Top 10 Machine Learning Scientist Courses To Take In 2022

Machine learning is important because it gives companies insight into customer behavior trends and business operating models, while supporting new product development. Many of today’s leading companies, such as Facebook, Google, and Uber, are making machine learning central to their operations. Machine learning has become an important competitive differentiator for many companies. Here are the 10 best machine learning scientist courses you can take in 2022.

Machine Learning Scientist with Python at Datacamp

In this course, you will learn how to process data for features, train your models, evaluate performance, and tune parameters for better performance. During the process, you’ll get an introduction to natural language processing, image processing, and popular libraries like 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 and in-demand field of machine learning. Through a series of practical case studies, you will gain applied experience in key areas of machine learning, including prediction, classification, clustering, and information retrieval. You will learn how to analyze large and complex data sets, build systems that adapt and improve over time, and build smart applications that can make predictions from data.

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

Upon completion of this course, you will learn best practices for training and developing test sets and analyzing bias/variance to build deep learning applications; be able to use standard neural network techniques such as initialization, L2 regularization and dropout, 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 verify 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 dataset to uncover potentially predictive relationships. As you build the movie recommender system, you will learn how to train algorithms using training data so that you can predict the outcome of future data sets. 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 curriculum has been carefully designed by MIT faculty to equip you with the skills and knowledge to apply data science techniques to help you make data-driven decisions. Encompass technologies most relevant to the business, such as machine learning, deep learning, NLP, recommender systems, and more.

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Machine Learning at Google Cloud

In this course, you’ll experience end-to-end machine learning on Google Cloud, starting with building a machine learning-focused strategy and progressing to training, optimization, and model 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 walks through the benefits of this easy-to-use API, then quickly moves on to practical techniques, starting with linear and logistic regression, decision trees, and random forest models.

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Machine learning with Python: the basics at Linkedin

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

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Applied Machine Learning: Algorithms

This course goes from logistic regression to gradient magnification and shows how to define a structure that guides you in choosing the best one for the problem at hand. Each algorithm has its pros and cons, making each the preferred choice for certain types of problems. Understanding what really drives each algorithm, along with its pros and cons, can give you a significant competitive edge as a data scientist.

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Power BI: integrating AI and machine learning

This course introduces existing AI and machine learning capabilities available on directly inaccessible Power BI features. Data and business analytics specialist Helen Wall gives you a helpful overview of Power BI, then dives into the steps for setting up Power Query and your data model.

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Sherry J. Basler