapidly evolving field.
This course is ideal for individuals who want to gain expertise in working with decision trees using IBM SPSS Modeler, expanding their data science skillset while learning essential algorithms like CHAID and C&RT. The hands-on approach helps learners better analyze complex data and make more informed decisions in real-world applications.
This comprehensive course provides an in-depth understanding of various machine learning algorithms, empowering students to tackle a wide range of data science problems with confidence. Under the expert guidance of instructor Derek Jedamski, participants will learn the fundamentals of popular algorithms such as logistic regression and gradient boosting, and discover how to implement the most appropriate methodology for the task at hand. The aws machine learning platform offers a plethora of benefits, making it the perfect learning environment for those looking to expand their knowledge in this field.
In the second part of the Applied Machine Learning series, students will build upon their foundational knowledge by diving deeper into the inner workings of each algorithm. By focusing on the key drivers, advantages, and limitations of each method, learners will come away equipped with the tools and understanding necessary to choose the most suitable solution for their particular data science challenges. By the end of the course, participants will not only be well-versed in a broad selection of algorithms but also be able to make informed decisions regarding their application in real-world scenarios, giving them a competitive edge in the ever-evolving landscape of machine learning and data science.
This course is the best for those who want to gain an in-depth understanding of various machine learning algorithms, tackle a wide range of data science problems with confidence, and make informed decisions regarding their application in real-world scenarios.
This comprehensive course delves into the captivating world of machine learning and its various applications, all through the lens of the popular programming language, Python. The curriculum demystifies the concept of machine learning by explaining the mechanisms through which machines learn and the different techniques employed in the process. Furthermore, it offers a systematic and comprehensive guide on the process of collecting, understanding, and preparing data for machine learning initiatives.
Presented by Frederick Nwanganga, an experienced and engaging instructor, the course covers each aspect of machine learning in a digestible and approachable manner. Learners will gain hands-on experience and guidance as they explore the power of Python in accomplishing each crucial step in the process. By the end of the course, students will be well-versed in building, evaluating, and interpreting the results of a machine learning model in Python, fully unlocking the potential of this versatile computational domain.
This course excels at teaching students how to effectively use Python programming language in various machine learning applications, building a strong foundation in the field and enhancing their problem-solving skills with techniques to unlock the potential of this versatile computational domain.
This project-based course is designed for programmers of all skill levels, teaching participants how to utilize machine learning to create systems capable of making recommendations, such as suggesting new products to customers. Under the guidance of Adam Geitgey, the course offers a hands-on experience in building a recommendation system that can recommend similar products based on customers' past preferences and purchases, as well as identifying products with shared characteristics.
Recommendation systems play a significant role in modern consumer websites, driving customer engagement and sales by helping users discover products and services they might not have found otherwise. The course employs free, open-source tools, such as Python 3.5, pandas, and numpy to provide an accessible learning experience. Upon completion, participants will possess the knowledge and skills required to apply machine learning techniques to solve recommendation problems, making a direct impact on their own projects and ventures.
This course is ideal for learners who want to develop their skills in machine learning to build effective recommendation systems. You'll gain hands-on experience using Python-based tools to create systems that suggest new products to customers based on their past preferences and purchases, driving customer engagement and sales.
Choosing the best machine learning course can be a challenging task, given the numerous options available. As a learner, it is essential to identify your intentions and goals before selecting a course. In this context, consider the following important factors:
Focus on content that covers a wide array of machine learning algorithms and applications to broaden your knowledge base.
Opt for courses that provide ample opportunities for enhancing your existing skills, such as implementing machine learning models using various programming languages.
Keep yourself updated with the latest developments in the field by selecting courses offering cutting-edge topics and research.
Develop a strong foundation in machine learning and its associated subfields by carefully assessing course content and modules.
Ensure the course has a strong emphasis on problem-solving and real-world applications to hone your skills further.
Verify that the course will help you acquire industry-relevant skills, thereby making you a valuable asset in the job market.
Assess whether the course provides comprehensive guidance on using machine learning for data analysis and decision-making.
Lastly, explore courses that guide you through potential career opportunities in the field of machine learning and AI.
To make an informed decision and align your learning objectives with the course content, always consider reading reviews and testimonials from previous learners. Good luck in your quest to become a machine learning expert!
Now that you have a carefully curated list of the best machine learning courses at your fingertips, it's time to invest in your professional development and future success. Whether you're a complete beginner or a seasoned expert, continuing to enhance your machine learning skills will keep you ahead of the competition and open up countless career opportunities in the rapidly evolving fields of AI and data science.
Remember, knowledge is the key to unlocking endless possibilities. By committing to refining your understanding and mastering the intricacies of machine learning, you'll become an integral part of building the smart technology that will define our world for generations to come. So, dive into these comprehensive courses and chart your journey toward becoming a machine learning authority, shaping the future one algorithm at a time.
How much does a machine learning course cost?
The cost of a machine learning course can vary depending on the platform and subscription model. For example, on LinkedIn Learning, you can access their courses with a subscription that costs $19.99 per month when billed annually or $39.99 per month when billed monthly. They also offer a 1-month free trial.
How long do machine learning courses take?
The duration of machine learning courses can vary based on the content and depth of the subject. On LinkedIn Learning, course durations range from under an hour to several hours, such as 58 minutes, 1h 44m, and 4h 15m. It's essential to choose a course that suits your time commitment and learning needs.
What can I expect to learn in a machine learning course?
In a machine learning course, you can expect to learn concepts related to algorithms, data analysis, artificial intelligence, and natural language processing. The courses often teach various techniques for creating, training, and evaluating machine learning models, as well as their practical applications in different industries. Some courses also focus on specific tools and programming languages such as Python, SQL Server, and Mathematica.