Machine Learning

 

ABOUT THE PROGRAM

Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.

COURSES

  • ML Track 1: ML Programmer

  • ML Track 2: DL Programmer

  • ML Track 3: ML Engineer

  • ML Track 4: ML Architect

  • AWS Certified Machine Learning

This is a self-paced program. Self-paced programs create a unique learning experience that allows students to learn independently and at a pace that best suits them.

CERTIFICATION

This program prepares the student to take the AWS Certified Machine Learning certification exam.

The cost of the certification exam is included in the tuition.

The certification exam is not a requirement for graduation. Vendor certifications are at the student’s expense. Vouchers may be available depending on the student’s funding and financial aid.

Tuition: $2,497

Duration: 121 Hours (includes 30 hours of virtual practice labs)

Includes e-books, virtual practice labs, bootcamp, mentoring, and exam review questions for the AWS Certified Machine Learning Exam.

Students have full online access to the program for 1 year.

Prerequisites: HS Diploma/GED, basic PC skills and familiarity with the Internet

Occupational Objectives: Machine Learning Engineer, Software Developer

To learn more about ETI’s tuition and financial aid options, click here.

COURSE Outline

ML Programmer

23 hours lecture + 8 hours virtual practice lab

  • Mentoring

  • NLP for ML with Python: NLP Using Python and NLTK

  • NLP for ML with Python: Advanced NLP Using spaCy and Scikit-learn

  • Linear Algebra and Probability: Fundamentals of Linear Algebra

  • Linear Algebra and Probability: Advanced Linear Algebra

  • Linear Regression Models: Introduction to Linear Regression

  • Linear Regression Models: Building Simple Regression Models with Scikit Learn and Keras

  • Linear Regression Models: Multiple and Parsimonious Linear Regression

  • Linear Regression Models: An Introduction to Logistic Regression

  • Linear Regression Models: Simplifying Regression and Classification with Estimators

  • Computational Theory: Language Principle & Finite Automata Theory

  • Computational Theory: Using Turing, Transducers, & Complexity Classes

  • Model Management: Building Machine Learning Models & Pipelines

  • Model Management: Building & Deploying Machine Learning Models in Production

  • Bayesian Methods: Bayesian Concepts and Core Components

  • Bayesian Methods: Implementing Bayesian Model and Computation with PyMC

  • Bayesian Methods: Advanced Bayesian Computation Model

  • Reinforcement Learning: Essentials

  • Reinforcement Learning: Tools & Frameworks

  • Math for Data Science & Machine Learning

  • Building ML Training Sets: Introduction

  • Building ML Training Sets: Preprocessing Datasets for Linear Regression

  • Building ML Training Sets: Preprocessing Datasets for Classification

  • Linear Model and Gradient Descent: Managing Linear Models

  • Linear Model and Gradient Descent: Gradient Descent and Regularization

  • Final Exam: ML Programmer

DL Programmer

22 hours lecture + 8 hours virtual practice lab

  • Mentoring

  • Getting Started with Neural Networks: Biological & Artificial Neural Networks

  • Getting Started with Neural Networks: Perceptrons & Neural Network Algorithms

  • Building Neural Networks: Development Principles

  • Building Neural Networks: Artificial Neural Networks Using Frameworks

  • Training Neural Networks: Implementing the Learning Process

  • Training Neural Networks: Advanced Learning Algorithms

  • Improving Neural Networks: Neural Network Performance Management

  • Improving Neural Networks: Loss Function and Optimization

  • Improving Neural Networks: Data Scaling and Regularization

  • ConvNets: Introduction to Convolutional Neural Networks

  • ConvNets: Working with Convolutional Neural Networks

  • Convolutional Neural Networks: Fundamentals

  • Convolutional Neural Networks: Implementing & Training

  • Convo Nets for Visual Recognition: Filters and Features Mapping in CNN

  • Convo Nets for Visual Recognition: Computer Vision and CNN Architectures

  • Fundamentals of Sequence Model: Artificial Neural Network & Sequence Modeling

  • Fundamentals of Sequence Model: Language Model & Modeling Algorithms

  • Build & Train RNNs: Neural Network Components

  • Build & Train RNNs: Implementing Recurrent Neural Networks

  • ML Algorithms: Multivariate Calculation & Algorithms

  • ML Algorithms: Machine Learning Implementation Using Calculus & Probability

  • Final Exam: DL Programmer

ML Engineer

17 hours lecture + 8 hours virtual practice lab

  • Mentoring

  • Predictive Modeling: Predictive Analytics and Exploratory Data Analysis

  • Predictive Modeling: Implementing Predictive Models Using Visualizations

  • Predictive Modeling Best Practices: Applying Predictive Analytics

  • Planning AI Implementation

  • Automation Design and Robotics

  • ML/DL in the Enterprise: Machine Learning Modeling, Development, & Deployment

  • ML/DL in the Enterprise: Machine Learning Infrastructure & Metamodel

  • Enterprises Services: Enterprise Machine Learning with AWS

  • Enterprises Services: Machine Learning Implementation on Microsoft Azure

  • Enterprises Services: Machine Learning Implementation on Google Cloud Platform

  • Architecting Balance: Designing Hybrid Cloud Solutions

  • Enterprise Architecture: Architectural Principles and Patterns

  • Enterprise Architecture: Design Architecture for Machine Learning Applications

  • Architecting Balance: Hybrid Cloud Implementation with AWS and Azure

  • Refactoring ML/DL Algorithms: Techniques and Principles

  • Refactoring ML/DL Algorithms: Refactor Machine Learning Algorithms

  • Final Exam: ML Engineer

ML Architect

13 hours lecture + 6 hours virtual practice lab

  • Mentoring

  • Applied Predictive Modeling

  • Implementing Deep Learning: Practical Deep Learning Using Frameworks and Tools

  • Implementing Deep Learning: Optimized Deep Learning Applications

  • Applied Deep Learning: Unsupervised Data

  • Applied Deep Learning: Generative Adversarial Networks and Q-Learning

  • Advanced Reinforcement Learning: Principles

  • Advanced Reinforcement Learning: Implementation

  • ML/DL Best Practices: Machine Learning and Workflow Best Practices

  • ML/DL Best Practices: Building Pipelines with Applied Rules

  • Research Topics in ML and DL

  • Deep Learning with Keras

  • Final Exam: ML Architect

AWS Certified Machine Learning

This course is an Amazon Web Services product that allows a developer to discover patterns in end-user data through algorithms, construct mathematical models based on these patterns and then create and implement predictive applications.

16 hours

  • Data Engineering, Machine Learning, & AWS

  • Amazon S3 Simple Storage Service

  • Data Movement

  • Data Pipelines & Workflows

  • Jupyter Notebook & Python

  • Data Analysis Fundamentals

  • Athena, QuickSight, & EMR

  • Feature Engineering Overview

  • Feature Engineering Techniques

  • Problem Framing & Algorithm Selection

  • Machine Learning & SageMaker

  • ML Algorithms in SageMaker

  • Advanced SageMaker Functionality

  • AI/ML Services

  • Problem Formulation & Data Collection

  • Data Preparation & SageMaker Security

  • Model Training & Evaluation

  • AI Services & SageMaker Applications