AI: Machine Learning Techniques with Scala
Explore the most innovative and cutting edge 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
StartLecture 9 Supervised Learning Problem Formulation (2:41)
StartLecture 10 Two Basic Regression Algorithms (4:13)
StartLecture 11 Implementing Linear Regression and GLMs in Scala (4:25)
StartLecture 12 Two Basic Classification Algorithms (4:32)
StartLecture 13 Implementing K-Nearest Neighbors and Naive Bayes in Scala (7:26)
StartLecture 14 Model Selection (5:15)
Bill Gates Says These Are the Jobs He Would Drop Out of College for Today
If he were to go into computer sciences today, Gates said, the area that he thought had the most potential was artificial intelligence. Gates brought up a recent victory by Google DeepMind over the top player in the world at Go, a game some predicted a computer could never master. He called it a remarkable achievement that signaled there is more to come in advancement in artificial intelligence. And he said the research being done in the field now is "profound" and on the verge of making new breakthroughs. "The ability for artificial agents to read and understand material is going to be phenomenal," says Gates. "Anything connected with that would be an exciting lifetime career."