6 revolutionary things to know about Machine Learning

We are stepping into an avant-garde period, powered by advances in robotics, the adoption of smart home appliances, intelligent retail stores, self-driving car technology etc. Machine leaning is at the forefront of all these new-age technological advancements. The development of automated machines which have the capability match up to or maybe even surpass the human intelligence in the coming time. Machine learning is undoubtedly the next ‘big’ thing. And, it is believed that most of the future technologies will be hooked on to it.

Why is machine learning important?

Machine learning is given a lot of importance because it helps in prophesying behavior and spotting patterns that humans fail to predict. Machine learning has a myriad of very useful practical applications. Through Machine Learning, it’s possible to manage formerly baffling scenarios. After understanding the Machine Learning model with efficient generalization capabilities, it can be used to take important decisions accordingly. Machine Learning enables an individual to take a decision based on a host of scenarios. One can clearly cannot write a code that has the capability to manage all the new scenarios.

Artificial Intelligence is capable of performing various activities which require learning and judgment. From self-driven cars, investment banking, many healthcare-related functions and recruitment functions, AI is already being used to accomplish various tasks in different domains.

6 revolutionary lessons about machine learning

Machine learning algorithms are capable of finding out ways to execute necessary tasks simply by generalizing from scenarios. This is more practicable and cost-effective whereas, manual programming is not that cost effective and feasible. The increased amount of ‘data available’ is sure to give rise to more number of issues related to the data captured as well. Hence, machine learning is the thing of the future as it will be used widely in computer and other fields. Though, developing effective machine learning applications need a considerable amount of “black art” that is not that easy to find in manuals.

Listed below are the 6 most valuable lessons about machine learning:

1. Generalization is the core

One of the most basic features of machine learning is that the algorithm have to generalize from the data from training to the complete domain of all unseen scenarios in the field so that it can make correct extrapolations when you use the model. This process of generalization needs that the data that we utilize to train the model has a decent and dependable sample of the interpretations in the mapping we wish the algorithm to learn. The better the quality and the higher representative, the smoother it will be for the model to understand the unidentified and fundamental “true” mapping that subsists from inputs to outputs. Generalization is the act of moving from something precise to something broad.

Machine learning algorithm is the techniques of automatically simplify from historical scenarios. They have the capability to generalize on a higher amount of data and a faster rate.

The most general mistake that all the machine learning beginners generally make is to test on training data, and till have the impression of success. If the selected classifier is then tried on new data, it is commonly no better than random guesstimating. So, if you onboard someone to develop a classifier, make sure to keep a little bit of the data with you. Also, try and test the classifier that they give you on it.

2. Learning = Representation + Evaluation + Optimization

An ML algorithm is broken into 3 parts; representation, evaluation and optimization.

Representation: The data needs to be poured into an appropriate algorithmic form. For Text classification one may extract characteristics from your full-text inputs and mold them into a bag-of-words representation. Conversely, picking a representation is synonymous with choosing the set of classifiers that it can perhaps learn. This set is named as the hypothesis space of learner.

Evaluation: It is a metric that helps us understand what we are doing at the moment. An evaluation process is required to differentiate good classifiers from not so good ones. Say, if you are trying to predict a figure across a test say for example for a set of size n, here, you can calculate Mean Absolute Error = 1n∑ni=1|observedi−predictedi| or you may even choose to use the Root Mean Squared Error = 1n∑ni=1(observedi−predictedi)2−−−−−−−−−−−−−−−−−−−−−−−−−−√



Optimization: It refers to the process of finding out ways to select varied techniques to optimize it. For instance, we can simply try every hypothesis in our hypothesis space. Else, we may also choose to utilize a much more intelligent technique to try the most favorable hypothesis only. Also, as we are optimizing, we may utilize the evaluation function to understand if this specific hypothesis is good or not. Optimization technique allows the user to find out more about the classifier created if the evaluation function has got more than one optimum. Firstly, the beginners should start with off-the-shelf optimizers, and later on they can move to the custom-designed ones.

3. Data alone cannot do the job!

Generalization is the main purpose however, it has a major concern that only data is not enough, irrespective of the quantity. However, fortunately, functions we want to master are not drawn consistently from the bunch of all arithmetically possible functions! Even the most general assumptions, including, smoothness, similar examples having analogous classes, inadequate dependencies, or restricted complexity – are mostly enough to function well, and this is one of the main reasons that make machine learning so powerful. Basically all the beginners syndicate knowledge with Big data to produce programs.

4. Beware of Overfitting

We may end up just fantasizing a classifier if the data is not adequate, and is incapable of completely determining the apt classifier. This issue is termed as overfitting, and it is considered as a nuisance of ML. Noticing overfitting is beneficial, but it doesn’t resolve the issue. You have to find ways to get rid of it. Fortuitously, you have a plenty of options to try. Cross-validation helps to combat overfitting. Training with more data, regularization, removing features, early stopping, ensambling are some of the other methods to offload Overfitting.

5. Feature engineering is the key to success

Feature engineering is the technique of using core domain knowledge of the data to develop features that make ML algorithms work better. If it is done properly, it amplifies the predictive strength of the algorithms by developing features from raw data. These features simplify the complete machine learning process. Utilization of several independent features which nicely correlate with the class, then learning becomes easy.

6. Accuracy & Simplicity are different

Occam’s razor superbly states that objects should not be increased beyond the requirement. This means that of two classifiers have similar training error, the simpler of the two will probably have the nethermost test error. Every machine learning project should be initiated with a relentless aim on the business question that you wish to answer. You should start by formulating the main success principles for the analysis.

Applying Occam’s razor and selecting the model that is easiest to interpret, to elucidate, to deploy and manage are the key steps to take to build powerful machine learning programs. It is suggested to choose the simplest model that is adequately accurate, however, make sure that you know the issue deeply to know what “sufficiently accurate” implies in practice.

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