Machine learning and AI

Overview:

This course focuses on the practical aspects of Machine Learning, Deep Learning and Artificial Intelligence. The objective is to make use of TensorFlow for various types of neural networks. The participants will build and train deep learning models.

Audience Profile:

• The intended audience for this course.
• Data Engineers.
• Data Scientists.
• Machine Learning Engineers.
• Integration Engineers.
• Architects.

Course Outline:

• Understanding the Big Picture.
• GCP Introduction.
• Environment for Experiments.
• Introduction to Random Forests
• Random Forest Deep Dive.
• Feature Importance, Tree Interpreter
• Getting holistic view: Architectures and Pipelines.
• Machine Learning APIs.
• Performance, Validation and Model Interpretation.
• Key Statistics.
• Extrapolation and RF from Scratch.
• Data Visualization.
• Acquiring & Preparing Data.
• Data Products and Live Coding.
• RF from Scratch and Gradient Descent.
• Regularization, Learning Rates and NLP.
• More NLP and Columnar Data.
• Embeddings.
• Complete Rossmann, Ethical Issues.
• Gradient Descent and Logistic Regression.
• Feature Engineering.
• Machine Learning using Scikit Learn.
• Supervised Machine Learning.
• Un-Supervised Machine Learning.
• Spark Introduction.
• Spark SQL.
• Spark Machine Learning.
• Introduction to Deep Learning.
• Artificial Neural Network.
• TensorFlow API.
• Convolutional Neural Networks (CNN).
• Interactive Data exploration using DataLab.
• CloudML: Scalable Models on GCP.
• Conversational AI.