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Welcome To William Tan Data Science & Machine Learning Portfolios

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Data Science & Machine Learning Enthusiast!

Education

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Certifications

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Technological Toolbox/Skills

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Artificial Intelligence

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Artificial Intelligence (AI) is a science that empowers computers to mimic human intelligence, such as decision-making, text processing, and visual perception. AI is a broader field that contains several subfield such as machine learning, deep learning, robotics, and computer vision.

Machine Learning

Machine Learning is a subfield of Artificial Intelligence that enables machines to improve at a given task with experience. It is important to note that all machine learning techniques are classified as Artificial Intelligence ones. However, not all Artificial Intelligence could count as Machine Learning since some basic Rule-based engines could be classified as AI, but do not learn from experience therefore, they do not belong to the machine learning category.

Click Project 1: Classification & Regression

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Supervised Machine Learning: In Supervised learning, you train the machine using data which is well “labelled.” It means some data is already tagged with the correct answer. It can be compared to learning in the presence of a supervisor or a teacher.

What is the difference between regression and classification in supervised learning?

* Classification is the task of predicting a discrete class label.

* Regression is the task of predicting a continuous quantity.

CLASSIFICATION CASE STUDY - This project performs HR analytics that is revolutionising the way human resources departments operate, leading to higher efficiency and better results overall. Human resources have been using analytics for years. However, the collection, processing and analysis of data have been largely manual, and given the nature of human resources dynamics and HR KPIs, the approach has been constraining HR. Here is an opportunity to try machine learning in identifying the employees most likely to get promoted.

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REGRESSION CASE STUDY - This project pertains to Airbnb wants to expand on travelling possibilities and present a more unique, personalised way of experiencing the world. Utilise all these features from the dataset to predict the rental price of the listed properties.

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Click Project 2: Clustering & Association Rules

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Unsupervised learning is a machine learning technique where you do not need to supervise the model. Instead, you need to allow the model to work on its own to discover information. It mainly deals with unlabelled data.

CLUSTERING CASE STUDY - This project focuses on bank credit risk, in particular the customer credit, which the bank will gain from giving credit only if the customers do not default on the loan, which means that they will not repay the debt. One of the solutions to address the bank problem is to use hierarchical clustering, which requires creating clusters that have predetermined order from bottom to top using the Agglomerative, which is an unsupervised machine learning algorithm used to cluster unlabeled data points.

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ASSOCIATION RULES CASE STUDY - This project creates recommendation systems that are being widely used in all forms of digital platforms; Association rules can be applied in the form of TV Shows recommendation systems to discover the existing relations between features in the database. By analysing the database, which contains TV shows of distinguishable users, it can find some interesting rules occurring in the analysed data.

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Deep Learning

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Deep Learning is a specialised field of Machine Learning that relies on the training of Deep Artificial Neural Networks (ANNs) using a large dataset such as images or texts. ANNs are information-processing models inspired by the human brain. The human brain consists of billions of neurons that communicate with each other using electrical and chemical signals and enable humans to see, feel, and make the decision. ANNs work by mathematically mimicking the human brain and connecting multiple “artificial” neurons in a multilayered fashion. The more hidden layers added to the network, the deeper the network gets. What differentiates deep learning from machine learning techniques is their ability to extract features automatically as illustrated in the following example:

* Machine learning Process: (1) selecting the model to train, (2) manually performing feature extraction.

* Deep Learning Process: (1) Select the architecture of the network, (2) features are automatically extracted by feeding in the training data (such as images) along with the target class (label).

Click Project 3: Image Classifications

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Convolutional Neural Network (CNN) is a class of deep neural networks most commonly used for analysing visual imagery. Convolution layers are the building blocks of CNNs. Convolution is the simple application of a filter to an input that results in activation. Repeated application of the same filter to an input results in a map of activations called a feature map, indicating the locations and strength of a detected feature in input, such as an image. What makes CNN so powerful and useful is that it can generate excellent predictions with minimal image preprocessing. Also, CNN is immune to spatial variance and can detect features anywhere in the input images.

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VGG16 is a CNN architecture which was used to win ILSVR (Imagenet) competition in 2014. It is considered to be one of the excellent vision model architectures to date. The most unique thing about VGG16 is that instead of having a large number of hyper-parameters, they focused on having convolution layers of 3x3 filter with a stride 1 and always used the same padding and maxpool layer of 2x2 filter of stride 2. It follows this arrangement of convolution and max pool layers consistently throughout the whole architecture. In the end, it has 2 FC (fully connected layers) followed by a softmax for output. The 16 in VGG16 refers to it having 16 layers that have weights. This network is a pretty large network and it has about 138 million (approx) parameters.

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IMAGE CLASSIFICATIONS CASE STUDY - This project focuses on building various multiclass image classification models to recognise and classify ten different types of food.

Click Project 4: Sentiment Analysis

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Recurrent Neural Network (RNN) processes sequences by iterating through the sequence elements and maintaining a state containing information relative to what it has seen so far. In effect, an RNN is a type of neural network that has an internal loop. The state of the RNN is reset between processing two different, independent sequences so still consider one sequence a single data point: a single input to the network. What changes is that this data point is no longer processed in a single step; rather, the network internally loops over sequence elements.

Long Short-Term Memory (LSTM) was created as the solution to short-term memory caused by RNN. It has internal mechanisms called gates that can regulate the flow of information. An LSTM has a similar control flow as a recurrent neural network. It processes data passing on information as it propagates forward. The differences are the operations within the LSTM’s cells.

GRU is the newer generation of Recurrent Neural networks and is pretty similar to an LSTM. GRU’s got rid of the cell state and used the hidden state to transfer information to solve the vanishing gradient problem with a standard recurrent neural network. It also only has two gates, a reset gate and an update gate.

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GloVe (Global Vectors for Word Representation) is an unsupervised learning algorithm for obtaining vector representations for words developed by Stanford researchers in 2014. Training is performed on aggregated global word-word co-occurrence statistics from a corpus, and the resulting representations showcase interesting linear substructures of the word vector space.

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SENTIMENT ANALYSIS CASE STUDY - This project focuses on building a sentiment analysis model to predict Disney Plus Disney Plus App review scores based on Google Play Store reviews.

Click Project 5: Feature Engineering

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Feature engineering is a machine learning technique that leverages data to create new variables that are not in the training set. It can produce new features for both supervised and unsupervised learning, with the goal of simplifying and speeding up data transformations while also enhancing model accuracy.

FEATURE ENGINEERING CASE STUDY - This project focuses on wrangling the data from the three baseball datasets to understand various data wrangling techniques such as joining the tables and exploring, preparing, and transforming data through multiple methods. Once the data has been transformed and is ready for modelling, proceed to build regression and classification models.

Click Project 6: Data Visualisation

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DATA VISUALISATION CASE STUDY - This project focuses on assuming that you are part of the market research team for Cardio Good Fitness, a retail business is specialising in the sales of treadmills. The team has collected data on individuals who purchased a treadmill at the Cardio Good Fitness retail stores for the past three months. Through data preparation, exploration and visualisation, the market research team decides to investigate whether there are differences across the product lines with respect to customer characteristics.

Data Science

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The ultimate goal of data science is to solve problems by extracting knowledge from data and providing support for complex decisions. The first part of solving a problem is getting a good understanding of its domain. You need to understand the business before using data science for risk analysis. You need to know the details of the business processes before designing an automated quality assurance process. First, you understand the domain. Then, you find a problem. If you skip this part, you have a good chance of solving the wrong problem. After coming up with a good problem definition, you seek a solution.

Click Project 7: Statistics

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Statistics, in general, is the method of collection of data, tabulation, and interpretation of numerical data. It is an area of applied mathematics concerned with data collection analysis, interpretation, and presentation. With statistics, we can see how data can be used to solve complex problems.

STATISTICAL ANALYSIS CASE STUDY - This project focuses on answering six questions about the students’ performance. Using the appropriate data from the dataset provided, perform statistical techniques to provide answers to these questions.

Click Project 8: Python Programming

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Python is an open-sourcing programming language. Therefore, it is freely available for everyone to access, download, and execute. There are no licensing costs involved with Python. Companies can save money by working with Python. The open-source nature of Python also ensures that developers can update the programming language and make modifications. But there are costs involved in using Python for app development. They may not be the licensing costs but the price of hiring a programmer, paying the development partner, and maintaining the app. Python programming language has a syntax similar to the English language, making it extremely easy and simple for anyone to read and understand its codes. You can pick up this language without much trouble and learn it easily. This is one of the reasons why Python is better compared to other programming languages such as C, C++, or Java.

PYTHON PROGRAMMING CASE STUDY - This project aims to address Part (A) requirement to compute the correlation coefficient between the HDB Resale Price Index (RPI) and Singapore’s population from 1990 to 2019. Part (B) requirements are to develop a system with the main menu to allow users to input the basic savings, savings with pay raise, choice of HDB flat to buy, identify the most expensive flats per sqm and exit from the system.