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CIS439 - Deep Learning and Neural Networks

(STY-CIS439.AK1)
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1

Introduction

  • Course Description
  • How To Use This Course
  • Course-Specific Technical Requirements
2

Solving Business Problems Using AI and ML

  • TOPIC A: Identify AI and ML Solutions for Business Problems
  • TOPIC B: Formulate a Machine Learning Problem
  • TOPIC C: Select Approaches to Machine Learning
  • Summary
3

Preparing Data

  • TOPIC A: Collect Data
  • TOPIC B: Transform Data
  • TOPIC C: Engineer Features
  • TOPIC D: Work with Unstructured Data
  • Summary
4

Training, Evaluating, and Tuning a Machine Learning Model

  • TOPIC A: Train a Machine Learning Model
  • TOPIC B: Evaluate and Tune a Machine Learning Model
  • Summary
5

Building Forecasting Models

  • TOPIC A: Build Univariate Time Series Models
  • TOPIC B: Build Multivariate Time Series Models
  • Summary
6

Building Classification Models Using Logistic Regression and k-Nearest Neighbor

  • TOPIC A: Train Binary Classification Models Using Logistic Regression
  • TOPIC B: Train Binary Classification Models Using k- Nearest Neighbor
  • TOPIC C: Train Multi-Class Classification Models
  • TOPIC D: Evaluate Classification Models
  • TOPIC E: Tune Classification Models
  • Summary
7

Building Clustering Models

  • TOPIC A: Build k-Means Clustering Models
  • TOPIC B: Build Hierarchical Clustering Models
  • Summary
8

Building Decision Trees and Random Forests

  • TOPIC A: Build Decision Tree Models
  • TOPIC B: Build Random Forest Models
  • Summary
9

Building Support-Vector Machines

  • TOPIC A: Build SVM Models for Classification
  • TOPIC B: Build SVM Models for Regression
  • Summary
10

Building Artificial Neural Networks

  • TOPIC A: Build Multi-Layer Perceptrons (MLP)
  • TOPIC B: Build Convolutional Neural Networks (CNN)
  • TOPIC C: Build Recurrent Neural Networks (RNN)
  • Summary
11

Operationalizing Machine Learning Models

  • TOPIC A: Deploy Machine Learning Models
  • TOPIC B: Automate the Machine Learning Process with MLOps
  • TOPIC C: Integrate Models into Machine Learning Systems
  • Summary

1

Preparing Data

  • Loading and Exploring the Dataset
  • Working with Text Data
2

Training, Evaluating, and Tuning a Machine Learning Model

  • Training a Machine Learning Model
3

Building Forecasting Models

  • Building a Univariate Time Series Model
  • Building a Multivariate Time Series Model
4

Building Classification Models Using Logistic Regression and k-Nearest Neighbor

  • Training a Binary Classification Model Using Logistic Regression
  • Training a Binary Classification Model Using k-NN
5

Building Clustering Models

  • Building a Hierarchical Clustering Model
6

Building Decision Trees and Random Forests

  • Building a Decision Tree Model and a Random Forest
7

Building Support-Vector Machines

  • Building an SVM Model for Classification
8

Building Artificial Neural Networks

  • Building a CNN
  • Building an RNN

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