Section outline

  • Module Lecturer

    Mafas Raheem

    Data Scientist | Business Analyst | Senior Lecturer

    I am an academic/trainer/researcher specializing in the field of Data Science & Business Analytics with nearly 17 years of academic & industry experience. I hold an MSc in Data Science & Business Analytics and a Master of Business Administration degree and currently reading my PhD in the area of machine learning (Natural Language Processing) at the Asia Pacific University of Innovation and Technology, Malaysia. I have published a significant number of indexed journal articles in the area of Machine Learning and Data Science matching the current business needs.

    I am actively involved in consulting data analytics/machine learning projects for the business/retail domains. I have been involved in numerous data mining projects in Malaysia, and overseas. My knowledge in statistics along with my data mining/machine learning expertise always adds value in solving the contemporary business problems faced by SMEs in the area of market expansion. Also, I conduct training for data analysts and data science professionals in the area of machine learning, data storytelling and business analysis.

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    Email: raheem@apu.edu.my

    Email Subject: CT052-3-M-ODL-NLP– your intake – your name – subject/request title
    Use only your APU official Email for correspondence.

    Consultation:

    Refer to “Staff Consultation Hour” on APU Apspace to book appointments.


    Module Synopsis
    The module discusses various models and techniques in current NLP practices. The module covers a broad range of topics in natural language processing, including word and sentence tokenization, text classification and sentiment analysis, spelling correction, information extraction, parsing. Further, it also introduces the underlying theory from probability, statistics, and machine learning that are crucial for the field, and cover fundamental algorithms like n-gram language modelling, naive bayes and maxent classifiers. The specified theories and concepts will be delivered using relevant natural language processing libraries such as NLTK, textblob, VADER, langdetect and translate along with Scikit-Learn to handle machine learning algorithms and related operations.


    Course Learning Outcomes (CLO)
    At the end of the course the students will be able to:

    CLO1     Demonstrate candidate natural language processing techniques for a problem in a specific domain (A3, PLO6)
    CLO2     Formulate text processing techniques for a real-world application (C6, PLO2)
    CLO3     Defend a proposed natural language processing system for a chosen problem (A4, PLO10)


    Course Outline



    Assessments
    In-course Assessment - 100%
    1. Report - 60%
    2. Demo Presentation - 40%

    References

    Recommended References
    These text books are available in APU eLibrary
    1. Campesato, O. (2020). Python 3 for machine learning. Mercury Learning & Information. ISBN-13: 9781683924937
    2. Liu, Z., Lin, Y., & Sun, M. (2020) Representation Learning for Natural Language Processing. Springer Singapore. ISBN-13: 9789811555732
    3. Patrick, et. al., (2020) Natural Language Processing. SAGE Publications Ltd. ISBN-13: 9781529749120