As technology continues to advance, the field of machine learning has become increasingly popular. Machine learning involves using algorithms and statistical models to enable a machine to learn from data and make decisions without being explicitly programmed. This field has become essential in various industries, including healthcare, finance, and marketing. However, building a machine learning model requires significant expertise and resources. This is where Automated Machine Learning (AutoML) comes in.
AutoML is the process of automating the end-to-end process of applying machine learning to real-world problems. AutoML platforms use various techniques, including neural architecture search, to automate the model building process, making it easier for non-experts to build machine learning models.
One popular AutoML platform is Google AutoML, which allows developers with limited machine learning expertise to train high-quality models for image recognition, language translation, and other applications. Another popular platform is Apple’s Core ML, which is a machine learning framework that enables developers to integrate machine learning models into their iOS applications.
Automated Machine Learning for Beginners (Google & Apple)
To help beginners learn about AutoML, Udemy offers a course called “Automated Machine Learning for Beginners (Google & Apple).” This course aims to teach learners how to build machine learning models using Google AutoML and Apple’s Core ML platforms. The course is designed for beginners with no prior knowledge of machine learning or programming.
The course is divided into ten sections, with each section covering a specific topic related to AutoML. The first section provides an overview of machine learning and AutoML, while the second section covers the fundamentals of Python programming, which is the programming language used in the course.
In the third section, learners are introduced to Google AutoML and learn how to create a custom image classification model using AutoML. In the fourth section, learners learn how to train and evaluate a custom language model using Google AutoML’s Natural Language Processing (NLP) features.
The fifth section of the course covers Apple’s Core ML and how to use it to integrate machine learning models into iOS applications. In the sixth section, learners learn how to use Core ML to build a custom image classifier for iOS applications. The seventh section covers Core ML’s NLP features, and learners learn how to build a custom language model for iOS applications.
The eighth section covers transfer learning, which is a technique used to retrain an existing machine learning model for a new task. The ninth section of the course covers hyperparameter tuning, which is the process of selecting the best combination of parameters for a machine learning model.
Finally, the course concludes with a section on deploying machine learning models to production. Learners learn how to deploy their custom models built using Google AutoML and Apple’s Core ML to the cloud and iOS applications.
Overall, the “Automated Machine Learning for Beginners (Google & Apple)” course on Udemy is an excellent resource for beginners who want to learn about AutoML. The course is well-structured, and the instructor does an excellent job of explaining complex concepts in an easy-to-understand way. By the end of the course, learners will have the knowledge and skills required to build machine learning models using Google AutoML and Apple’s Core ML platforms.