An Intensive 5-Day Training Course
Certified Artificial Intelligence Practitioner (CAIP)
Scheduled Dates
| 11 - 15 Dec 2023 | Dubai - UAE | $5,950 |
| 24 - 28 Jun 2024 | London - UK | $5,950 |
| 16 - 20 Dec 2024 | Dubai - UAE | $5,950 |
Why Choose this Training Course?
Artificial intelligence (AI) and machine learning (ML) have become essential parts of the tool set 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 PetroKnowledge Certified Artificial Intelligence Practitioner (CAIP) training 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.
What are the Goals?
At the end of this Certified Artificial Intelligence Practitioner(CAIP) training 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.
Who is this Training Course for?
The skills covered in this training course converge on four areas—software development, IT operations, applied math and statistics, and business analysis. Target participants 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 participant 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 participant in this course should have several years of experience with computing technology, including some aptitude in computer programming.
This PetroKnowledge Certified Artificial Intelligence Practitioner(CAIP)training course is also designed to assist participants in preparing for the CertNexus® Certified Artificial Intelligence (AI) Practitioner (Exam AIP-210) certification.
Daily Agenda
Day One: Solving Business Problems Using AI and ML
- Identify AI and ML solutions for business problems
- Formulate a machine learning problem
- Select approaches to machine learning
Preparing Data
- Collect data
- Transform data
- Engineer features
- Work with unstructured data
Day Two: Training, Evaluating, and Tuning a Machine Learning Model
- Train a machine learning model
- Evaluate and tune a machine learning model
Building Linear Regression Models
- Build regression models using linear algebra
- Build regularized linear regression models
- Build iterative linear regression models
Building Forecasting Models
- Build univariate time series models
- Build multivariate time series models
Day Three: Building Classification Models Using Logistic Regression and k-Nearest Neighbor
- 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
Building Clustering Models
- Build k-means clustering models
- Build hierarchical clustering models
Building Decision Trees and Random Forests
- Build decision tree models
- Build random forest models
Day Four: Building Support-Vector Machines
- Build SVM models for classification
- Build SVM models for regression
Building Artificial Neural Networks
- Build Multi-Layer Perceptrons (MLP)
- Build Convolutional Neural Networks (CNN)
- Build Recurrent Neural Networks (RNN)
Day Five: Operationalizing Machine Learning Models
- Deploy machine learning models
- Automate the machine learning process with MLOps
- Integrate models into machine learning systems
Maintaining Machine Learning Operations
- Secure machine learning pipelines
- Maintain models in production
Certificate
- On successful completion of this training course, a PetroKnowledge Certificate will be awarded to the delegates
In Association With
GLOMACS Training
Our collaboration with GLOMACS aims to provide the best training services and benefits for our valued clients
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Koukash Consultancy
Our collaboration with Koukash Consultancy aims to provide the best training services and benefits for our valued clients.
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