Machine learning represents a key evolution in the fields of computer science, data analysis, software engineering, and artificial intelligence.
This program will teach you how to become a machine learning engineer, and apply predictive models to massive data sets in fields like finance, healthcare, education, and more.
Co-Created by:
- Kaggle
Exploration
In this introductory project, we will explore a subset of the RMS Titanic passenger manifest to determine which features best predict whether someone survived or did not survive. To complete this project, you will need to implement several conditional predictions and answer the questions below. Your project submission will be evaluated based on the completion of the code and your responses to the questions.
Model Evaluation and Validation
In this project, you will apply basic machine learning concepts on data collected for housing prices in the Boston, Massachusetts area to predict the selling price of a new home. You will first explore the data to obtain important features and descriptive statistics about the dataset. Next, you will properly split the data into testing and training subsets, and determine a suitable performance metric for this problem. You will then analyze performance graphs for a learning algorithm with varying parameters and training set sizes. This will enable you to pick the optimal model that best generalizes for unseen data. Finally, you will test this optimal model on a new sample and compare the predicted selling price to your statistics.
Supervised Learning
A local school district has a goal to reach a 95% graduation rate by the end of the decade by identifying students who need intervention before they drop out of school. As a software engineer contacted by the school district, your task is to model the factors that predict how likely a student is to pass their high school final exam, by constructing an intervention system that leverages supervised learning techniques. The board of supervisors has asked that you find the most effective model that uses the least amount of computation costs to save on the budget. You will need to analyze the dataset on students' performance and develop a model that will predict the likelihood that a given student will pass, quantifying whether an intervention is necessary.
Unsupervised Learning
A wholesale distributor recently tested a change to their delivery method for some customers, by moving from a morning delivery service five days a week to a cheaper evening delivery service three days a week.Initial testing did not discover any significant unsatisfactory results, so they implemented the cheaper option for all customers. Almost immediately, the distributor began getting complaints about the delivery service change and customers were canceling deliveries — losing the distributor more money than what was being saved. You’ve been hired by the wholesale distributor to find what types of customers they have to help them make better, more informed business decisions in the future. Your task is to use unsupervised learning techniques to see if any similarities exist between customers, and how to best segment customers into distinct categories.