This course is designed for professionals who want to develop practical Artificial Intelligence (AI) and Machine Learning (ML) solutions to address real business challenges. It is ideal for individuals with a background in data science, software development, business analysis, or IT who are looking to strengthen their machine learning skills and apply advanced analytics to support data-driven decision-making.

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

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.