AI News, Machine Learning Algorithms for Every Occasion

Machine Learning Algorithms for Every Occasion

A machine learning algorithm is a method that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.

Algorithms like linear regression, deep learning, convolutional neural networks and recommendation systems are widely being used and explained.

Real world data collection is rife with problems like—missing data, noisy data, less data than what the algorithm needs.

So for example there are a lot of outliers in your data then linear regression will perform extremely poorly but decision trees would be a fairly stable solution.

Business question 1 — Let’s say a startup’s marketing team wants to decide the size of marketing budget.

(leftmost curve) Business Question 2 — While if the same company wants to separate their users into male and female categories on the basis of just their height and weight, then it becomes a question of classification.

Business Question 3 — If the same company wants to profile their users to understand their credit risk, the algorithm of choice would be unsupervised learning.

in the trend prediction example we already know a few data points that correspond to the values of (marketing budget, revenue).

Since we already know a few pairs before hand we can start predicting revenue for new values of marketing budget.

Business constraints appear in the form of costs involved, the time-to-market, promised speeds to the customers and energy effeciencies.

A startup has less money as compared to giants like google so a startup would deploy decision tree algorithm whereas Google would go for a random forest consisting of 1000s of decision trees.

Expert Talk: Data Science vs. Data Analytics vs. Machine Learning

Data science, analytics, and machine learning are growing at an astronomical rate and companies are now looking for professionals who can sift through the goldmine of data and help them drive swift business decisions efficiently.

We caught up with Eric Taylor, Senior Data Scientist at CircleUp, in a Simplilearn Fireside Chat to find out what makes data science and data analytics such an exciting field and what skills will help professionals gain a strong foothold in this fast-growing domain.

Created by Hugh Conway in 2010, this Venn diagram consists of three circles: math and statistics, subject expertise (knowledge about the domain to abstract and calculate) and hacking skills.

A data scientist gathers data from multiple sources and applies machine learning, predictive analytics, and sentiment analysis to extract critical information from the collected data sets.

They understand data from a business point of view and are able to provide accurate predictions and insights that can be used to power critical business decisions.

Venn DiagramSource: Drew Conway Anyone who’s interested in building a strong career in this domain should gain key skills in three departments: analytics, programming and domain knowledge.

data analyst should be able to take a specific question or topic, discuss what the data looks like and represent that data to relevant stakeholders in the company.

While a data scientist is expected to forecast the future based on past patterns, data analysts extract meaningful insights from various data sources.

Traditional machine learning software is comprised of statistical analysis and predictive analysis that is used to spot patterns and catch hidden insights based on perceived data.

Source: Quora Data science can be seen as the incorporation of multiple parental disciplines, including data analytics, software engineering, data engineering, machine learning, predictive analytics, data analytics, and more.

Data science is responsible for bringing structure to big data, searching for compelling patterns, and advising decision-makers to bring in the changes effectively to suit the business needs.

Yes, K-Means can help in ensuring better customer engagement!

If you look at my previous posts, I did attempt on supervised learning covering linear regression, logistic regression, gradient descent, Support Vector Machines and this may be the time to try something with respect to unsupervised learning.

Clustering often gets mixed up with Classification but they both represent two different categories of machine learning, the former maps to unsupervised learning and latter to supervised learning where the data is pretty much tagged/labeled for easy identification which is not the case with Unsupervised learning.

For example, if we take banks and its decision making on setting up the next ATM machine, it can be easily done by clustering customers based on their geographic location such that the densely populated location can be picked as the ideal candidate for setting up the next ATM.

Though the algorithm starts aligning data points with k random centroids, eventually the data points start converging to its logical clusters by refining and aligning with the ‘mean’ of the data points.

Let’s try understanding this little more in detail going by the steps involved with in the algorithm Overall, the algorithm takes less or more time to converge depending on how the data is represented, if the data is already separated well into clusters then it may take less time to converge than when the data is not alienated properly.