10 Best AWS Machine Learning Courses & Certifications Online In 2023
Discover the top 10 AWS Machine Learning courses and certifications online in 2023 to boost your career in the rapidly evolving world of cloud-based artificial intelligence, curated by experts with over 456,479 enrolled students and 49,588 ratings and reviews.
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If you're seeking to expand your knowledge in AWS Machine Learning, look no further. Our team has exhaustively researched 13 popular AWS Machine Learning courses from various providers, with a staggering 456,479 enrolled students who have left a total of 49,588 ratings and reviews. We have meticulously evaluated and hand-picked the best courses based on ratings, reviews, enrollments, learner feedback, engaging content, comprehensive curriculum, release date, and affordability, coupled with our extensive experience and expertise.
In an ever-evolving field like AWS Machine Learning, having access to the most relevant and updated course material is crucial to keep up with the pace of innovation. To help you better navigate the world of AWS Machine Learning, we have compiled a diverse list of courses catering to different skill levels and areas of specialization. The carefully curated courses promise an enriching learning experience, setting you on the path to a rewarding and successful career in AWS Machine Learning. Get ready to ignite your passion and expand your skillset in the captivating world of AWS Machine Learning!
This course introduces you to the rapidly growing field of machine learning (ML), which is predicted to create an additional 58 million jobs in the next few years as per the World Economic Forum. With an increasing need for skilled professionals in this domain, this course can help you stay updated with the latest advancements, enhance your skills, and keep your career prospects in sync with the expanding market. Learn the foundational concepts of ML utilized in building AI applications and gain a competitive edge, regardless of your current expertise.
The course covers critical topics such as Machine Learning on AWS, Computer Vision on AWS, and Natural Language Processing (NLP) on AWS. The curriculum is designed to provide a thorough understanding of various ML concepts through several modules, assisted by insights from industry experts on leveraging AWS services effectively. By completing this course, you will be well-equipped to apply your newfound skills in implementing ML on AWS, thus contributing to the ever-evolving landscape of artificial intelligence services.
This course is ideal for individuals who want to gain foundational knowledge in Machine Learning using AWS services, including Computer Vision and Natural Language Processing.
This comprehensive Algorithmic Trading course is designed to teach participants how to build a truly data-driven day trading bot by covering the essential concepts and techniques, exploring powerful trading strategies, and incorporating machine learning and AWS. The curriculum emphasizes the importance of understanding the day trading business and its associated terms such as Bid-Ask Spread, Pips, Leverage, Margin Requirement, and more. In addition, the course delves into the development of unique trading strategies using Python and offers detailed explanations of various technical indicators and machine learning-powered strategies.
The course offers robust lessons on backtesting and forward testing techniques, highlighting the need for rigorous strategy testing before investing real money. Participants will also learn to factor in trading costs while developing and refining their strategies. To help traders optimize their workflows, the course provides step-by-step guidance on automating trading strategies using Python, powerful broker APIs such as Oanda, Interactive Brokers, and FXCM, and Amazon Web Services (AWS). The techniques and frameworks covered in the course can also be applied to long-term investing, making it a versatile source of knowledge for anyone interested in finance and algorithmic trading.
This course is ideal for individuals looking to comprehensively understand and build data-driven day trading bots using Python, Machine Learning, and AWS while exploring powerful trading strategies and techniques.
The aws machine learning course serves as an excellent introduction, covering a wide range of topics such as computer vision, data extraction and analysis, language processing, speech recognition, translation, and more. While focusing on the important aspects of AI, ML, and Deep Learning, the course emphasizes the applications of these technologies to enhance user experience and meet the specific needs of a business. By using Amazon's services, participants can leverage pre-built training models and perform raw inference tasks without worrying about the complexities of infrastructure management.
This course is also designed to ignite creativity in participants, as they gain a deeper understanding of machine learning and its applications. Whether you have an existing solution that could benefit from incorporating ML technologies, or you're looking to build a new solution from scratch, this course will help you explore the possibilities with AWS machine learning. Through the various services introduced in the course, learners can develop a more efficient and dynamic approach to problem-solving, ultimately improving their ability to create and optimize solutions for various business use cases.
This course is ideal for gaining a comprehensive understanding of AWS Machine Learning and its powerful capabilities, as well as implementing and operating its solutions in various real-world applications.
As AWS machine learning capabilities continue to expand and evolve, staying ahead of the curve with the latest techniques is paramount. In this course, you will learn how to efficiently use AWS tools and services to uncover the true potential of machine learning and AI in your organization. Expert instructor David Linthicum provides a comprehensive understanding of SageMaker, Amazon's primary AI platform, walking you through various use cases and scenarios that demonstrate current best practices in a myriad of fields, from healthcare to manufacturing.
This course is not just theoretical; it focuses on the practical applications of machine learning within the AWS ecosystem. You will learn how to build, train, and integrate machine learning models using SageMaker, ultimately leading to the development of real-world AI applications. Furthermore, the course addresses common concerns related to AI adoption, such as costs and security, and showcases true-to-life AWS solutions that can mitigate these concerns. By the end of the course, you will have the knowledge and tools necessary to confidently leverage cloud-based machine learning within your organization, across a wide range of applications and industries.
This course is ideal for individuals seeking a comprehensive understanding of AWS tools and services to efficiently use machine learning and AI within their organizations, with a focus on real-world applications across various industries.
Dive into the world of aws machine learning with this comprehensive and accessible course on reinforcement learning, utilizing the innovative AWS DeepRacer platform. As a hands-on introduction to machine learning concepts, you'll have the opportunity to work with an autonomous one-eighteenth-scale race car designed to test machine learning models by racing on a physical track. Taught by experienced instructor Michael Smith, this course ensures a fun and engaging learning experience, even for those without a technical background.
In this course, Michael will guide you through the fundamental tools and techniques required to develop your own reinforcement learning algorithm using the resources provided by AWS DeepRacer. With a focus on practical application and real-world examples, this course is designed to give you an in-depth understanding of how reinforcement learning operates and how it can be harnessed for success across various industries and applications. So get ready to start your engines and accelerate your machine learning journey with the power of AWS DeepRacer!
This course is best for gaining an in-depth understanding of reinforcement learning and developing practical algorithms using the innovative AWS DeepRacer platform. It provides hands-on experience in working with an autonomous race car, focusing on real-world applications and industry success.
Introduction to Machine Learning on AWS is an ideal starting point for software developers and engineers who are new to the world of AWS machine learning services. This comprehensive course is designed to familiarize learners with the various Amazon tools and services that make machine learning more accessible, efficient, and scalable. Key topics encompass a wide range of solutions, including computer vision, data extraction and analysis, language processing, speech recognition, translation, ML model training, and virtual agents.
Through their participation in this course, learners will be able to identify areas in their current solutions where AI, ML, or Deep Learning can be implemented to significantly enhance user experience and better meet business objectives. Students will appreciate how these powerful solutions can be easily integrated with existing applications, offering countless opportunities for optimization and growth. Ultimately, participants will gain the necessary knowledge and insights to harness the power of AWS machine learning services and take their projects to the next level.
This course is perfect for software developers and engineers seeking a strong foundation in AWS Machine Learning, covering various Amazon tools and services, data extraction and analysis, language processing, and more.
Prepare for your AWS Certified Machine Learning—Specialty (MLS-C01) exam with our top-notch practice exams. Benefit from unlimited retakes and a vast pool of original questions. Get expert assistance when needed and in-depth explanations for each question, including descriptions of correct solutions and annotations with images and reference links from AWS documentation. Access the exams on the go with the Udemy app, designed for mobile compatibility.
The focus of the practice exams is on four key aspects of data engineering: Exploratory Data Analytics, Modeling and Operations, Data Engineering, and the implementation of Machine Learning. These exams are designed to test a candidate's knowledge and understanding of various Artificial Intelligence services, including AWS SageMaker, Lex, Transcribe, Rekognition, Comprehend, and Personalize. In addition to questions related to AI services, the exams also include scenario-based questions that assess a candidate's understanding of Big Data services. This includes services such as Glue, Athena, S3, and the Kinesis Family. These questions are designed to evaluate a candidate's ability to apply their knowledge of data engineering in real-world situations, ensuring that they have a well-rounded understanding of the subject.
This course is the best for preparing for the AWS Certified Machine Learning—Specialty exam through top-notch practice exams, expert assistance, and in-depth explanations for each question.
Delving into the world of AWS machine learning, the Implementing and Operating AWS Machine Learning Solutions course is a comprehensive and informative journey that encompasses essential aspects related to machine learning solutions. This course is specifically designed to cater to those seeking to improve their understanding of the Machine Learning Implementation and Operations domain, which is one of the four domains addressed in the AWS Machine Learning Specialty certification exam.
The course covers several critical areas of the machine learning domain, such as the exploration of AWS services that can support a machine learning solution in production, deployment, and scaling of machine learning models using Amazon Sagemaker, and security best practices for your machine learning solution with AWS. Upon completion of the course, learners will have acquired the expertise and know-how necessary to excel in the AWS Machine Learning Specialty certification exam and demonstrate proficiency in implementing and operating machine learning solutions on the AWS platform.
This course is best for those seeking to improve their understanding of implementing and operating machine learning solutions on the AWS platform, as well as preparing for the AWS Machine Learning Specialty certification exam.
The importance of understanding how to deal with data has increased significantly in the age of information, and the course, Exploratory Data Analysis with AWS Machine Learning, aims to equip you with the necessary skills to analyze, visualize, preprocess, and feature engineer datasets in preparation for successful machine learning processes. This course is designed for beginners, and you'll be guided through utilizing relevant AWS services for performing data analysis. As data analysis is a critical step in the data preparation and feature engineering process, having a solid foundation in this area is essential for those pursuing an AWS Machine Learning specialty certification.
In the course, you'll begin by learning how to identify trends and distribution in data using basic statistics, enabling you to discover key insights hidden beneath the surface. From there, you'll be taught how to effectively visualize your dataset to uncover any overarching patterns, further strengthening your understanding of the underlying data trends. Finally, you'll acquire the skills needed to prepare your data for the machine learning pipeline by utilizing preprocessing and feature engineering techniques. By the end of the course, you will have a comprehensive understanding of exploratory data analysis and its applications in the realm of machine learning, giving you a competitive edge in the AWS Machine Learning specialty certification journey.
This course excels at teaching students how to analyze, visualize, preprocess, and feature engineer datasets in preparation for successful machine learning processes using AWS Machine Learning services.
AWS machine learning is a powerful tool that can help to automate decision-making processes and learn from data patterns. Whether you are a developer, data scientist, or just a curious mind looking to dive into the vast ocean of machine learning, "AWS Foundations: Machine Learning Basics" led by top-notch industry professionals provides a comprehensive introduction to machine learning concepts, terminologies, and processes. This course breaks down the complex world of machine learning into simple, easily digestible topics that allow you to grasp the essential concepts required to build effective machine learning models.
Throughout this course, students will explore and understand the foundations of machine learning, including how machine learning can help solve business problems, when to use machine learning models, as well as the different phases of a machine learning pipeline. By the end of the course, participants will have developed a solid understanding of the fundamentals of AWS machine learning, allowing them to apply these concepts to real-world applications confidently. So if you are looking to gain invaluable knowledge and skills in one of the most in-demand career fields, look no further than this comprehensive course.
This course is ideal for those who want to gain a comprehensive understanding of the foundations of Machine Learning and explore its various applications using AWS. It offers valuable insights for developers, data scientists, and tech enthusiasts looking to build effective models and solve real-world problems.
How to choose best AWS machine learning online course
Choosing the best AWS machine learning course is crucial for enhancing your skills and achieving your desired goals, whether you want to gain an understanding of AWS machine learning capabilities, implement and operate solutions, or even obtain a certification. To choose the right course, you need to consider various essential factors which will ensure the course is in sync with your objectives and offers the most suitable learning experience. In this learners' guide, we'll discuss some crucial points to look out for when choosing an AWS machine learning course.
First and foremost, evaluate the course's content depth, structure, and relevance to your objectives. Make sure the course covers important topics and offers hands-on experience, applicable to real-world scenarios.
Once you have narrowed it down to a few courses, examine the instructor's profile, including their expertise, teaching methods, and experience in the field to ensure you receive high-quality learning from an industry professional.
Read reviews and testimonials from past students to gauge the effectiveness and satisfaction derived from the course. Positive feedback is an indication that the course delivered value to its attendees.
Don't forget to consider the course format and duration, as this will impact how easily you can fit the course into your daily schedule. If you're working or have other commitments, look for flexible course offerings, like self-paced or part-time options.
If you're interested in obtaining a certification, pay attention to the course's alignment with exam objectives and whether it offers practice materials, such as sample questions, to help you prepare for the certification test.
Lastly, compare course costs and consider your budget. While generally, higher-priced courses may offer more comprehensive material, don't overlook more affordable options that might also provide excellent value.
In conclusion, selecting the right AWS machine learning course is essential to achieving your learning objectives. By considering the factors mentioned above, you can evaluate various course offerings and make an informed decision, drastically improving your chances for success in the world of AWS machine learning.
As you progress through the expertly crafted AWS Machine Learning courses in this compilation, you'll gain a thorough understanding of the vast capabilities that AWS Machine Learning has to offer. These courses will not only help you learn how to implement and operate AWS Machine Learning solutions effectively, but they will also prepare you to obtain certifications, setting you apart in the competitive world of cloud computing. With a strong foundation in machine learning on AWS and exposure to real-world applications, you'll be able to unlock your full potential in the field of machine learning and build an exciting career for yourself.
Whether you are a beginner looking to explore the basics of machine learning on AWS, or an experienced professional seeking to expand your knowledge in specialized areas such as algorithmic trading and exploratory data analysis, these courses offer something for everyone. The valuable insights, engaging learning experiences, and practical exam materials provided in these courses will ensure that you develop the skills you need to excel in your AWS Machine Learning journey. So, go ahead and dive into the comprehensive world of AWS Machine Learning courses and begin your rewarding career today!
How much does an AWS machine learning course cost?
The cost of AWS machine learning courses varies depending on the platform and subscription model. Some platforms offer free trials or free access to courses without a certificate, while others require a monthly or annual subscription fee. Prices range from $29 per month for Pluralsight to $99.99 for individual courses on Udemy. Coursera's and LinkedIn Learning's subscriptions range between $19.99 and $59 per month.
How long do AWS machine learning courses take?
The duration of AWS machine learning courses varies depending on the course content and objectives. Course lengths can range from as short as 29 minutes to as long as 41 hours and 35 minutes. On average, expect the courses to take several hours to complete, as they are designed to provide a comprehensive understanding of the subject matter.
What are the prerequisites for enrolling in an AWS machine learning course?
Prerequisites for enrolling in an AWS machine learning course depend on the course's content and difficulty level. Generally, most courses assume that the learner has some foundational knowledge in programming, such as Python, and basic understanding of machine learning concepts. Some more advanced courses may require deeper knowledge in a specific area or prior experience with AWS services. Check the course descriptions and prerequisites on the platform to ensure you meet the requirements before enrolling.