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 tools to develop, test, and deploy those solutions, and ensure that they protect the privacy of users. This course includes hands-on activities for each topic area.
In this course, you will implement AI techniques in order to solve business problems. You will:
• Specify a general approach to solve a given business problem that uses applied AI and ML.
• Collect and refine a dataset to prepare it for training and testing.
• Train and tune a machine learning model.
• Finalize a machine learning model and present the results to the appropriate audience.
• Build linear regression models.
• Build classification models.
• Build clustering models.
• Build decision trees and random forests.
• Build support-vector machines (SVMs).
• Build artificial neural networks (ANNs).
• Promote data privacy and ethical practices within AI and ML projects.
The skills covered in this course converge on three areas—
- software development,
- applied math and statistics,
- and business analysis.
To ensure your success in this course, you should have at least a high-level understanding of fundamental AI concepts, including, but not limited to: machine learning, supervised learning, unsupervised learning, artificial neural networks, computer vision, and natural language processing. You should also have experience working with databases and a high-level programming language such as Python, Java, or C/C++. You can obtain this level of skills and knowledge by taking the following Logical Operations or comparable course: • Database Design: A Modern Approach • Python® Programming: Introduction • Python® Programming: Advanced.
Topic A: Identify AI and ML Solutions for Business Problems
Topic B: Formulate a Machine Learning Problem
Topic C: Select Appropriate Tools
Topic A: Collect the Dataset
Topic B: Analyze the Dataset to Gain Insights
Topic C: Use Visualizations to Analyze Data
Topic D: Prepare Data
Topic A: Set Up a Machine Learning Model
Topic B: Train the Model
Topic A: Translate Results into Business Actions
Topic B: Incorporate a Model into a Long-Term Business Solution
Topic A: Build a Regression Model Using Linear Algebra
Topic B: Build a Regularized Regression Model Using Linear Algebra
Topic C: Build an Iterative Linear Regression Model
Topic A: Train Binary Classification Models
Topic B: Train Multi-Class Classification Models
Topic C: Evaluate Classification Models
Topic D: Tune Classification Models
Topic A: Build k-Means Clustering Models
Topic B: Build Hierarchical Clustering Models
Topic A: Build Decision Tree Models
Topic B: Build Random Forest Models
Topic A: Build SVM Models for Classification
Topic B: Build SVM Models for Regression
Topic A: Build Multi-Layer Perceptrons (MLP)
Topic B: Build Convolutional Neural Networks (CNN)
Topic A: Protect Data Privacy
Topic B: Promote Ethical Practices
Topic C: Establish Data Privacy and Ethics Policies Appendix A: Mapping Course