Course Description

This course is based on how students learn how to use machine learning. By addressing the problem, you can learn how machine learning methods are actually used, and at the same time, Allows you to develop programming skills. Pear for several libraries used primarily by Python Basic machine learning methods such as regression analysis, logistic analysis, discrimination analysis, model evaluation, linear model selection, The techniques required for machine learning modeling, including penalization models, can be learned by actually implementing them through Python. It also uses TensorFlow to implement and learn many deep learning techniques. Artificial Neural Network , CNN(Convolutional Neural Network), RNN (Recurrent Neural Network). Not only the basic deep learning techniques but also LSTM (Long Short-Term Memory), RBM (Restricted Advanced deeper such as B oltzmann Machine), AE (Auto Encoder), GAN (Generative Adversarial Network) Learn and learn until Ning technique

Prerequisites and Co-requisites

• Python Programming Basics
• Statistical Learning

Course Objectives and Learning Outcomes

This course enhances students’ ability to solve problems by applying programming and machine learning. The course also allows students to understand a variety of deep learning techniques and uses tensorflow to implement them. It increases the ability to do it.

Once students have taken this course, they will be able to:

• You will be able to program native file programs.
• You can use machine tools to implement machine learning techniques.
• Tensorflow will be available.
• You can use tensorflow to implement deep learning techniques.