The Advanced Analytics and Machine Learning using Python course is designed to equip students with advanced skills in data analysis, statistics, and machine learning using the Python programming language. The course is suitable for individuals who have a basic understanding of Python programming and wish to further their knowledge in the area of advanced analytics and machine learning.
Throughout the course, students will explore various machine learning algorithms, such as decision trees, support vector machines, and others, and learn how to implement them in Python using popular libraries. In addition to theoretical concepts, the course emphasizes on the practical applications of advanced analytics and machine learning in real-world scenarios. Participants will work on various case studies, including data preprocessing, feature engineering, model selection, and evaluation.
Upon completion of the course, students will be able to confidently apply advanced analytics and machine learning techniques to a wide range of data-driven problems.
Course Objectives
- Evaluate the business problems in the financial industry today and the data associated with them to develop predictive analytics applications
- Understand and use the various algorithms and tools to work with the data and test hypotheses
- Construct analytics models/results as part of solutions to address business problems and derive patterns and solutions
- Learn to code machine learning algorithms and build models using Python
- Evaluate the performance of analytics models and learn how to optimize models
- Learn the various statistical models, machine learning algorithms, and understand how to use them in various business scenarios
- Evaluate the importance of data and features which are used to solve business problems
- Analyze the results or outputs of analytics models
- Understand and evaluate possible big data applications in conjunction with machine learning
Pre-requisites
- This course requires a basic understanding of Python. Participants who do not have the basic knowledge are encouraged to take up Fundamentals of Python Programming prior to this course.
- Hardware & Software
- This course will be conducted as a Virtual Live Class (VLC) via Zoom platform.
- Participants must own a Zoom account and have a laptop or a desktop with “Zoom Client for Meetings” installed. Download from zoom.us/download.
System Requirement |
Must-have: Please ensure that your computer or laptop meets the following requirements.
Good-to-have:
Not recommended: |
Course Outline
Module 1: Introduction to Machine Learning with scikit-learn
The objective is to understand the basics of machine learning and what it means. The module also introduces the basic concepts of supervised and unsupervised machine learning and introduces a very important library used for machine learning on Python – scikit-learn.
● Introducing the machine learning flow and concepts
● Functions within scikit-learn
● Introduction to supervised and unsupervised machine learning
Module 2: Unsupervised Machine Learning
This module aims to equip participants with the fundamentals of unsupervised machine learning using a very popular python library called scikit-learn. Unsupervised learning is very important across various business cases today, right from customer segmentation to property analysis.
● Understanding unsupervised ML algorithms
● Introduction to clustering (k-means)
● Implementing clustering with real use cases
Module 3: Supervised Machine Learning
Supervised machine learning is one of the most popular techniques in machine learning today. This module will stress on some of the most popular algorithms in regression and classification and equip participants with an understanding of how the algorithms work and where they can be used.
● Introduction to various supervised learning algorithms
● Understanding feature engineering and feature sets
● Understanding and implementing
○ Linear Regression
○ Logistic Regression
○ Support Vector Machines
○ Decision Trees
● Implementing the above algorithms with real use cases
Module 4: Evaluating machine learning models
One of the key steps in the data science lifecycle is to evaluate machine learning models to make sure the right one is selected for use in the business. Also, these models need to be trained and optimized over time. This module aims to do just that by covering the techniques aiding model selection and evaluation and optimization.
● Understanding model selection and evaluation methods
● Optimize machine learning models
Certificate Obtained and Conferred by
- Certificate of Achievement from NTUC LearningHub will be issued to participants who have met at least 75% attendance and passed the prescribed assessment(s).
- Upon meeting at least 75% attendance and passing the assessment(s), Statement of Attainment (SOAs) will be issued by SkillsFuture Singapore (SSG) to certify that the participant has achieved the following Competency Standard(s):
- Computational Modelling (ICT-DIT-3021-1.1)
Categories
More Information
- NTUC LearningHub
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