Practical AI and ML
Sub Category
- Data Science
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Objectives
- Build solid knowledge necessary for data scientists about AI, Machine Learning and Deep Learning
- Understand the basics and underlying dynamics of supervised learining models: LinearRegression, LogisiticRegression, SVM, DNN, DecisionTrees and RandomForests.
- Get introduced to unsupervised learning approaches for dimensionality reduction and clustering.
- Build practical Machine Learning models and pipelines using python, scikit-learn, pandas, keras and tensorflow
- Solve practical problems like image classification, text classification, price prediction.
Pre Requisites
- Python
- Linear Algebra
- Probability and Statistics
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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Coupon Code(s)