Artificial Intelligence / Machine Learning (AI/ML) Training, Uyo, Akwa Ibom State and Port Harcourt, Rivers State

Artificial Intelligence / Machine Learning Training, Uyo, Akwa Ibom State and Port Harcourt, Rivers State

I. Introduction to Artificial Intelligence (AI)

Learning Objectives:

Define Artificial Intelligence and its core principles.
Identify the various subfields of AI (e.g., Machine Learning, Natural Language Processing, Computer Vision).
Understand the Turing Test and its limitations.
Applications of AI: Explore real-world applications of AI across different industries (e.g., healthcare, finance, self-driving cars, etc.).

Learning Objectives:

Recognize the impact of AI on various aspects of society.
Analyze case studies showcasing successful AI implementations.
The Ethics of AI: Discuss the ethical considerations surrounding AI development and deployment, including bias, fairness, and transparency.

Learning Objectives:

Identify potential biases present in AI systems.
Explain the importance of fairness and transparency in AI.
Explore ethical frameworks for responsible AI development.
II. Introduction to Machine Learning (ML)

Fundamentals of Machine Learning: Understand the core concepts of Machine Learning, including supervised vs. unsupervised learning, algorithms, data, and evaluation metrics.

Learning Objectives:

Differentiate between supervised, unsupervised, and reinforcement learning.
Explain the role of data in Machine Learning.
Identify common evaluation metrics used for ML models.
Machine Learning Workflow: Gain a practical understanding of the Machine Learning workflow, encompassing data collection, pre-processing, model training, evaluation, and deployment.

Learning Objectives:

Describe the steps involved in the Machine Learning lifecycle.
Explain the importance of data pre-processing techniques.
Understand the model training and evaluation process.
Hands-on Session 1: Introduction to Python Programming (optional): For beginners, a basic introduction to Python programming can be included to equip them with a foundation for further learning in popular ML libraries like Scikit-learn and TensorFlow.

III. Supervised Machine Learning Algorithms

Linear Regression: Learn about linear regression, a fundamental supervised learning algorithm for continuous target variables.

Learning Objectives:

Explain the concept of linear regression and its applications.
Interpret the results of a linear regression model.
Classification Algorithms: Explore common classification algorithms like Logistic Regression, Decision Trees, Support Vector Machines (SVMs), and K-Nearest Neighbors (KNN) used for predicting discrete outcomes.

Learning Objectives:

Distinguish between different classification algorithms and their strengths/weaknesses.
Apply classification algorithms to solve practical problems.
Hands-on Session 2: Supervised Learning in Python (using libraries like Scikit-learn): Participants will gain practical experience implementing supervised learning algorithms using Python libraries.

IV. Unsupervised Machine Learning Algorithms

Clustering Algorithms: Introduce unsupervised learning techniques like K-Means Clustering and Hierarchical Clustering for grouping data points.

Learning Objectives:

Explain the concept of clustering and its applications.
Implement K-Means Clustering and interpret the results.
Dimensionality Reduction Techniques: Explore dimensionality reduction techniques like Principal Component Analysis (PCA) to reduce the number of features in a dataset while retaining important information.

Learning Objectives:

Understand the benefits of dimensionality reduction.
Apply PCA for feature selection and model improvement.
Hands-on Session 3: Unsupervised Learning in Python (using libraries like Scikit-learn): Participants will practice applying unsupervised learning algorithms in Python.

V. Deep Learning and Artificial Neural Networks (ANNs)

Introduction to Deep Learning: Provide an overview of Deep Learning, a subfield of Machine Learning using Artificial Neural Networks (ANNs) for complex pattern recognition.

Learning Objectives:

Explain the basic structure and function of Artificial Neural Networks.
Understand the concept of deep learning architectures.
Deep Learning Architectures: Explore popular Deep Learning architectures like Convolutional Neural Networks (CNNs) for image recognition and Recurrent Neural Networks (RNNs) for sequence data.

Learning Objectives:

Identify different Deep Learning architectures and their applications.
Explain the working principles of CNNs and RNNs.
Hands-on Session 4 : Deep Learning Frameworks (using libraries like TensorFlow or Keras):