Description

The certification is aimed at machine learning developers who want to learn about computer vision, natural language, and deployment with standards referring to industry competency standards. At the end of the class, students can learn about the implementation of Machine Learning in industry starting from Computer Vision, Natural Language, and deployment of Machine Learning projects.

Material studied:

• Problem Framing: Learn how to formulate a machine learning solution to a problem. (35 minutes)
• Creating and Training Neural Network Models with Keras and TensorFlow : Learn to develop models with different data formats, create accuracy and loss plots of models, use of callbacks, TensorFlow datasets, and batch loading. (4 hours 35 minutes)
• Recommendation Systems: Get to know the techniques used in recommendation systems such as collaborative and content based filtering, as well as recommendation systems using neural networks. (45 minutes)
• Image Classification: Develop models for image classification and techniques commonly used in industry. (2 hours 35 minutes)
• Natural Language Processing (NLP) with TensorFlow: Learn to process words using TensorFlow. (8 hours 10 minutes)
• Time Series: Learn to develop models for predicting time series data. (7 hours 20 minutes)
• Reinforcement Learning: Technique for training models based on reward and punishment theory, using Keras, game playing agents, and the minimax algorithm. (45 minutes)
• Deployment: Explains how to deploy machine learning models to websites and Android applications using TensorFlow (8 hours 35 minutes)

Learning evaluation:

Submission (final project) consists of creating a machine learning model for image classification and saving the model in TF-Lite format.