Learn free complete course of Scala for Machine Learning. This course is very useful for those who want to learn machine learning and totally for of cost. Machine learning is data analysis method which automates analytical model building. Machine learning is widely used in medical diagnosis, image processing, learning association, prediction, classification, regression etc. Scala language has fade over the years, its use seems to be growing at a steady clip, and the experience of usage is the language is improving immediately. In this course you will learn Mathematical notation for the curious and Why machine learning is very important. In this section you will learn about machine learning Classification, Prediction, Optimization and Regression. In this course you will learn about Scala and Abstraction Scalability, Configuration, Maintainability Scala and Computation on demand of Scala in detail.
In this course you will learn about Model categorization in detail. And you will learn about these topics in detail that are divided in sections: Taxonomy of machine learning algorithms, Unsupervised learning, Clustering, Dimension, reduction Supervised learning, Generative models, Discrimination of models and Reinforcement learning. You will also learn Tools and frameworks of machine learning and also learn Java, Scala, Apache Commons Math, Description of tools, Overview of computational workflows,Writing a simple workflow,Selecting a dataset, Loading the dataset, Preprocessing the dataset, Creating a model (learning), Classify the data of tools. You will also learn Time series moving averages, The simple moving average, The weighted moving average, The exponential moving average, Fourier analysis, Discrete Fourier transform, DFT-based filtering, Detection of market cycles in detail.
You will also learn these Scala Machine learning topics in detail:
Kernel Models and Support Vector Machines Kernel functions complete topics and Common discriminative kernels in detail.
Artificial Neural Networks and Feed-forward neural networks with their sub topics.
The multi layer perception with the activation function, the network architecture, Software design, Model definition
Layers, Synapses and Connections.Genetic Algorithms Evolution in detail.Genetic algorithms and machine learning and Genetic algorithm components.
You will also learn Trading operators, The unfitness function, Trading signals, Trading strategies, Signal encoding in detail.
Detail of Advantages and risks of genetic algorithms.
Complete topic of Reinforcement Learning Introduction in detail.
- Complete detail of machine learning classifier systems
- Introduction to LCS in machine learning classifier systems
- Why LCS is useful
- Terminology of machine learning classifier systems
- Extended learning classifier systems machine learning classifier systems
- XCS components machine learning classifier systems
- Application to portfolio management
- XCS core data of machine learning classifier systems
- XCS rules of machine learning classifier systems
- Covering of machine learning classifier systems
- Example of implementation of machine learning classifier systems
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