Course Outline
1 - 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
2 - Preparing Data
- Topic A: Collect Data
- Topic B: Transform Data
- Topic C: Engineer Features
- Topic D: Work with Unstructured Data
3 - Training, Evaluating, and Tuning a Machine Learning Model
- Topic A: Train a Machine Learning Model
- Topic B: Evaluate and Tune a Machine Learning Model
4 - Building Linear Regression Models
- Topic A: Build Regression Models Using Linear Algebra
- Topic B: Build Regularized Linear Regression Models
- Topic C: Build Iterative Linear Regression Models
5 - Building Forecasting Models
- Topic A: Build Univariate Time Series Models
- Topic B: Build Multivariate Time Series Models
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
7 - Building Clustering Models
- Topic A: Build k-Means Clustering Models
- Topic B: Build Hierarchical Clustering Models
8 - Building Decision Trees and Random Forests
- Topic A: Build Decision Tree Models
- Topic B: Build Random Forest Models
9 - Building Support-Vector Machines
- Topic A: Build SVM Models for Classification
- Topic B: Build SVM Models for Regression
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)
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
12 - Maintaining Machine Learning Operations
- Topic A: Secure Machine Learning Pipelines
- Topic B: Maintain Models in Production
Target Audience
The skills covered in this course converge on four areas—software development, IT operations, applied math and statistics, and business analysis. Target students for this course should be looking to build upon their knowledge of the data science process so that they can apply AI systems, particularly machine learning models, to business problems. So, the target student is likely a data science practitioner, software developer, or business analyst looking to expand their knowledge of machine learning algorithms and how they can help create intelligent decision-making products that bring value to the business. A typical student in this course should have several years of experience with computing technology, including some aptitude in computer programming. This course is also designed to assist students in preparing for the CertNexus® Certified Artificial Intelligence (AI) Practitioner (Exam AIP-210) certification.