Artificial Intelligence (AI) and Machine Learning (ML) have become essential tools that many organizations want in their arsenal. They provide actionable insights that drive critical decisions and enable organizations to create exciting and innovative products and services.
On this course, which is designed to assist you in preparing for the CertNexus® Certified Artificial Intelligence (AI) Practitioner (Exam AIP-210) certification, you’ll learn how to apply various approaches and algorithms to solve business problems through AI and ML.
You’ll gain skills in four key areas; software development, IT operations, applied math and statistics, and business analysis – all while following a methodical workflow for developing data-driven solutions.
This Certified AI Practitioner course is available as a private training session that can be delivered via Virtual Classroom or at a location of your choice in Australia.
Who should attend:
This course is for you if you’re looking to build upon your existing knowledge of the data science process so that you can apply AI systems – particularly ML models – to business problems. It’s ideal for science practitioners, software developers, or business analysts looking to expand their knowledge of ML algorithms and how they can create intelligent decision-making products.
What you'll learn:
By the end of this course, you will be able to:
- Solve a given business problem using AI and ML
- Prepare data for use in ML
- Train, evaluate and tune a ML 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 support-vector machines (SVMs)
- Build artificial neural networks for deep learning
- Put machine learning models into operation using automated processes
- Maintain ML pipelines and models while they are in production
To get the most out of this course, you should be familiar with the overall data science and machine learning process from end to end; understanding how to formulate the problem; collecting and preparing data; analyzing data; engineering and preprocessing data; training, tuning and evaluating a model; and finalizing a model.
You should also be familiar with statistical concepts such as sampling, hypothesis testing, probability distribution and randomness; and summary statistics such as mean, median, mode, interquartile range (IQR), standard deviation, skewness, etc., as well as methods of visual data analysis.
Finally, you should be able to write code in Python, as well as have experience using libraries such as NumPy and pandas.
- Identify AI and ML solutions for business problems
- Formulate a machine learning problem
- Select approaches to ML
- Collect data
- Transform data
- Engineer features
- Work with unstructured data
- Train a machine learning model
- Elevate and tune a machine learning model
- Build regression models using linear algebra
- Build regularized linear regression models
- Build iterative linear regression models
- Build univariate time series models
- Build multivariate time series models
- Train binary classification models using logistic regression
- Train binary classification models using k-Nearest neighbor
- Train multi-class classification models
- Evaluate classification models
- Tune classification models
- Build k-Means clustering models
- Build hierarchical clustering models
- Build decision tree models
- Build random forest models
- Build SVM models for classification
- Build SVM models for regression
- Build multi-layer perceptrons (MLP)
- Build convolutional neural networks (CNN)
- Build recurrent neural networks (RNN)
- Deploy machine learning models
- Automate the machine learning process with MLOps
- Integrate models in to machine learning system
- Secure machine learning pipelines
- Maintain models in production