kk289/IBM-Data-Science

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kk289/IBM-Data-Science

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This repo consists of all courses of IBM - Data Science Professional Certificate, providing with techniques covering a wide array of data science topics including open source tools and libraries, methodologies, Python, databases, SQL, data visualization, data analysis, and machine learning. You will practice hands-on in the IBM Cloud using real data science tools and real-world data sets.

Predicting-Customer-Lifetime-Value_CLV

Predicting Customer Lifetime Value

Amazon-SageMaker

AWS is a powerful tool for learning and implementing machine learning algorithms.

Jupyter Notebook

ML-Multiclass_Classification_and_Neural_Network-MATLAB

Neural networks is a model inspired by how the brain works. It is widely used today in many applications: when your phone interprets and understand your voice commands, it is likely that a neural network is helping to understand your speech; when you cash a check, the machines that automatically read the digits also use neural networks.

ML-Support_Vector_Machines-MATLAB

We use support vector machines (SVMs) with various example 2D datasets. Experimenting with these datasets will help us gain an intuition of how SVMs work and how to use a Gaussian kernel with SVMs. In the next half of the exercise, we use support vector machines to build a spam classifier.

Stock_Price_Prediction

Stock Price Prediction of APPLE Using Python

ML-Regularized_Linear_Regression-Bias_Variance-MATLAB

Here, we implement regularized linear regression to predict the amount of water flowing out of a dam using the change of water level in a reservoir. In the next half, we go through some diagnostics of debugging learning algorithms and examine the effects of bias v.s. variance.

CS-215_Fundamental-of-Programming-II

Fundamental of Programming II

CS-210_Fundamental-of-Programming-I

Fundamental of Programming I

Predictive_Modelling_with_Azure_Machine_Learning_Studio

Build a predictive model using Azure ML Studio. Demonstrate a working knowledge of setting up experiments on Azure ML Studio. Operationalize machine learning workflows with Azure's drag-and-drop modules.

Java-Automation-OpenBrowser

Web Browser Automation with Selenium and Java.

ML-Neural_Networks_Learning-MATLAB

The Neural Network is one of the most powerful learning algorithms (when a linear classifier doesn't work, this is what I usually turn to), and explaining the 'backpropagation' algorithm for training these models.

ML-K-Means_Clustering_and_PCA-MATLAB

This repo leads us to implement the K-means clustering algorithm and apply it to compress an image. And use principal component analysis to find a low-dimensional representation of face images.

ML-Anomaly_Detection_and_Recommender_Systems-MATLAB

Implement the anomaly detection algorithm which is widely used in fraud detection (e.g. ‘has this credit card been stolen?’) and apply it to detect failing servers on a network. And use collaborative filtering to build a recommender system for movies, which are used by companies like Amazon, Netflix, and Apple to recommend products to their users. Recommender systems look at patterns of activities between different users and different products to produce these recommendations.