Basics of machine Learning

This course is designed to provide individuals with a foundational understanding of machine learning concepts and techniques. This course aims to equip learners with the fundamental knowledge and skills necessary to explore and apply machine learning algorithms in various domains.

By the end of the course, participants should have a solid understanding of the fundamental concepts and techniques used in machine learning. They should be able to select appropriate algorithms for different types of problems, preprocess and analyze data, train and evaluate machine learning models, and interpret the results.

Week 1: Introduction to Machine Learning

• Understanding the basics of machine learning and its applications

• Differentiating between supervised, unsupervised, and reinforcement learning

• Overview of the machine learning workflow and process

• Setting up the development environment for machine learning tasks

Week 2: Data Preprocessing and Exploratory Data Analysis

• Data cleaning and handling missing values

• Feature selection and feature engineering

• Handling categorical data and numerical data normalization

• Exploratory data analysis techniques for understanding data patterns and relationships

Week 3: Supervised Learning Algorithms

• Introduction to supervised learning

• Linear regression and logistic regression Naive Bayes classifier

• Decision trees and random forests

• Evaluating and validating machine learning models

Week 4: Unsupervised Learning Algorithms

• Introduction to unsupervised learning

• Clustering algorithms (K-means, hierarchical clustering)

• Dimensionality reduction techniques (Principal Component Analysis, t-SNE)

• Anomaly detection techniques

• Evaluating unsupervised learning results and performance metrics

Week 5: Introduction to Neural Networks and Deep Learning

• Basics of artificial neural networks

• Activation functions and backpropagation algorithm

• Introduction to deep learning architectures (feedforward, convolutional, recurrent)

• Transfer learning and pre-trained models

• Fine-tuning and optimizing deep learning models

Week 6: Model Evaluation, Deployment, and Ethical Considerations

• Advanced evaluation metrics for classification and regression models

• Overfitting, underfitting, and regularization techniques

• Deploying machine learning models in production environments

• Introduction to ethical considerations in machine learning

• Bias, fairness, and transparency in machine learning systems