Course Overview
Artificial intelligence (AI) and machine learning (ML) have become essential parts 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, all while following a methodical workflow for developing data-driven solutions.
Enrollment in this course includes student digital courseware, labs, and exam voucher for Certified Artificial Intelligence Practitioner (Exam AIP-210).
What you’ll learn
Upon completion of Certified Artificial Intelligence (AI) Practitioner (Exam AIP-210) course, students will be able to:
- Solve a given business problem using AI and ML.
- Prepare data for use in machine learning.
- Train, evaluate, and tune a machine learning model.
- Build linear regression models.
- Build forecasting models.
- Build classification models using logistic regression and k -nearest neighbor.
- Build clustering models.
- Build classification and regression models using decision trees and random forests.
- Build classification and regression models using support-vector machines (SVMs).
- Build artificial neural networks for deep learning.
- Put machine learning models into operation using automated processes.
- Maintain machine learning pipelines and models while they are in production
Requirements
- To ensure your success in this course, you should be familiar with the concepts that are foundational to data science, including:
- The 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.
- Statistical concepts such as sampling, hypothesis testing, probability distribution, randomness, etc.
- Summary statistics such as mean, median, mode, interquartile range (IQR), standard deviation, skewness, etc.
- Graphs, 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.
Target audiences
- 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.
Curriculum
- 1 Section
- 12 Lessons
- 5 Days
Expand all sectionsCollapse all sections
- Topics12
- 1.1Lesson 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
- 1.2Lesson 2: Preparing Data Topic A: Collect Data Topic B: Transform Data Topic C: Engineer Features Topic D: Work with Unstructured Data
- 1.3Lesson 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
- 1.4Lesson 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
- 1.5Lesson 5: Building Forecasting Models Topic A: Build Univariate Time Series Models Topic B: Build Multivariate Time Series Models
- 1.6Lesson 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
- 1.7Lesson 7: Building Clustering Models Topic A: Build k-Means Clustering Models Topic B: Build Hierarchical Clustering Models
- 1.8Lesson 8: Building Decision Trees and Random Forests Topic A: Build Decision Tree Models Topic B: Build Random Forest Models
- 1.9Lesson 9: Building Support-Vector Machines Topic A: Build SVM Models for Classification Topic B: Build SVM Models for Regression
- 1.10Lesson 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)
- 1.11Lesson 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
- 1.12Lesson 12: Maintaining Machine Learning Operations Topic A: Secure Machine Learning Pipelines Topic B: Maintain Models in Production
Instructor
