Who can take this course:
Udacity offers two courses in machine learning, each aimed at different levels of experience:
- Intro to Machine Learning. This online course is ideal for beginners who are looking to build foundational knowledge in machine learning. Intermediate Python knowledge (~40 hours) together with basic knowledge in probability and statistics is recommended, but not required. Udacity also offers an “Intro to Programming” course which teaches all the recommended prerequisite material for this machine learning course.
- Machine Learning Engineer. This online course is considerably more advanced than the previous, and so are the requirements. Students who wish to take this course need to have proficiency not only in Python but also in machine learning algorithms. Our recommendation – start off with “Intro to Machine Learning”, and then continue with “Machine Learning Engineer”, as the two training programs complement each other perfectly.
What you’ll learn:
Udacity’s beginner-oriented course, “Intro to Machine Learning”, will cover:
- Using Python & SQL for data access & analysis from different data sources.
- Building predictive models using both unsupervised & supervised machine learning tools & techniques.
- Performing feature engineering to improve current machine learning models.
- Optimizing, tuning, and improving algorithms according to various metrics.
- Comparing the performances of learned models using various metrics.
As the curriculum shows, the course material here is still highly technical, despite it being a machine learning course aimed at beginners. Therefore, we suggest that you start off with developing the prerequisite skillsets mentioned earlier (Python, probability, statistics), before attempting this course.
Udacity’s advanced machine learning online course, “Machine Learning Engineer”, covers the following topics:
- Building and testing machine learning Python codes.
- Building predictive models using various unsupervised & supervised machine learning tools & techniques.
- Cloud deployment terminologies and best practices.
- Using Amazon SageMaker for deploying machine learning models.
- A/B testing different deployed models and evaluating their performance.
- Utilizing an API for deploying a model to a website in a way that it responds to user input, dynamically.
- Updating deployed models to respond to changes in the underlying data source.
In addition, the two courses will teach you how to create real-life projects in the field of machine learning. Being able to create machine learning projects independently is highly valuable. That is because independently developed projects can be shown to potential employers to demonstrate expertise. No matter the industry, job applicants who have a real portfolio of projects under their belt will always be chosen first, compared to those candidates with purely theoretical knowledge.
Additionally, Udacity’s machine learning courses will provide you with a clear overview of the existing career opportunities in the industry.
With support from industry professionals, highly in-depth course material, and actionable exercises, these two programs are worth any budding coder’s time and effort. Start off with the basics of machine learning with the Intro to Machine Learning certification program. Then, take your expertise one step further by learning how to create innovative machine learning programs and building your very own portfolio, by taking the Machine Learning Engineer certification program. Overall, these two are fantastic machine learning courses which work the best when taken consecutively. If you’re a beginner with some basic Python knowledge, then these two will be the best machine learning courses for you to take.