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Professional Certificate in Machine Learning

This program covers a recap of basics in Python programming plus concepts and implementations of Machine Learning (ML) including regression, classification and clustering using Python and libraries like Keras and SK learn. This includes ML algorithms like linear and logistic regression, KNN, decision trees, support vector machines, K-means, neural networks along with their applications. Further it focuses on recent research trends in ML and an introduction to industry implementations of ML algorithms using cloud-based tools and services.

Next Intake

February 2023

Intakes

Feb/Aug

Duration

2 ½ Months

(Part Time)

Saturdays 9.00 am to 1.00 pm

Entry Requirements

Knowledge in Python, Coding

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Course Outline

1.0 Python recap
1.1 procedural and OOP
2.0 Introduction to Machine Learning (Pasan)
2.1 Ideas of regression, classification, clustering, Neural Networks and Deep NN
3.0/4.0 Regression
linear and logistic
Discussion on SK learn and Keras for ML Including model training and evaluation
5.0/6.0 Classification
KNN, DT, SVM, Ensemble Systems
7.0 Clustering
5.1 K-means

8.0 Neural Networks
9.0 Machine Learning in the Cloud (MLaaS)
10.1ML OPS
10.2 Research Opportunities

Learning Outcomes

1. Understanding and ability to apply the fundamental programming elements on Python and
its OOP concepts.
2. Understanding the types of machine learning algorithms in high level.
3. In-depth understanding of concepts and ability to apply supervised and unsupervised
machine learning techniques including Regression, Classification and Clustering for real-life
problems.
4. In-depth understanding of concepts and ability to apply Neural Network related machine
learning techniques like CNN, RNN and LSTM for real-life problems.
5. Ability to design, develop and deploy machine learning workflows in cloud based
environments like AWS, Azure, GCP.
6. Understanding of scientific research opportunities in the area of machine learning.