Machine Learning

Course Objectives:

• Develop a clear understanding of machine learning concepts and terminology.
• Explore various machine learning algorithms and their real-world applications.
• Learn how to preprocess and transform data for machine learning tasks.
• Acquire hands-on experience in building and evaluating machine learning models.
• Prepare for further exploration of advanced machine learning topics.

Course Curriculum:

1. Introduction to Machine Learning

   • Understanding the role of machine learning in data analysis
   • Types of machine learning: supervised, unsupervised, and reinforcement learning

2. Data Preprocessing and Feature Engineering

   • Data cleaning, handling missing values, and outlier detection
   • Feature selection, transformation, and scaling techniques

3. Supervised Learning Algorithms

   • Linear regression, logistic regression, and decision trees
   • Support vector machines (SVM) and k-nearest neighbors (k-NN)

4. Unsupervised Learning Algorithms

   • Clustering techniques: k-means, hierarchical clustering, and DBSCAN
   • Dimensionality reduction methods: PCA and t-SNE

5. Model Evaluation and Validation

   • Splitting data into training and testing sets
   • Metrics for evaluating classification and regression models

6. Introduction to Neural Networks

   • Basic concepts of artificial neural networks
   • Overview of deep learning and its applications