Kompetensi

Data Science

Python for Machine Learning and Data Analytics

This course will introduce the learner to applied data analytics with Python, focusing more on the techniques and methods than on the statistics behind these methods The course will start with a discussion of how machine learning is different than descriptive statistics and the introduction to the scikit learn toolkit

Course Objective

1. Mengenal pasti perbezaan antara teknik Supervised (classification) dan Unsupervised (Clustering);
2. Mengenal pasti teknik yang diperlukan untuk mengalisa data set tertentu;
3. Menggunakan Feature Engineering untuk memenuhi keperluan Machine Learning;
4. Menulis kod Python untuk menjalankan analisis data.

Course Outcomes

1. Identify difference between supervised (classification) and unsupervised (clustering) technique
2. Identify which technique they need to apply for a particular dataset and need
3. Engineer features to meet the machine learning needs
4. Write python code to carry out an analysis

Course Outline

1. Module1: Introduction
   • Introduction to Machine Learning
   • Introduction to Scikit-Learn Package
   • Regression vs Classification
2. Module2: Supervised Learning
   • K-Nearest Neighbour(kNN)
   • Naïve Bayes
   • Logistic Regression
   • Support Vector Machine (SVM)
   • Decision Tree & Random Forest
   • Hyperparameter Model Tuning, Regularization –Ridge and Lasso