Top 10 Machine Learning Scientist Courses to Take up in 2022

Machine learning is important because it gives enterprises a view of trends in customer behavior and business operational patterns, as well as supports the development of new products. Many of today's leading companies, such as Facebook, Google, and Uber, make machine learning a central part of their operations. Machine learning has become a significant competitive differentiator for many companies. Here are the top 10 machine learning scientist courses that you can take up in 2022.

Machine Learning Scientist with Python at Datacamp

In this course, you'll learn how to process data for features, train your models, assess performance, and tune parameters for better performance. In the process, you'll get an introduction to natural language processing, image processing, and popular libraries such as Spark and Keras.

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Machine Learning Specialization at the University of Washington

This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data.

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Improving Deep Neural Networks: Hyperparameter tuning, Regularization, and Optimization at Deeplearning.AI

By the end of this course, you will learn the best practices to train and develop test sets and analyze bias/variance for building deep learning applications; be able to use standard neural network techniques such as initialization, L2 and dropout regularization, hyperparameter tuning, batch normalization, and gradient checking; implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence; and implement a neural network in TensorFlow.

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Data Science: Machine Learning

In this course, you will learn about training data, and how to use a set of data to discover potentially predictive relationships. As you build the movie recommendation system, you will learn how to train algorithms using training data so you can predict the outcome for future datasets. You will also learn about overtraining and techniques to avoid it such as cross-validation. All of these skills are fundamental to machine learning.

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Data Science and Machine Learning: Making Data-Driven Decisions at MIT

The Data Science and Machine Learning Program curriculum have been carefully crafted by MIT faculty to provide you with the skills & knowledge to apply data science techniques to help you make data-driven decisions. Encompass the most business-relevant technologies, such as Machine Learning, Deep Learning, NLP, Recommendation Systems, and more.

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Machine Learning at Google Cloud

In this course you will experiment with end-to-end machine learning on Google Cloud, starting from building a machine learning-focused strategy and progressing into model training, optimization, and production.

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Machine Learning with Scikit-Learn at Linkedin

In this course, data scientist Michael Galarnyk explains how to use scikit-learn for supervised and unsupervised machine learning. Michael reviews the benefits of this easy-to-use API and then quickly segues to practical techniques, starting with linear and logistic regression, decision trees, and random forest models.

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Machine Learning with Python: Foundations at Linkedin

In this course, Frederick Nwanganga introduces machine learning in an approachable way and provides step-by-step guidance on how to get started with machine learning via the most in-demand language in use today, Python. Frederick starts with exactly what it means for machines to learn and the different ways they learn, then get into how to collect, understand, and prepare data for machine learning.

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Applied Machine Learning: Algorithms

This course ranges from logistic regression to gradient boosting and shows how to set a structure that guides you through picking the best one for the problem at hand. Each algorithm has its pros and cons, making each one the preferred choice for certain types of problems. Understanding what actually drives each algorithm, as well as its benefits and drawbacks, can give you a significant competitive advantage as a data scientist.

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Power BI: Integrating AI and Machine Learning

This course showcases existing AI and machine learning capabilities available directly inaccessible Power BI functionalities. Data analytics and business analysis expert Helen Wall gives you a useful overview of Power BI, then dives into the steps to configure Power Query and your data model.

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