Adam Khoo Learning Center @ West Coast Plaza, 154 West Coast Road, Singapore 127371
Before the camp, students are required to pick up basic Python and programming concepts as well as review some statistical concepts.
We ensure that students are committed to the four week camp and are not immediately thrown into the deep end of the pool once it starts.
This week introduces students to the tools and libraries of Python that data scientists regularly use. The program also goes over basic collegiate-level statistical concepts.
The objective is to make the students comfortable with the tools they will use for the next four weeks to write programs in Python.
Manipulation & Analysis
Preprocessing data is crucial before conducting analysis, especially when the dataset contains thousands of rows and values. This week teaches some data manipulation techniques, including scaling the data to fit a normal distribution.
Intro to machine learning
Normalizing data is important because it reduces maps data from multiple distributions to a single scale. After preprocessing, the students learn how to create basic classification algorithms. This serves as the introduction to machine learning.
Prediction and Classification
Students dive into more machine learning topics, including linear / logistic regression and ensemble learning. Sci-kit learn will be used heavily to build and train classifiers.
Students will be comfortable with the library by the end of the week. Moreover, several optimization techniques are introduced to speed up training time, including principal component analysis.
Evaluation and Visualization
Students learn how to evaluate and measure the accuracy of classifiers. Evaluation is important when selecting a model for analyzing and classifying data.
After covering the basic process of building machine learning models, students will dive into additional topics that help further optimize their models.
Students learn some basic software engineering skills, so they can deploy the machine learning models they have constructed to the web using Flask.
Students apply the techniques from the first 5 weeks to the task of image recognition and classification, working with the MNIST database.
After learning more about visualizing data, students focus on their final projects. Once that is complete, they will receive an official certificate and start their internship.
Ready To Go?
Learn about data science and its applications. No programming background required. Learn with peers; form valuable networks.