Google's Deepmind AlphaGo victory over traditional Go champions was a stunning exhibition of how far machine learning has come. It's use of unorthodox and persistent winning algorithms showed the advancement of mathmatical logic.

Progress is always a two edge sword. The benefits of technology can be used to improve or decrease its advantages to the environment and society. The increase in the scope of available soft and hard power impacts on privacy and destruction. The threat of an AI killer robot is much more unlikely than the insidious drip drip of central control bleeding into politics and democracy. We should not create things because we can but because it has merit.

Learning and other algorithmic based solutions have received a lot of media attention which stretches from the helpful to the ridiculous. The development so far has been two dimensional and uses the speed of the chip and unlimited resources to back up an apparent front end of encroachment on human abilities. For those of us who have studied both machine learning and neuroscience it is apparent that humans and machines are very different entities. The brain is a collection of highly adapted cells which are modified to carry out specific tasks and integrated throughout the brain and body. The synthetic approach so far has been to use brute force to overcome the weaknesses of artificial design. The latest developments have sought to use a quasi neuronal type of inherent weighting to simulate the human brain. The question should be as always, is what are we trying to do here? If we are trying to out compete the human brain then we better get ready for a long wait.

One area being investigated is the use of robot swarms. This envisages small to large size communities of interlinked robots moving by various means in co-ordinated manoeuvres to obtain a goal. They may have an autonomous action controlled by a central or hive type capacity. Some also suggest they may replace declining bees as pollen spreaders. Although saving the bees could be a better alternative.

The effect of "AI" in medicine cannot be overstated. Its use in predictive and analytic outlooks using the enormous available data is changing the landscape in genetics and therapy. Robotic surgery whether local or remote offers a more reliable and detailed ability to improve the outlook for patients. The ideal scenario would be the increased training and lower costs for these benefits to become universal.

To understand the working functionality of this algorithm, imagine how you would arrange random logs of wood in increasing order of their weight. There is a catch; however – you cannot weigh each log. You have to guess its weight just by looking at the height and girth of the log (visual analysis) and arrange them using a combination of these visible parameters. This is what linear regression is like.

In this process, a relationship is established between independent and dependent variables by fitting them to a line. This line is known as the regression line and represented by a linear equation Y= a *X + b.

In this equation:

- Y – Dependent Variable
- a – Slope
- X – Independent variable
- b – Intercept

The coefficients a & b are derived by minimizing the sum of the squared difference of distance between data points and the regression line.

Logistic Regression is used to estimate discrete values (usually binary values like 0/1) from a set of independent variables. It helps predict the probability of an event by fitting data to a logit function. It is also called logit regression.

These methods listed below are often used to help improve logistic regression models:

- include interaction terms
- eliminate features
- regularize techniques
- use a non-linear model

It is one of the most popular machine learning algorithms in use today; this is a supervised learning algorithm that is used for classifying problems. It works well classifying for both categorical and continuous dependent variables. In this algorithm, we split the population into two or more homogeneous sets based on the most significant attributes/ independent variables.

SVM is a method of classification in which you plot raw data as points in an n-dimensional space (where n is the number of features you have). The value of each feature is then tied to a particular coordinate, making it easy to classify the data. Lines called classifiers can be used to split the data and plot them on a graph.

A Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature.

Even if these features are related to each other, a Naive Bayes classifier would consider all of these properties independently when calculating the probability of a particular outcome.

A Naive Bayesian model is easy to build and useful for massive datasets. It's simple and is known to outperform even highly sophisticated classification methods.

This algorithm can be applied to both classification and regression problems. Apparently, within the Data Science industry, it's more widely used to solve classification problems. It’s a simple algorithm that stores all available cases and classifies any new cases by taking a majority vote of its k neighbors. The case is then assigned to the class with which it has the most in common. A distance function performs this measurement.

KNN can be easily understood by comparing it to real life. For example, if you want information about a person, it makes sense to talk to his or her friends and colleagues!

Things to consider before selecting KNN:

- KNN is computationally expensive
- Variables should be normalized, or else higher range variables can bias the algorithm
- Data still needs to be pre-processed.

It is an unsupervised algorithm that solves clustering problems. Data sets are classified into a particular number of clusters (let's call that number K) in such a way that all the data points within a cluster are homogenous and heterogeneous from the data in other clusters.

How K-means forms clusters:

- The K-means algorithm picks k number of points, called centroids, for each cluster.
- Each data point forms a cluster with the closest centroids, i.e., K clusters.
- It now creates new centroids based on the existing cluster members.
- With these new centroids, the closest distance for each data point is determined. This process is repeated until the centroids do not change.

A collective of decision trees is called a Random Forest. To classify a new object based on its attributes, each tree is classified, and the tree “votes” for that class. The forest chooses the classification having the most votes (over all the trees in the forest).

Each tree is planted & grown as follows:

- If the number of cases in the training set is N, then a sample of N cases is taken at random. This sample will be the training set for growing the tree.
- If there are M input variables, a number m<<M is specified such that at each node, m variables are selected at random out of the M, and the best split on this m is used to split the node. The value of m is held constant during this process.
- Each tree is grown to the most substantial extent possible. There is no pruning.

In today's world, vast amounts of data are being stored and analyzed by corporates, government agencies, and research organizations. As a data scientist, you know that this raw data contains a lot of information - the challenge is in identifying significant patterns and variables.

Dimensionality reduction algorithms like Decision Tree, Factor Analysis, Missing Value Ratio, and Random Forest can help you find relevant details.

These are boosting algorithms used when massive loads of data have to be handled to make predictions with high accuracy. Boosting is an ensemble learning algorithm that combines the predictive power of several base estimators to improve robustness.

In short, it combines multiple weak or average predictors to build a strong predictor. These boosting algorithms always work well in data science competitions like Kaggle, AV Hackathon, CrowdAnalytix. These are the most preferred machine learning algorithms today. Use them, along with Python and R Codes, to achieve accurate outcomes.

- Multilayer perceptron (MLP).
- Convolutional
**neural**network (CNN). - Recursive
**neural**network (RNN). - Recurrent
**neural**network (RNN). - Long short-term memory (LSTM) .
- Sequence-to-sequence models.
- Sequence-to-sequence models.
- Generative
adversarial network (GAN).

- Shallow
**neural**networks. - And new ones developing all the time .

Copyright © 2020 allusions.co.uk | Maldwyn Palmer