Congratulations on completing the path. Let's get an overview of what you have learned and get the know-how of job that you can get with the new skill you have acquired.
A milestone
You have acquired an understanding of machine learning from basic to advanced. This is a big achievement. It is certainly not the end goal but it is a milestone in the right direction.
When you start looking for a job as a machine learning engineer, your understanding of supervised learning, unsupervised learning, image recognition using CNNs, natural language processing, will help make you a more suitable candidate. You have reached a milestone! Give yourself a round of applause šš».
A quick recap!
This path has provided a complete guide to machine learning. Youāll be able to take the practical lessons and actionable insights from this course and apply them to your projects.
Topics covered
- Basics of machine learning
- Image recognition using machine learning
- Natural language processing
- Application of machine learning in industry using case study
- Machine learning interview preparation.
Whatās next?
People often throw around the terms āmachine learningā, āartificial intelligenceā, and ādata scienceā interchangeably. In reality, machine learning is a subset of artificial intelligence and overlaps heavily with data science. Artificial intelligence deals with any technique that allows machines to display āintelligenceā, similar to humans. Machine learning is one of the main techniques used to create artificial intelligence, but other non-ML techniques (e.g. alpha-beta pruning, rule-based systems) are also widely used in AI.
On the other hand, data science deals with gathering insights from datasets. Traditionally, data scientists have used statistical methods for gathering these insights. However, as machine learning continues to grow, it has also penetrated into the field of data science.
In industry, any data scientist or AI researcher needs to have a good understanding of machine learning. You can explore the datascience path, solve an end-to-end ML project and learn how an ML project is deployed in production by getting hands-on-practice on famous bench mark datasets. Start your journey with data science by taking the Become a Data Scientist with Python path.