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Introduction to Machine Learning using Python
  • Overview
  • Objectives & Outline
  • Methodology
  • Participant Profile
  • Trainer
  • Overview
    PROGRAMME DETAILS

    Duration

    2 days

    TIME

    9:00 AM – 5:00 PM

    VENUE

    Asian Banking School
    This is an introductory programme on machine learning using scikit-learn and scipy for structured data, and common text processing methods. It has been designed for participants who aim to understand the possible applications of these techniques in their respective organisations.
    LEARNING LEVEL
    Foundation
    PROGRAMME FEE

    AICB MEMBER

    MYR

    2,200*

    / PAX

    NON-MEMBER

    MYR

    2,500*

    / PAX

    *Subject to 6% Service Tax

  • Objectives & Outline
    LEARNING OBJECTIVES
    By the end of this programme, participants will be able to:

    • Understand how to navigate themselves around the scikit-learn library, its documentation, and its six main purposes: Classification, Regression, Clustering, Dimensionality Reduction, Model Selection and Preprocessing
    • Run basic statistical operations using the scipy library
    • Implement basic text analysis methods for unstructured data

    PROGRAMME OUTLINE
    Basic Statistics with Scipy:
    • Discrete Statistical Distributions: Binomial Distribution, Poisson Distribution and Geometric Distribution
    • Continuous Statistical Distributions: Chi-squared Distribution, Exponential Distribution and Normal Distribution

    Classification and Regression:
    • Ordinary Least Squares
    • Ridge Regression
    • Logistic Regression
    • Support Vector Machines
    • Nearest Neighbours
    • Decision Trees

    Clustering:
    • K-Means
    • Spectral Clustering

    Dimensionality Reduction:
    • Principal Component Analysis
    • Latent Dirichlet Allocation (LDA)
    • Model selection and evaluation:
    • Cross-validation
    • Tuning hyper-parameters of an estimator
    • Quantify quality of predictions

    Pre-processing:
    • Standardisation and scaling
    • Normalisation
    • Encoding categorical features

    Natural Language Processing:
    • Extract basic features like number of words and characters etc.
    • Text pre-processing like punctuation removal, tokenisation, lemmatisation
    • Text processing like n-gram, bag of words and name entity recognition

  • Methodology

    Slides presentation, interactive discussions, workshop and experience sharing.

    Participants are expected to have a Google account with access to Google Colab and have some exposure to Mathematics/Statistics at pre-university level

  • Participant Profile
    Data analysts, junior data scientists and those interested in gaining valuable knowledge in this high demand area
  • Trainer

    KOH WYHOW

    Koh Wyhow is manager of the data science team at Star Media Group Berhad. He focuses on delivering advanced analytics and business intelligence solutions for the organisation like chatbots and image recognition solutions. He has consulted for clients in the airlines, media, property and FMCG industries during his time as a senior consultant at EY’s Data and Analytics team.

    His core capability is the ability to leverage data analytics to drive business opportunities in areas of customer relationship management (profitability, retention, upsell/cross sell), digital analytics (online behavioural analytics, DMP), visual analytics and enterprise dashboards.
    He was one of the data scientists which implemented strategies to run a national data-driven campaign for INVOKE during the 14th General Elections. As an independent learner, he picked up basic Python programming skills after office hours during his days as a Further Mathematics lecturer at a private college. Wyhow holds a BSc in Mathematics from the National University of Singapore.

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Please complete the following form to register with us. Alternatively, you can email us here or call us at +603 2742 7822 or +603 2742 7821 if you have any questions.
PAYMENT PROCESS

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