AI: Machine Learning Techniques with Scala

The ability to apply machine learning techniques to large datasets is becoming a highly sought-after skill in the world of technology. Scala can help you deliver key insights into your data—its unique capabilities as a language let you build sophisticated algorithms and statistical models. For this reason, machine learning and Scala fit together perfectly and knowledge of both would be beneficial for anyone entering the data science field.

In this course, you'll learn

• Write Scala code implementing neural network models for prediction and clustering
• Plot and analyze the structure of datasets with exploratory data analysis techniques using Scala
• Use new and popular Scala frameworks such as Akka and Spark to implement machine learning algorithms and Scala libraries such as Breeze for numerical computing and plotting
• Get to grips with the most popular machine learning algorithms used in the areas of regression, classification, clustering, dimensionality reduction, and neural networks
• Use the power of MLlib libraries to implement machine learning with Spark
• Work with the k-means algorithm and implement it in Scala with the real datasets
• Get to know what dimensionality reduction is and explore the theory behind how the PCA algorithm works
• Analyze and implement linear regression and GLMs in Scala and run them on real datasets
• Use the Naive bayes algorithms and its methods to predict the probability of different classes based on various attributes