100 days of Machine Learning

I continue to learn and work on ML projects, starting with basics to learn the fundamentals. This repository contains demos for machine learning, data analysis and visualization in Python using:

• numpy
• pandas
• matplotlib
• seaborn
• scikit-learn
• TensorFlow
• Keras

What is Machine learning? (ML) is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.

History time! The first ML model was built by Arthur Samuel in 1959. He developed a program that played checkers and learned from its own experience using a technique called "machine learning." This was a significant breakthrough in the field of artificial intelligence and paved the way for the development of more complex machine learning models. Since then, numerous researchers and scientists have contributed to the development of machine learning and built many different models for a wide range of applications.

Fundamental ML-concepts:

1. Supervised Learning: Supervised learning is a type of machine learning in which the model is trained on a labeled dataset, where the target variable is known. The goal is to predict the target variable for new, unseen data.

2. Unsupervised Learning: Unsupervised learning is a type of machine learning in which the model is trained on an unlabeled dataset, where the target variable is unknown. The goal is to discover patterns and relationships in the data.

3. Semi-Supervised Learning: Semi-supervised learning is a type of machine learning that uses both labeled and unlabeled data to improve the accuracy of the model.

4. Reinforcement Learning: Reinforcement learning is a type of machine learning in which an agent learns to make decisions by interacting with an environment and receiving feedback in the form of rewards or penalties.

5. Neural Networks: Neural networks are a type of machine learning algorithm that are inspired by the structure and function of the human brain. They consist of layers of interconnected nodes that process information and learn to make predictions.

6. Deep Learning: Deep learning is a subset of machine learning that uses deep neural networks with multiple layers to learn complex patterns and relationships in the data.

7. Feature Extraction: Feature extraction is the process of identifying and selecting the most important features or variables in the dataset to use in the machine learning model.

8. Model Evaluation: Model evaluation is the process of assessing the performance of a machine learning model on new, unseen data. Common evaluation metrics include accuracy, precision, recall, and F1 score.

Projects in this series:

Project 1: Iris Flower Classification

• Description: According to my reserach this is a classic beginner project where we classify iris flowers into three species (Setosa, Versicolor, and Virginica) based on the length and width of their sepals and petals.
• Learning Outcomes: Understanding classification problems. Familiarity with basic algorithms like k-Nearest Neighbors (k-NN), Decision Trees, and Logistic Regression.
• Challenges:
• Dataset: Iris Dataset
• Tools: Scikit-learn, Pandas

Project 2: Titanic Survival Prediction

• Description: This project involves predicting whether a passenger on the Titanic survived or not based on various features such as age, gender, ticket class, etc. The goal is to build a model that can accurately classify passengers into those who survived and those who did not.
• Learning Outcomes:
  • Understanding binary classification problems.
  • Hands-on experience with data preprocessing techniques such as handling missing values and feature engineering.
  • Introduction to various classification algorithms like Logistic Regression, Random Forest, and Support Vector Machines (SVM).
  • Model evaluation using metrics such as accuracy, precision, recall, and ROC-AUC.
• Challenges:
• Dataset: Public Titanic Dataset from Kaggle
• Tools: Pandas, Scikit-learn, Matplotlib, Seaborn

Project 3: Handwritten Digit Recognition (MNIST)

• Description: This project focuses on building a model to recognize handwritten digits (0-9) from the MNIST dataset. The dataset consists of 28x28 pixel images of digits, and the task is to classify each image into the correct digit.
• Learning Outcomes:
  • Working with image data.
  • Introduction to Convolutional Neural Networks (CNNs) for image recognition tasks.
  • Understanding the basics of deep learning and model training.
  • Model evaluation using metrics such as accuracy and confusion matrices.
• Challenges:
• Dataset: MNIST Dataset
• Tools: TensorFlow, Keras, Pandas

#LifeLongLearning

---

Sirin Koca | OsloMet | 2023