Certificate in AI Essentials

Program Highlights

Course Module : Certificate Programs
Duration : 72 hours
Format : Hybrid (Online/ In- person)
Center : All centers

Introduction

Throughout the course, you will explore the evolution and impact of AI, delve into the mechanics of machine learning and deep learning, and gain hands-on experience with natural language processing (NLP). You will also engage with ethical considerations in AI and examine future trends shaping the field. Through interactive lectures, practical labs, and real-world case studies, you will develop the skills to build and deploy AI models, preparing you to tackle complex problems with innovative AI solutions.

By the end of this course, you will have a comprehensive understanding of AI, equipping you with the knowledge and confidence to apply AI techniques in various professional settings and to continue your journey in this dynamic field.

Course learning outcome

Understand the basic concepts and history of AI.
Learn about various AI techniques, including machine learning, deep learning, and natural language processing.
Develop skills to implement simple AI models using popular programming languages and tools.
Explore real-world applications of AI across different industries.
Gain hands-on experience through projects and case studies.

Career Opportunities

AI/ML Research Assistant: Entry-level positions in research institutions or academic settings, assisting in the development of AI models and exploring new AI techniques.
Data Analyst: Leveraging AI tools to analyze large datasets, uncover patterns, and provide actionable insights for businesses.
AI Consultant: Advising companies on how to integrate AI into their operations, improving efficiency and decision-making processes.
Junior AI Developer: Working as part of a development team, focusing on building and deploying AI models for specific applications such as NLP, computer vision, or predictive analytics.
Business Intelligence Analyst: Utilizing AI to enhance business intelligence strategies, automate reporting, and develop predictive models to support business decisions.
AI Product Manager: Managing AI-driven products or services, ensuring they meet market needs and align with business objectives.
AI Trainer/Content Creator: Creating educational content or training programs on AI topics for a variety of audiences, from corporate training to online courses.
AI Sales Engineer: Helping to sell AI-based solutions by understanding client needs, demonstrating AI capabilities, and explaining technical aspects in a business context.
Technical Support Specialist (AI-focused): Providing support for AI-based software and applications, troubleshooting issues, and helping users maximize the benefits of AI tools.
Ethics Consultant in AI: Specializing in the ethical implications of AI, advising companies on responsible AI practices and ensuring compliance with regulations.

Course Content

Module 1

Introduction to Artificial intelligence

Artificial intelligence
Applications of Artificial Intelligence
Industry domains and sectors along with sample use cases
Machine learning Deep Learning
Difference between machine learning and deep learning Applications of deep learning

Tools and frameworks for AI, ML and DL
Languages used for AI, ML, and DL

Sample datasets for AI, ML, and DL development

Introduction to Generative AI

Overview of Generative AI
Applications of Generative AI

Content creation,Image and video generation,Music and audio synthesis andData augmentation

function of generator and discriminator networks

Implementing a simple GAN using TensorFlow/PyTorch
Training the GAN on image datasets

Module 2

Introduction to Python

Introduction to Python programming
Basic syntax, data types, and variables
Conditionals, loops, and functions in Python
Introduction to Python libraries (NumPy, Pandas)

Data Preprocessing

Understanding data preprocessing steps.
Handling missing values, data normalization.
Using Pandas for data manipulation.
Implementing data preprocessing techniques.

Module 3

Machine Learning

Introduction to popular ML algorithms.
Linear regression, decision trees.
Implementing a basic ML model in Python.

Splitting data into training and testing sets.
Training a linear regression model.
Evaluating model performance.

Deep Learning

Basics of neural networks and deep learning.
Understanding layers, neurons, and activation functions.
Building simple neural network.

Using TensorFlow/Keras.
Creating and training a neural network.

Introduction to deep learning frameworks.

Overview of TensorFlow and Keras.
Understanding model training and optimization.

Module 4

Natural Language Processing (NLP)

Understanding NLP and its applications.
Introduction to NLP tasks (sentiment analysis, text classification).
Tokenization, stemming, and lemmatization.
Using NLTK for text preprocessing.

Eligibility Requirements

For learners who are typically aged 18 and above.
For learners who have recently been in education or training for the entry, the profile is likely to include one of th e following.
WASSCE: Passes in any three Core Subjects and three elective subjects including (Mathematics, English Language) or its equivalent (SSSCE, O-Level, A-Level, etc.) other related level 3 subjects such as ATHE Level 3 Diploma
An Access to Higher Education Certificate delivered by an approved further education institute and validated by an Access Validating Agency
Other equivalent international qualifications