Artificial intelligence (AI) and machine learning (ML) have become an essential part of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, follow a methodical workflow to develop sound solutions, use open source, off-the-shelf.
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Description
1 - SOLVING BUSINESS PROBLEMS USING AI AND ML
Topic A: Identify AI and ML Solutions for Business Problems
Topic B: Follow a Machine Learning Workflow
Topic C: Formulate a Machine Learning Problem
Topic D: Select Appropriate Tools
2 - COLLECTING AND REFINING THE DATASET
Topic A: Collect the Dataset
Topic B: Analyze the Dataset to Gain Insights
Topic C: Use Visualizations to Analyze Data
Topic D: Prepare Data
3 - SETTING UP AND TRAINING A MODEL
Topic A: Set Up a Machine Learning Model
Topic B: Train the Model
4 - FINALIZING A MODEL
Topic A: Translate Results into Business Actions
Topic B: Incorporate a Model into a Long-Term Business Solution
5 - BUILDING LINEAR REGRESSION MODELS
Topic A: Build Regression Models Using Linear Algebra
Topic B: Build Regularized Regression Models Using Linear Algebra
Topic C: Build Iterative Linear Regression Models
6 - BUILDING CLASSIFICATION MODELS
Topic A: Train Binary Classification Models
Topic B: Train Multi-Class Classification Models
Topic C: Evaluate Classification Models
Topic D: 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 - PROMOTING DATA PRIVACY AND ETHICAL PRACTICES
Topic A: Protect Data Privacy
Topic B: Promote Ethical Practices
Topic C: Establish Data Privacy and Ethics Policies
Prerequisites
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-110) certification.
Audience
The skills covered in this course converge on three areas—software development, applied math and statistics, and business analysis. Target students for this course may be strong in one or two or these of these areas and looking to round out their skills in the other areas so they can apply artificial intelligence (AI) systems, particularly machine learning models, to business problems.