This course takes you from a basic knowledge of machine learning and Python to an expert understanding of applying Keras to develop efficient deep learning solutions. This course teaches you new techniques to handle neural networks, and in turn, broadens your options as a data scientist.
Applied Deep Learning with Keras takes you from a basic level of knowledge of machine learning and Python to an expert understanding of applying Keras to develop efficient deep learning solutions. To understand the difference between machine and deep learning, you will build a logistic regression model once with scikit-learn and then with Keras. By building prediction models for several real-world scenarios, such as disease prediction and customer churning, you will dive deep into Keras and its many models. You will also gain knowledge about how to evaluate, optimize, and improve your models to gain maximum information. You will learn how to use Keras Wrapper with scikit-learn and implement cross-validation techniques on your findings, and apply L1, L2, and dropout regularization techniques to improve the accuracy of your model. Because accuracy itself might not be up to the mark, you will learn to apply the null accuracy, precision, sensitivity, specificity, AUC-ROC score techniques to fine tune your model. Then, you will dive deep and explore the convolutional and recurrent neural networks in detail
3 Days/Lecture & Lab
If you have basic knowledge about data science and machine learning and want to upgrade your skills to learn about artificial neural networks and deep learning, you can accomplish a lot with this course. Prior experience of programming in Python and familiarity with statistics and logistic regression will help you get the most out of this course. Though not necessary, it will be an added bonus if you are familiar with the scikit-learn library.