Introduction to Machine Learning
There has never been a more exciting time to be a deep learning expert. With the advent of super-fast computers, open source algorithms, well-curated datasets, and affordable cloud services, deep learning experts are armed with the requisite skills to build amazing and impactful applications across all domains. Computer vision, natural language processing, and time series analysis are just a few of the areas where deep learning experts can make a real impact. Anyone with the right skills can build a groundbreaking application and perhaps become the next Elon Musk. For this to happen, adequate knowledge of deep learning frameworks such as TensorFlow is required.
The TensorFlow Developer Certificate aims to build a new generation of deep learning experts who are already in high demand across all fields. Hence, joining this club equips you with the required expertise to start your journey as a deep learning expert and also presents you with a certificate to show for your weeks, months, or years of hard work.
We will begin this chapter with a high-level introduction to machine learning (ML), after which we will examine the different types of ML approaches. Next, we will drill down into the ML life cycle and use cases (we will cover a few hands-on implementations in subsequent chapters). We conclude this chapter by introducing the TensorFlow Developer Certificate and examining the anatomy of the core components needed to ace the exam. By the end of this chapter, you should be able to clearly explain what ML is and have gained a foundational understanding of the ML life cycle. Also, after this chapter, you will be able to differentiate between different types of ML approaches and clearly understand what the TensorFlow Developer Certificate exam is all about.
In this chapter, we will cover the following topics:
- What is ML?
- Types of ML algorithms
- The ML life cycle
- Exploring ML use cases
- Introducing the learning journey
