Master Regression Modeling with Angular & TensorFlow.js
What you will learn:
- Build linear and non-linear regression models in TensorFlow.js
- Master the fundamentals of machine learning model training
- Develop smart applications with Angular and TypeScript
- Gain expertise in linear and non-linear regression techniques
- Utilize Angular for scientific computing and machine learning
- Practical TypeScript coding skills through hands-on examples
Description
Unlock the power of predictive modeling with this comprehensive course! Learn to build both linear and non-linear regression models using TensorFlow.js within the Angular framework. This practical course goes beyond theory, guiding you through the creation of a real-world application to predict perceived temperature, showcasing the effectiveness of regression analysis in a tangible context. We'll explore the nuances of model selection (e.g., Absolute Error Summation vs. Mean Squared Error), training techniques, and visualization, all within a clear, step-by-step learning environment. No prior experience in machine learning is required; we'll cover the fundamental concepts of machine learning along the way, making this course perfect for developers seeking to integrate predictive capabilities into their applications. You'll master the art of data manipulation, model building, and performance evaluation with TensorFlow.js, learning best practices and advanced methods to fine-tune your models for optimal accuracy. This course integrates seamlessly with Angular, allowing you to visualize the results and build interactive web applications using your machine learning models, all without server-side dependencies. Leverage Angular's power to create dynamic user interfaces, and gain a strong understanding of TypeScript along the way. This course provides a wealth of resources, including supplementary materials like a GitBook and GitHub repository to extend your learning journey beyond the course content. Take advantage of our regular course updates, designed to continuously enhance your learning experience based on your feedback. Start building your predictive models today!
Curriculum
Essential Coding Techniques
This section provides crucial foundational coding tips. Learn effective strategies for reusing code snippets to improve efficiency and interpret training graphs for better model understanding. The "Tips on learning and reusing code code snippet" lecture covers practical techniques for optimizing your workflow, while "Interpreting the training graph" provides insights into interpreting model training data and diagnosing performance issues.
Understanding Regression Models
Gain a deep understanding of simple regression models and their practical applications. Lectures cover model introduction, the limitations and appropriate use cases of simple models, and how to evaluate different methods such as Absolute Error Summation and Mean Squared Error for model optimization. Crucial concepts such as the relationship between training and validation data in model development are addressed, ensuring you understand the core principles of training a successful regression model.
Nonlinear Regression and Neural Networks
Explore the world of nonlinear regression and delve into the inner workings of neural networks. Lectures cover the conceptual foundation of neural networks, and address common misconceptions. A key concept covered here is the understanding of 'features' in machine learning which are essential for model creation and optimization.
Building a Linear Regression Model
This section guides you through the practical implementation of a linear regression model for predicting perceived temperature. You will set up the environment for the project, address initial challenges, and run a simple model to build your foundational understanding of TensorFlow.js. A discussion on objective ignorance and its relevance to the analysis will be included, to assist with model evaluation and interpretation.
Data Handling and Visualization
Master data handling techniques. Learn how to seamlessly upload your data from Google Spreadsheets, and master data exploration techniques. This section focuses on setting up data visualization and understanding your dataset prior to modelling. These lectures focus on practical data analysis, visualization and preparing your data for use in TensorFlow.js.
TensorFlow.js Model Creation and Training
This pivotal section focuses on building and training your regression model using TensorFlow.js. You’ll learn about the various settings, interpretation of the training curve, batch size adjustments, and techniques for adding features to enhance model performance. You’ll delve into the intricacies of creating tensors from your dataset and setting up optimal training parameters, culminating in the training process itself. Additionally, you'll learn how to create test graphs to evaluate the model's performance and accuracy.
