10 Best Machine Learning Courses & Certifications Online On Pluralsight To Take In 2023

Best Machine Learning Courses & Certifications On Pluralsight

1. How Machine Learning Works

This course offers a programmer-friendly approach to demystify the magical world of machine learning, enabling students to build their own computer vision program from scratch. By focusing on the practical implementation of machine learning concepts, learners will gain a strong understanding of the underlying processes involved in teaching computers to understand images and text.

In the course, "How Machine Learning Works," students will first explore supervised learning and then gradually progress towards coding their own learning program, improving it one step at a time. No prior experience with machine learning libraries is required as learners will be guided on writing the program independently. Upon completion, students will possess a functional computer vision program capable of recognizing handwritten characters and have a solid grasp of the foundational ideas of machine learning.

2. Building Machine Learning Models in SQL Using BigQuery ML

Building Machine Learning Models in SQL Using BigQuery ML focuses on the democratization of machine learning by allowing data analysts and engineers to build and utilize machine learning models directly from SQL without the need for higher-level programming languages. The course guides learners through building and training machine learning models for linear and logistic regression using SQL commands on BigQuery, part of the Google Cloud Platform's serverless data warehouse. Students will explore the various options available on GCP for building and training models, and learn how to make informed decisions on which services to use based on their specific needs.

Throughout the course, participants will work with real-world datasets stored in BigQuery to build linear regression and binary classification models. By specifying training parameters in SQL, BigQuery makes machine learning accessible even to those who are not familiar with high-level programming languages. Furthermore, students will learn how to analyze the models built using evaluation and feature inspection functions in BigQuery. Practical exercises include running BigQuery commands on Cloud Datalab using a Jupyter notebook that is hosted on GCP and closely integrated with its services. Upon completion, learners will have gained a comprehensive understanding of how to utilize BigQuery ML to extract valuable insights from their data by applying linear and logistic regression models.

3. Deep Learning Using TensorFlow and Apache MXNet on Amazon Sagemaker

Deep Learning Using TensorFlow and Apache MXNet on Amazon Sagemaker offers an in-depth introduction to SageMaker, a powerful and fully managed machine learning platform on AWS, which simplifies prototyping, building, training, and hosting ML models. The course covers a range of topics, including how to use built-in algorithms, such as linear learner and PCA algorithms hosted on SageMaker containers, to minimize the amount of custom code required to prepare your data for machine learning models.

Throughout the course, learners will explore three different techniques for building custom models on SageMaker: bringing in a pre-trained model and hosting it on SageMaker's first-party containers; building a model using Apache MXNet; and creating a custom container to be trained on SageMaker. Additionally, participants will learn how to connect to other AWS services, such as S3 and Redshift, to access their training data and run training in a distributed manner. This comprehensive course also covers autoscaling techniques for optimizing model performance across various use cases and applications.

4. Microsoft Azure AI Engineer: Developing ML Pipelines in Microsoft Azure

In the ever-evolving world of artificial intelligence, the role of a Microsoft Azure AI engineer has become increasingly essential. This course is tailored for data professionals, developers, and IT personnel who need to efficiently collaborate on data science projects and consistently develop high-quality machine learning models in Microsoft Azure. Gaining a comprehensive understanding of the Microsoft Azure Machine Learning service is integral to success in this field.

The course provides expert guidance on creating no-code machine learning pipelines using Azure ML service visual designer and training ML models utilizing Python, Jupyter notebooks, and Microsoft Azure Machine Learning workspace. Additionally, participants will learn to monitor their Azure Machine Learning environments from both data scientist and data engineer perspectives. Upon completion of the course, students will have established a strong foundation in Microsoft Azure Machine Learning service, positioning them for success in the Microsoft Azure AI engineer job role.

5. Implementing and Operating AWS Machine Learning Solutions

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.

6. Artificial Intelligence: The Big Picture of AI

This comprehensive course is designed to provide a solid understanding of artificial intelligence and its potential implications for individuals, businesses, and the future. With a strong focus on modern data-driven AI technologies such as machine learning, deep learning, and reinforcement learning, this course aims to equip learners with the knowledge and tools needed to stay competitive in a rapidly evolving technological landscape.

In addition to exploring cutting-edge AI techniques, the course also delves into the practical applications of AI tools and their impact on various industries, labor markets, and societies as a whole. By the end of this course, learners will have a well-rounded understanding of the AI landscape, enabling them to make informed decisions about their careers and adapt to advancements in the field. Overall, this course is a valuable resource for anyone looking to gain a deeper understanding of artificial intelligence and its transformative potential.

7. Designing a Machine Learning Model

The world of Machine Learning continues to expand rapidly, and it is becoming increasingly necessary for professionals in the field to know how to design a machine learning model tailored to a specific problem and dataset. This course takes you through the key considerations involved in crafting a customized machine learning model, including comparisons of various canonical problems and identifying the right solution techniques based on the given problem and available data.

Throughout the course, you'll develop a well-rounded understanding of rule-based systems versus machine learning systems, how traditional and deep learning models function, and the differences between supervised, unsupervised, and reinforcement learning techniques. You'll gain insights into classic supervised learning methods like regression and classification as well as unsupervised techniques such as clustering and dimensionality reduction. Additionally, you'll explore the assumptions and outcomes of these techniques, how solutions can be evaluated, and how to design end-to-end machine learning workflows for canonical problems, ensemble learning, and neural networks. Upon completion of the course, you'll possess the skills and knowledge needed to correctly identify the ideal machine learning problem setup and apply the most suitable solution technique for your specific use-case.

8. Foundations of Statistics and Probability for Machine Learning

This machine learning focused course provides comprehensive insights into the fundamentals of statistics and probability, thus empowering learners to build and interpret significant machine learning models. Key concepts covered during the course include basic statistics, probability, hypothesis testing, and regression analysis. The course emphasizes on the application of these statistical concepts in data analysis as a precursor to employing machine learning methods.

Throughout the course, learners will gain knowledge about diverse topics such as measures of central tendency and dispersion, principles of probability and probability distributions, and understanding of skewness and kurtosis. Moreover, students will delve into hypothesis testing, interpreting statistical test results, and developing regression models with single and multiple predictors. They will also acquire essential evaluation techniques for regression models using R-squared and adjusted R-squared while understanding the significance of t-statistic and p-value associated with regression coefficients. Overall, this course will equip learners with the statistical expertise needed to explore, analyze, and interpret data in the realm of machine learning.

9. Build, Train, and Deploy Your First Neural Network with TensorFlow 2

Designed for those looking to dive into the world of machine learning, this course offers a thorough understanding of the fundamentals needed to build, train, and deploy a neural network using TensorFlow 2. TensorFlow is an open-source machine learning framework that simplifies the process of developing neural network-based models, making it more accessible for a variety of developers. Throughout this course, learners will first gain insight into the basic principles of machine learning and how it empowers the creation of models that learn from data.

Furthermore, students will learn how to apply these principles to neural networks, specifically working on a model designed to predict clothing types in images. The course also highlights TensorFlow's unique features, such as TensorBoard, which allows learners to easily evaluate and optimize the performance of their neural networks. By the end of the course, participants will have the skills and knowledge required to successfully create, train, and deploy a predictive neural network using TensorFlow, thereby unlocking the potential to integrate machine learning capabilities into various client applications.

10. Building Machine Learning Models in Spark 2

As the demand for processing large volumes of data efficiently continues to grow, leveraging the power of distributed environments becomes increasingly important. In response to this need, the course focuses on training machine learning (ML) models using Spark, a leading big data processing engine. By examining the use of Spark 2.x's distributed processing environment, you will gain valuable insights into building and training various ML models, including regression, classification, clustering, and recommendation systems.

This comprehensive course begins by introducing the two ML libraries available in Spark 2 - the older spark.mllib library built on top of RDDs, and the newer spark.ml library built on top of dataframes. The comparative analysis of these libraries will equip you to make informed decisions about which library is best suited for a particular task. Through practical examples, such as building a classification model using Decision Trees, the course showcases the process of implementing these models within the Spark 2 environment. Moreover, you will explore advanced features exclusive to Spark 2, like the ML pipelines used to chain data transformations and ML operations. By the end of this course, you will have a strong grasp on using the advanced features in Spark 2 for machine learning and distributed training at scale.

Compare Machine learning Online Courses

How to choose best Machine learning online course

Choosing the best machine learning course can be a daunting task, especially with the myriad of options available in the market. As a learner, you must align your objectives with the course content to ensure maximum value for your investment. Here are some critical factors to consider when selecting a machine learning course:

• Course content that covers your specific user intents, such as learning the basics of machine learning, understanding the development of ML pipelines in various platforms, or gaining expertise in deep learning using popular tools like TensorFlow and Apache MXNet.
• Practical application and hands-on experience, which allow you to apply your knowledge to real-life scenarios and build your own machine learning models for different use cases.
• The credibility of the course providers and instructors, ensuring they have a proven track record in machine learning and can deliver up-to-date industry insights.
• Access to a robust community and networking opportunities that can help you engage with fellow learners, industry professionals, and potential employers in the field of machine learning.
• A comprehensive curriculum that includes foundational concepts of statistics, probability, and artificial intelligence, as well as in-depth coverage of advanced techniques and popular tools like TensorFlow and Spark.

In conclusion, ensure the course you choose aligns with your learning goals, offers hands-on experience, and is provided by credible sources. Additionally, pay close attention to the avenues for networking and support, as this plays a significant role in your success as a machine learning professional.

Conclusion

In conclusion, this comprehensive list of machine learning courses caters to a diverse range of learners with varying objectives in the field. By exploring these options, you are not only investing in your education but securing a strong foundation in the ever-expanding field of machine learning. It's the perfect opportunity to strengthen your knowledge, build essential skills, and stay ahead of the curve as you embark on your journey to becoming an expert in AI, ML, and data analysis.

Seize this opportunity to fuel your passion for learning and propel your career to new heights. With determination, hard work, and the right guidance from these carefully crafted courses, success will be yours to achieve. So, don't wait any longer – dive right in and start exploring the vast world of machine learning to see where it takes you. Let the transformation begin!