Certified Artificial Intelligence Practitioner (Exam AIP-210)

Upon completion of Certified Artificial Intelligence (AI) Practitioner (Exam AIP-210) course, students will be able to:

• a given business problem using AI and ML.
• data for use in machine learning.
• evaluate, and tune a machine learning model.
• linear regression models.
• forecasting models.
• classification models using logistic regression and k -nearest neighbor.
• clustering models.
• classification and regression models using decision trees and random forests.
• classification and regression models using support-vector machines (SVMs).
• artificial neural networks for deep learning.
• machine learning models into operation using automated processes.
• machine learning pipelines and models while they are in production

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.
 

To ensure your success in this course, you should be familiar with the concepts that are foundational to data science, including:
• overall data science and machine learning process from end to end: formulating the problem;
collecting and preparing data; analyzing data; engineering and preprocessing data; training, tuning, and evaluating a model; and finalizing a model.
• concepts such as sampling, hypothesis testing, probability distribution, randomness, etc.
• statistics such as mean, median, mode, interquartile range (IQR), standard deviation,
skewness, etc.
• plots, charts, and other methods of visual data analysis.
You can obtain this level of skills and knowledge by taking the CertNexus course Certified Data Science Practitioner (CDSP) (Exam DSP-110). You must also be comfortable writing code in the Python programming language, including the use of fundamental Python data science libraries like NumPy and pandas. The Logical Operations course Using Data Science Tools in Python® teaches these skills.To ensure their success in this course, students 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. They can obtain this level of knowledge by taking the CertNexus AIBIZ™ (Exam AIZ-110) course. They should also have experience working with databases and high-level programming languages such as Python, Java, or C/C++. They can obtain this level of skills and knowledge by taking the following courses:
• Database Design: A Modern Approach
• Python® Programming: Introduction
• Python® Programming: Advanced

Lesson 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

Lesson 2: Preparing Data

• Topic A: Collect Data
• Topic B: Transform Data
• Topic C: Engineer Features
• Topic D: Work with Unstructured Data

Lesson 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

Lesson 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

Lesson 5: Building Forecasting Models

• Topic A: Build Univariate Time Series Models
• Topic B: Build Multivariate Time Series Models

Lesson 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

Lesson 7: Building Clustering Models

• Topic A: Build k-Means Clustering Models
• Topic B: Build Hierarchical Clustering Models

Lesson 8: Building Decision Trees and Random Forests

• 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)

Topic C: Build Recurrent Neural Networks (RNN) Lesson 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

Lesson 12: Maintaining Machine Learning Operations

• Topic A: Secure Machine Learning Pipelines
• Topic B: Maintain Models in Production

Appendix A: Mapping Course Content to CertNexus® Certified Artificial Intelligence (AI) Practitioner (Exam AIP-210) Appendix B: Datasets Used in This Course