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7 ways to reduce your AWS costs

Are you spending more than planned on AWS? Or maybe you just want to spend less? What can you do? With the great variety of services and pricing options that AWS offers, you can build unimaginable networks of servers in the cloud, something very difficult and expensive to do with traditional IT infrastructure. But with that power in your hands it is really easy to go far from what you exactly need, ending up with a lot of underused or overused running resources which are difficult to keep track of.   Most AWS users are initially attracted to the service because of its pay-as-you-use pricing model. Like running water or electricity, you only pay for what you. But as your usage increases so does your billing size. On-demand is great so far as your pockets go. But with careful budget planning you can benefit from other models and save a lot of money on the long run. There are several ways to save yourself from paying high bills on AWS. Once you are able to define or re-define your origina…

7 Machine Learning Algorithms every Data Engineer and Data Scientist Must know about!

Machine learning has become such a buzz word these days and that is because organisations are collecting more and more data and using these algorithms can help utilise and monetise the data. In this post I will give an overview of seven most common machine learning algorithms and in each subsequent post I will explain each of the algorithms and show you how to implement them using TensorFlow.

Sophisticated Machine Learning algorithms look set to replicate human intelligence and consciousness. Applications of Machine Learning encompass a variety of challenging and complex problems ranging from spam filtering and fraud detection, to marketing personalisation and online search recommendations, to smart cars and healthcare diagnostics. Understanding the algorithms behind these use cases is the first step toward advancement in Machine Learning.

Machine Learning algorithms come in (at least) three major flavours:

Unsupervised Learning: Instead of predicting results, this algorithm helps identify the structure of the available data that is not previously classified.

Supervised Learning: Algorithms that predict results or infer a function from the available data used as predictors.

Reinforced Learning: Algorithms that apply an action to the available data set, learn how well the desired action performed, and develops an effective strategy to select actions that deliver the best results.

Supervised Learning

Naïve Bayes Classification

A probabilistic classification algorithm that assumes the presence of a particular outcome is unrelated to the presence of other outcomes. As such, all predictor variables are treated independently.

The algorithm is commonly used in email spam filtering, social media sentiment analysis and other text-based classification problems.

Support Vector Machine (SVM) Algorithm

SVM models are used to separate datasets into classes with a clear and high-margin boundary, as much as possible. A dataset in N-dimensional space will be separated by a classifier in an N-1 dimension to enable clear distinction between segregated datasets across the hyperplane.

Such a binary classification is commonly used in relative comparison of stock performance, for instance. The SVM model would recognise the parameters impactful to stock performance such as time, geopolitical events, business type etc. and predict performance for future stock performance as un labeled data.

Logistic Regression

A binary classification model identifies the probability of the data belonging to a specific categorical dependent variable, based on the given information on predictor variables. The output is typically demonstrated as an S-curve for data segregation across multiple classes.

Naturally, the algorithm is used to identify solutions to common probability-related real-world problems such as the probable success rate of a particular new product offering, or if rain would occur on a particular day of the week. 

Unsupervised Learning

K-Means Clustering

A non-deterministic model that classify ‘n’ number of observations between ‘k’ types of clusters. A K-Mean or centroid is identified for each observation as an iterative process, typically until the data is appropriately partitioned between the K clusters.

A simple use case example includes search engine results of a keyword search that may return several different types of entries. For example, searching for the term “Apple” may return information on the Apple Inc. company; or apple the fruit; or even the 1967 Star Trek episode. K-Means Clustering enables to classify the returned Web pages based on appropriate definition of the term.

Principal Component Analysis (PCA)


The model is used to map multivariate, multi-dimensional datasets as linearly uncorrelated variables, or Principal Components on orthogonal axes. In essence, PCA reveals the internal structure of the dataset based on the most informative viewpoints.

The technique is commonly used in the neuroscience application of identifying spike-triggering actions of neurons.

Singular Value Decomposition (SVD)

A model that decomposes a complex matrix dataset into its fundamental parts. The redundant data is eliminated such that a list of N unique vectors of the matrix can be defined as a linear combination of fewer unique vector dimensions.

Data compression of image files is a common example of SVD, which filters out unnecessary excess information without significantly downgrading image quality.

Reinforced Learning

QLearning

A model used to identify action-selection policies to optimally control Markovian domains, or undirected graphical data sets of random fields.  The algorithm iteratively approximates the expected utility of present actions and develops policies with the highest rewards (accuracy of expected values) for future states of the Q function.

The technique is widely used in motion planning and navigation-based applications in robotics, automobiles and video games.

The feasibility of each algorithm depends on several considerations, including the accuracy and linearity of classification required, training time and number of parameters used to yield appropriate results. Each model may also make specific assumptions to accelerate performance or deliver useful results. The tradeoff is best developed after a detailed and thorough understanding of how each model works and the machine learning requirements of your datasets.

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