Machine Learning & Artificial Intelligence Course Overview
This course is an introduction to the fundamentals of machine learning and artificial intelligence (AI). Students will learn how to apply various algorithms to real-world data sets and gain an understanding of how these algorithms work under the hood. Additionally, the course will cover the ethical implications of AI and how to design and deploy machine learning models responsibly.
Machine Learning & Artificial Intelligence Course Outline
Module 1: Introduction to Machine Learning and AI
- Overview of machine learning and AI
- History and applications of machine learning
- Machine learning pipeline and process
- Types of machine learning: supervised, unsupervised, and reinforcement learning
Module 2: Data Preprocessing and Feature Engineering
- Data cleaning and transformation
- Feature selection and extraction
- Data visualization
Module 3: Supervised Learning
- Regression and classification problems
- Linear regression and logistic regression
- Decision trees and random forests
- Support vector machines
Module 4: Unsupervised Learning
- Clustering techniques
- Principal component analysis
- Dimensionality reduction
- Support vector machines
Module 5: Deep Learning
- Neural networks and deep learning architectures
- Convolutional neural networks (CNNs)
- Recurrent neural networks (RNNs)
- Autoencoders and generative adversarial networks (GANs)
Module 6: Evaluation Metrics and Model Selection
- Model evaluation metrics: accuracy, precision, recall, F1 score, AUC-ROC
- Cross-validation and hyperparameter tuning
- Model selection and ensemble methods
Module 7: Responsible and Ethical AI
- Bias and fairness in machine learning
- Privacy and security considerations
- Explainability and interpretability of machine learning models
- Societal implications of AI
Module 8: Emerging Trends in AI
- Reinforcement learning and deep reinforcement learning
- Natural language processing and text analytics
- Computer vision and image recognition
- Time series forecasting and anomaly detection
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I am Deborah Osilade, I am a Data Analyst and an Instructor at Piston and Fusion Business Academy. I love leveraging data to create high quality data intelligent solutions. I enjoy working on building Predictive Models, Financial models and designing Dashboards solutions. My specialization and core skills are in Finance, Data Science & Machine Learning. I have a Post Graduate Diploma in Data Science and Business Analysis from the University of Texas at Ausin and a B.A. Accounting and Finance degree from Middlesex University. My proficiency with data science tools span across Programming Language for Data Science, Data visualization tools and Data management tools.
Machine Learning & Artificial Intelligence Course Dates & Schedules
| Batch 1 | Batch 2 | Batch 3 | |
|---|---|---|---|
| Date | 24th May – 2nd June 2023 | 24th May – 2nd June | 24th May – 2nd June |
| Training Days | Mon & Wed | Tue & Thr | Sat |
| Training Time | 9am – 12pm | 1pm – 3pm | 9am – 3pm |
| Island Trading | Helen Bennett | UK | |
| Delivery Method | Classroom & Online | Hybrid | Classroom |
| Location | 122a Obadina stree Lagos | zoom | Microsoft Teams |
Course FAQ
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