Starting from a history of machine learning, we discuss why neural networks today perform so well in a variety of data science problems. We then discuss how to set up a supervised learning problem and find a good solution using gradient descent. This involves creating datasets that permit generalization; we talk about methods of doing so in a repeatable way that supports experimentation.
Identify why deep learning is currently popular
Optimize and evaluate models using loss functions and performance metrics
Mitigate common problems that arise in machine learning
Create repeatable and scalable training, evaluation, and test datasets
Introduction to Course
In this course you’ll get foundational ML knowledge so that you understand the terminology that we use throughout the specialization. You will also learn practical tips and pitfalls from ML practitioners here at Google and walk away with the code and the knowledge to bootstrap your own ML models.
Improve Data Quality and Exploratory Data Analysis
In this module, we will introduce data quality issues and how to improve them. We will then look at exploratory data anlaysis.
In this module, we will introduce some of the main types of machine learning and review the history of ML leading up to the state of the art so that you can accelerate your growth as an ML practitioner.
In this module we will walk you through how to optimize your ML models.
Generalization and Sampling
Now it’s time to answer a rather weird question: when is the most accurate ML model not the right one to pick? As we hinted at in the last module on Optimization -- simply because a model has a loss metric of 0 for your training dataset does not mean it will perform well on new data in the real world. You will learn how to create repeatable training, evaluation, and test datasets and establish performance benchmarks.
Dimitrios Tosidis completed this course, spending 7 hours a week on it and found the course difficulty to be easy.
This course is about an overall strategy for the machine learning and a presentation of the TensorFlow. It is a very detailed course with a real example of a model through the labs. I have studied data analytics in my MSc and I could say that this course is very clear and helpful for what someone have to do with a big data problem.