Course Description
Bootcamp | 30 hours | 3.0 CEUs | $3,795
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.
Course Outline
- Lesson 1: Solving Business Problems Using AI and ML
- Topic A: Identify AI and ML Solutions for Business Problems
- Topic C: Formulate a Machine Learning Problem
- Topic D: Select Appropriate Tools
- Lesson 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
- Lesson 3: Setting Up and Training a Model
- Topic A: Set Up a Machine Learning Model
- Topic B: Train the Model
- Lesson 4: Finalizing a Model
- Topic A: Translate Results into Business Actions
- Topic B: Incorporate a Model into a Long-Term Business Solution
- Lesson 5: Building Linear Regression Models
- 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
- Lesson 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
- Lesson 7: Building Clustering Models
- Topic A: Build k-Means Clustering Models
- Topic B: Build Hierarchical Clustering Models
- Lesson 8: Building Advanced Models
- Topic A: Build Decision Tree Models
- Topic B: Build Random Forest Models
- Lesson 9: Building Support-Vector Machines
- Topic A: Build SVM Models for Classification
- Topic B: Build SVM Models for Regression
- Lesson 10: Building Artificial Neural Networks
- Topic A: Build Multi-Layer Perceptrons (MLP)
- Topic B: Build Convolutional Neural Networks (CNN)
- Lesson 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
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 can obtain this level of knowledge by taking the CertNexus AIBIZ™ (Exam AIZ-110) course.
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
Duration
30 hours | 5 Days or 10 Nights*Academic Unit eligibility to be determined by college/university in which you are enrolled in a degree seeking program.