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.

starstarstarstarstar_outline

* Actual course outline may vary depending on offering center. Contact your sales representative for more information.

Learning Objectives

In this course, you will develop AI solutions for business problems.
You will:
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

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

2

  • PREPARING DATA

  • Topic A: Collect Data

    Topic B: Transform Data

    Topic C: Engineer Features

    Topic D: Work with Unstructured Data

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

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

5

  • BUILDING FORECASTING MODELS

  • Topic A: Build Univariate Time Series Models

    Topic B: Build Multivariate Time Series Models

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

7

  • BUILDING CLUSTERING MODELS

  • Topic A: Build k-Means Clustering Models

    Topic B: Build Hierarchical Clustering Models

8

  • BUILDING DECISION TREES AND RANDOM FORESTS

  • Topic A: Build Decision Tree Models

    Topic B: Build Random Forest Models

9

  • BUILDING SUPPORT-VECTOR MACHINES

  • Topic A: Build SVM Models for Classification

    Topic B: Build SVM Models for Regression

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)

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

12

  • MAINTAINING MACHINE LEARNING OPERATIONS

  • Topic A: Secure Machine Learning Pipelines

    Topic B: Maintain Models in Production

Audience

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 decisionmaking 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

Language

English

Prerequisites

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

Course Info

Not Your Location? Change

Cost: $ 3,475

Length: 5.0 days (40 hours)

Level:

For scheduling options, please chat with an expert below.

Loading...