Machine Learning Essentials for Scala Developers is a three-day course designed to provide a solid introduction to the world of machine learning using the Scala language. Throughout the hands-on course, you’ll explore a range of machine learning algorithms and techniques, from supervised and unsupervised learning to neural networks and deep learning, all specifically crafted for Scala developers. Our expert trainer will guide you through real-world, focused hands-on labs designed to help you apply the knowledge you gain in real-world scenarios, giving you the confidence to tackle machine learning challenges in your own projects.

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* Actual course outline may vary depending on offering center. Contact your sales representative for more information.

Learning Objectives

Working in a hands-on learning environment led by our expert instructor you’ll:
Grasp the fundamentals of machine learning and its various categories, empowering you to make informed decisions about which techniques to apply in different situations.
Master the use of Scala-specific tools and libraries, such as Breeze, Saddle, and DeepLearning.scala, allowing you to efficiently process, analyze, and visualize data for machine learning projects.
Develop a strong understanding of supervised and unsupervised learning algorithms, enabling you to confidently choose the right approach for your data and effectively build predictive models
Gain hands-on experience with neural networks and deep learning, equipping you with the know-how to create advanced applications in areas like natural language processing and image recognition.
Explore the world of generative AI and learn how to utilize GPT-Scala for creative text generation tasks, broadening your skill set and making you a more versatile developer.
Conquer the realm of scalable machine learning with Scala, learning the secrets to tackling large-scale data processing and analysis challenges with ease.
Sharpen your skills in model evaluation, validation, and optimization, ensuring that your machine learning models perform reliably and effectively in any situation.

1

  • INTRODUCTION TO MACHINE

  • Learning and Scala

    Learning Outcome: Understand the fundamentals of machine learning and Scala's role in this domain.

    What is Machine Learning?

    Machine Learning with Scala: Advantages and Use Cases

2

  • SUPERVISED LEARNING IN SCALA

  • Learn the basics of supervised learning and how to apply it using Scala.

    Supervised Learning: Regression and Classification

    Linear Regression in Scala

    Logistic Regression in Scala

3

  • UNSUPERVISED LEARNING IN SCALA

  • Understand unsupervised learning and how to apply it using Scala.

    Unsupervised Learning:Clustering and Dimensionality Reduction

    K-means Clustering in Scala

    Principal Component Analysis in Scala

4

  • NEURAL NETWORKS AND DEEP LEARNING IN SCALA

  • Learning Outcome: Learn the basics of neural networks and deep learning with a focus on implementing them in Scala.

    Introduction to Neural Networks

    Feedforward Neural Networks in Scala

    Deep Learning and Convolutional Neural Networks

5

  • INTRODUCTION TO GENERATIVE AI AND GPT IN SCALA

  • Gain a basic understanding of generative AI and GPT, and how to utilize GPT-Scala for natural language tasks.

    Generative AI: Overview and Use Cases

    Introduction to GPT (Generative Pre-trained Transformer)

    GPT-Scala: A Library for GPT in Scala

6

  • REINFORCEMENT LEARNING IN SCALA

  • Understand the basics of reinforcement learning and its implementation in Scala.

    Introduction to Reinforcement Learning

    Q-learning and Value Iteration

    Reinforcement Learning with Scala

7

  • TIME SERIES ANALYSIS USING SCALA

  • Learn time series analysis techniques and how to apply them in Scala.

    Introduction to Time Series Analysis

    Autoregressive Integrated Moving Average (ARIMA) Models

    Time Series Analysis in Scala

8

  • NATURAL LANGUAGE PROCESSING (NLP) WITH SCALA

  • Gain an understanding of natural language processing techniques and their application in Scala.

    Introduction to NLP: Techniques and Applications

    Text Processing and Feature Extraction

    NLP Libraries and Tools for Scala

9

  • IMAGE PROCESSING AND COMPUTER VISION WITH SCALA

  • Learn image processing techniques and computer vision concepts with a focus on implementing them in Scala.

    Introduction to Image Processing and Computer Vision

    Feature Extraction and Image Classification

    Image Processing Libraries for Scala

10

  • MODEL EVALUATION AND VALIDATION

  • Understand the importance of model evaluation and validation, and how to apply these concepts using Scala.

    Model Evaluation Metrics

    Cross-Validation Techniques

    Model Selection and Tuning in Scala

11

  • SCALABLE MACHINE LEARNING WITH SCALA

  • Learn how to handle large-scale machine learning problems using Scala.

    Challenges of Large-Scale Machine Learning

    Data Partitioning and Parallelization

    Distributed Machine Learning with Scala

12

  • MACHINE LEARNING DEPLOYMENT AND PRODUCTION

  • Understand the process of deploying machine learning models into production using Scala.

    Deployment Challenges and Best Practices

    Model Serialization and Deserialization

    Monitoring and Updating Models in Production

13

  • ENSEMBLE LEARNING TECHNIQUES IN SCALA

  • Discover ensemble learning techniques and their implementation in Scala.

    Introduction to Ensemble Learning

    Bagging and Boosting Techniques

    Implementing Ensemble Models in Scala

14

  • FEATURE ENGINEERING FOR MACHINE LEARNING IN SCALA

  • Learn advanced feature engineering techniques to improve machine learning model performance in Scala.

    Importance of Feature Engineering in Machine Learning

    Feature Scaling and Normalization Techniques

    Handling Missing Data and Categorical Features

15

  • ADVANCED OPTIMIZATION TECHNIQUES FOR MACHINE LEARNING

  • Understand advanced optimization techniques for machine learning models and their application in Scala.

    Gradient Descent and Variants

    Regularization Techniques (L1 and L2)

    Hyperparameter Tuning Strategies

Audience

This course is geared for experienced Scala developers who are new to the world of machine learning and are eager to expand their skillset. Professionals such as data engineers, data scientists, and software engineers who want to harness the power of machine learning in their Scala-based projects will greatly benefit from attending. Additionally, team leads and technical managers who oversee Scala development projects and want to integrate machine learning capabilities into their workflows can gain valuable insights from this course.

Language

English

Prerequisites

Students should have practical skills equivalent to or should have attended the following course(s) as a prerequisite: TTSCL2104 Fast Track to Scala for OO / Java Developers (4 days)

$2,295

Length: 3.0 days (24 hours)

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29
Nov
Wednesday
10:00 AM ET -
6:00 PM ET
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