AI Technologies in Action: Power Your Business with Next-Gen Solutions

(CAIP™)

All our courses are 100% teacher-led by our authorized instructors and include certification exams.
Getting certified, you can also have the possibility to join Be Licensed’s recruitment portal.

Course Overview

Unlock your potential with our AI professional course, designed to equip you with the skills to tackle business challenges through advanced AI and machine learning techniques. You'll learn to automate processes, reduce costs, and streamline operations while freeing your team to focus on strategic initiatives. Gain expertise in dataset engineering, algorithm selection, and model training, all while ensuring ethical AI outcomes. With this course, you'll lead AI-driven innovation, enhancing customer experiences and driving your organization toward success. Elevate your career and be at the forefront of AI transformation.

Course outcome

By completing this course, individuals will be equipped with the knowledge to solve complex business challenges using AI and machine learning, from analyzing use cases to communicating solutions with stakeholders. They will gain proficiency in data engineering, transforming various data formats, and ensuring data quality for effective machine learning workflows. Additionally, participants will learn how to design, optimize, and deploy machine learning models, addressing ethical considerations throughout. This comprehensive preparation enables learners to confidently take the associated exam and succeed in real-world AI applications.

Your new skills

Problem-solving using artificial intelligence and machine learning in business contexts.
Analysis of machine learning algorithm success probabilities.
Identifying appropriate use cases for AI technologies (e.g., image recognition, NLP, speech recognition).
Communicating AI and ML concepts to stakeholders.
Recognizing and addressing potential ethical concerns in AI applications.
Understanding the impact of data quality and size on machine learning algorithms.
Data collection and transformation for machine learning workflows.
Working with different data formats (textual, numerical, audio, video).
Transforming numerical and categorical data.
Designing machine learning and deep learning models.
Optimizing machine learning algorithms, including tuning hyperparameters.
Training, validating, and testing data subsets for model building.
Deploying machine learning models in operational environments.
Securing and maintaining machine learning pipelines.
Post-production maintenance of deployed machine learning models.

Prerequisites:

Knowledge:

To succeed in the course, the following background knowledge is recommended:

Applied Mathematics and Math Theory, and Statistical Modeling Procedures: linear algebra, sampling, hypothesis testing, randomness, multivariate calculus, random forest, XGBoost, probability distributions like Poisson, normal, binomial, etc.
Data Science and Machine Learning Processes: formulating the problem; collecting and preparing data; analyzing data; engineering and preprocessing data; training, tuning, and evaluating a model; and finalizing a model.
Programming Abilities for ML, Statistics, and Querying: Python® and R, and SQL.
Data Visualisation Skills: Graphs, plots, charts, and other methods of visual data analysis.

Hardware: A computer (PC running Windows 10 is preferred, but Mac can be used) capable of hosting a Virtualbox Linux VM, with the following minimum specifications:

2GHz 64-bit (x64) processor that supports the VT-x and AMD-V virtualization instruction set and Second Level Address Translation (SLAT)
8 GB RAM
20 GB storage space free

Software: Anaconda and Jupyter Notebook run in the Linux VM.

Access: Internet access - a stable fiber connection of at least 10 Mbps up and down speeds is recommended.

What is included in the price?

Fully led by an authorized instructor.

All materials needed to complete the course are included in the course fee. This includes course literature, exercise assignments and fees for the certification exam.

The course is held remotely in a virtual classroom. All lectures are recorded and are available as reference material for two weeks after course completion.