Machine Learning Essentials for Scala Developers (TTML5506-S)

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
• Lab: Setting up your environment (OPTIONAL)

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
• Lab: Linear Regression Model

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
• Lab: K-means Clustering: Apply K-means clustering to a dataset in Scala using Breeze.

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
• Lab: Feedforward Neural Network: Build a simple feedforward neural network in Scala using DeepLearning.scala.

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
• Lab: Text Generation with GPT-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
• Lab: Q-learning in Scala: Implement a simple Q-learning algorithm in Scala to solve a gridworld problem.

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
• Lab: Time Series Forecasting: Perform time series forecasting using Scala with an ARIMA model.

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
• Lab: Text Classification in Scala: Description: Implement a text classification model in Scala using Breeze and Saddle.

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
• Lab: Image Classification in 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
• Lab: Model Evaluation 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
• Lab: Parallelized Machine Learning: Implement a parallelized version of a machine learning algorithm in 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
• Lab: Model Deployment with Scala

Addendum: Next Steps in Machine Learning with Scala

Bonus Chapters / Time Permitting

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
• Lab: Implementing a Random Forest model

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
• Lab: Feature Selection and Transformation

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
• Lab: Hyperparameter Tuning with Grid Search 

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.

If your team requires different topics, additional skills or a custom approach, our team will collaborate with you to adjust the course to focus on your specific learning objectives and goals.

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.

Take Before: Students should have practical skills equivalent to or should have attended the following course(s) as a pre-requisite:

• Fast Track to Scala for OO / Java Developers

Question: What if I have to reschedule my class due to conflict?

Answer: Ten (10) business days’ notice is required to reschedule a class with no additional fees. Notify TOPTALENT LEARNING as soon as possible at 469-721-6100 or by written notification to info@toptalentlearning.com to avoid rescheduling penalties.

Question: How do I enroll for this class?

Answer: Please contact our team at 469-721-6100; we will gladly guide you through the online purchasing process.

Question: What happens once I purchase a class?

Answer: You will receive a receipt and an enrollment confirmation sent to the email you submitted at purchase. Your enrollment email will have instructions on how to access the class. Any additional questions our team is here to support you. Please call us at 469-721-6100.

Question: What is your late policy?

Answer: If a student is 15 minutes late, they risk losing their seat to a standby student. If a student is 30 minutes late or more, they will need to reschedule. A no-show fee will apply. Retakes are enrolled on a stand-by basis. The student must supply previously issued courseware. Additional fees may apply.

Question: What happens when I finish my class?

Answer: You will receive a ‘Certificate of Completion’ once you complete the class. If you purchased an exam voucher for the class, a team member from TOPTALENT LEARNING will reach out to discuss your readiness for the voucher and make arrangements to send it.

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