Machine Learning Models in Science

Go to Course: https://www.coursera.org/learn/machine-learning-models-in-science

Introduction

### Course Review: Machine Learning Models in Science on Coursera #### Overview Machine learning (ML) has become an indispensable tool for tackling complex scientific problems, and the course "Machine Learning Models in Science" on Coursera is an excellent way to dive into this innovative field. This course, open to learners from all backgrounds, focuses on the complete machine learning pipeline—from data preprocessing to advanced algorithms. Whether you're a budding data scientist or a seasoned researcher looking to infuse machine learning techniques into your work, this course is tailored for you. #### Syllabus Breakdown The course is well-structured into four main modules, each building on the previous one, ensuring that learners develop a robust understanding of both theory and practical application. 1. **Before the AI: Preparing and Preprocessing Data** The journey begins with the critical aspect of data preprocessing—an essential step often overlooked. This module covers various techniques for handling missing values and outliers, ensuring that your data is clean and ready for analysis. You'll delve into data transformations, focusing on Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), which are vital for dimensionality reduction. By coding these preprocessing techniques in Python, you'll gain hands-on experience that will prepare you for the applications of machine learning algorithms in subsequent modules. 2. **Foundational AI Algorithms: K-Means and SVM** This module introduces two fundamental algorithms: K-Means and Support Vector Machines (SVM). You'll explore the nuances of supervised and unsupervised learning—a crucial distinction in ML. The comparison between K-Nearest Neighbors and K-Means clustering enriches your understanding of classification and clustering tasks. The course provides a deep dive into the theory underlying these algorithms, followed by practical coding sessions to implement them in Python, reinforcing your newly acquired knowledge. 3. **Advanced AI: Neural Networks and Decision Trees** As you progress, the course introduces you to advanced machine learning techniques. Here, you'll learn about tree-based algorithms, specifically random forests, which have gained popularity for their effectiveness in classification and regression tasks. The module also guides you through the realm of neural networks, beginning with exploratory experiments using the TensorFlow playground. By coding your own neural networks, you’ll become adept at making predictions from unseen data, a critical skill in real-world applications. 4. **Course Project** To solidify your learning, the course culminates in a project aimed at predicting diabetes from health data. This hands-on experience is invaluable as it allows you to compare different regression models, analyze their performance, and apply your theoretical knowledge to a practical scenario. The course project serves not just as an assessment but as a concrete demonstration of your capabilities, making it a portfolio piece that will impress future employers. #### Recommendations I highly recommend "Machine Learning Models in Science" for anyone eager to unlock the potential of machine learning in scientific inquiry. The course is structured thoughtfully, making complex subjects accessible even for beginners. The instructors are knowledgeable, and the emphasis on Python programming ensures you gain practical skills alongside theoretical knowledge. Additionally, the course’s focus on real-world applications helps bridge the gap between academia and industry, making it a fantastic avenue for researchers, professionals, and enthusiasts alike. In conclusion, if you're looking to enhance your understanding of data science and machine learning while tackling fascinating scientific challenges, this course on Coursera is a must-enroll! You'll emerge from the experience equipped with the skills and confidence needed to apply machine learning models effectively in scientific contexts.

Syllabus

Before the AI: Preparing and Preprocessing Data

In this module, we'll tackle the steps taken before we can use AI algorithms. We'll start with an introduction to the most prominent data preprocessing techniques including filling in missing values and removing outliers. Then we'll dive into data transformations including PCA and LDA, two methods featured heavily for dimensionality reduction. Finally, we'll learn how to code the algorithms in Python to set up your data for use in the next module.

In this module, we'll dive into two of the most foundational machine learning algorithms: K-Means and support vector machines. We'll start by comparing the two branches of ML: supervised and unsupervised learning. Then, we'll go into the specific similarities and differences between K-Nearest neighbors for classification and K-Means clustering. Finally, we'll perform deep dives into K-Means and SVMs, learning the basic theory behind them and how to implement each in Python.

In this module, we'll explore some advanced AI techniques. We'll start with tree-based algorithms, made popular because of the use of random forests for both classification and regression. Then, we'll build our way to neural networks, starting from experimentation on the different models. We'll spend some time in the Tensorflow playground getting familiar with the different mechanics behind neural networks. Finally, we'll code our own neural networks to make predictions on unseen data.

In this module, we'll go through a course project to predict diabetes from health data. We'll compare different regressors by implementing them and checking the error on a test set.