Artificial Intelligence (Machine Learning) Program (6 Weeks)
Artificial Intelligence (Machine Learning) Program (6 Weeks)
Program Overview: The Artificial Intelligence (Machine Learning) course is designed to introduce students to the fundamentals of machine learning (ML) and its practical applications. Through this course, participants will learn how to create models that allow computers to learn from and make predictions based on data. The curriculum provides a comprehensive introduction to supervised and unsupervised learning, model evaluation, and real-world ML techniques that are widely used in industries such as finance, healthcare, e-commerce, and more.
Course Duration: 6 weeks
Fees: 1,200,000 UGX
Course Objectives:
Upon completion of this course, students will:
Understand Core ML Concepts: Learn key principles behind machine learning, including supervised, unsupervised, and reinforcement learning techniques.
Master Data Preprocessing and Analysis: Gain the skills necessary to clean, preprocess, and analyze data using programming languages such as Python.
Implement ML Algorithms: Learn how to apply popular algorithms such as linear regression, decision trees, k-nearest neighbors (KNN), and support vector machines (SVM).
Build Predictive Models: Understand how to train, evaluate, and optimize models for real-world prediction tasks.
Explore Real-world Applications of ML: Gain insights into how ML is transforming industries and how to use these technologies in practical scenarios.
Work with ML Libraries: Utilize libraries such as scikit-learn, Pandas, and NumPy for building and implementing machine learning solutions.
Detailed Course Outline:
Introduction to Machine Learning:
Overview of artificial intelligence (AI) and machine learning.
Understanding the different types of learning: supervised, unsupervised, and reinforcement learning.
Overview of machine learning processes: data collection, model selection, training, and evaluation.
Data Preprocessing and Analysis:
Introduction to data cleaning, normalization, and feature extraction.
Handling missing data and outliers.
Exploratory data analysis and visualization techniques.
Using Python for data manipulation with Pandas and NumPy.
Supervised Learning Algorithms:
Linear regression, logistic regression, and their applications.
Classification algorithms: decision trees, k-nearest neighbors (KNN), and support vector machines (SVM).
Model training, validation, and testing.
Bias-variance tradeoff and overfitting prevention techniques.
Unsupervised Learning Techniques:
Introduction to clustering algorithms like k-means and hierarchical clustering.
Principal component analysis (PCA) for dimensionality reduction.
Applications of unsupervised learning in data mining and customer segmentation.
Model Evaluation and Tuning:
Performance metrics: accuracy, precision, recall, F1-score, and confusion matrix.
Cross-validation and hyperparameter tuning.
Introduction to ensemble methods such as random forests and boosting.
Practical Projects and Use Cases:
Building predictive models using real-world datasets.
Hands-on projects in areas such as healthcare, finance, marketing, and image recognition.
Presenting and visualizing results to stakeholders.
Career Prospects:
Graduates of the Artificial Intelligence (Machine Learning) course will have the necessary skills to pursue roles such as:
Machine Learning Engineer
Data Analyst
AI Researcher
Business Intelligence Analyst
Software Engineer specializing in AI
Why Choose This Course?
Practical Applications: The course focuses on real-world problems, equipping students with industry-relevant skills.
Comprehensive Training: With a balance of theory and hands-on practice, students will leave the course prepared to tackle complex ML challenges.
Expert Instruction: Learn from industry professionals with vast experience in AI and ML.
