Machine Learning and AI with AWS

Data engineering is a crucial component of modern data-driven organizations, responsible for transforming raw data into valuable insights. In this course, you will explore the core principles of data engineering, learn how to work with popular databases like PostgreSQL and MongoDB, and leverage Talend's powerful ETL capabilities to streamline your data processing workflows. 

Machine Learning and Artificial Intelligence with AWS SageMaker and Deep Learning: Unlock the Power of AI

Learning Outcomes
Upon successful completion of this course, you will be able to :

Machine Learning Algorithms

Master various machine learning algorithms, including supervised, unsupervised, and reinforcement learning techniques, to solve real-world problems effectively.

Data Preprocessing and Feature Engineering

Learn to preprocess data and engineer features to improve model performance and accuracy while working with complex datasets.

Deep Learning and Neural Networks

 Gain expertise in deep learning concepts and architectures, including artificial neural networks, convolutional neural networks, and recurrent neural networks, to solve advanced AI challenges.

 AWS SageMaker Integration

Develop proficiency in utilizing AWS SageMaker for model training, deployment, and management, enabling seamless integration of machine learning workflows with cloud infrastructure

Course Modules

Introduction to Machine Learning
  • Understanding machine learning and AI
  • Overview of the machine learning and AI landscape
  • Popular tools, libraries, and frameworks for ML and AI
Supervised Learning Techniques
  • Introduction to supervised learning
  • Regression, classification, and ensemble methods
  • Model evaluation and validation
Unsupervised Learning Techniques
  • Introduction to unsupervised learning
  • Clustering, dimensionality reduction, and anomaly detection
  • Model evaluation and validation
Reinforcement Learning Techniques
  • Introduction to reinforcement learning
  • Markov decision processes and Q-learning
  • Deep reinforcement learning
Deep Learning and Neural Networks
  • Introduction to deep learning and neural networks
  • Convolutional neural networks (CNNs)
  • Recurrent neural networks (RNNs) and Long Short-Term Memory (LSTM)
  • Generative models: Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs)
AWS SageMaker for Machine Learning and AI
  • Introduction to AWS SageMaker
  • Building, training, and deploying models using SageMaker
  • Hyperparameter tuning and model monitoring with SageMaker
Capstone Project
  • Apply the skills learned throughout the course to a real-world machine learning and AI problem