Before we venture off on our journey to improvise what is probably the biggest field of study, research, and development, it is only apt and fitting that we understand it first, even if at a very basic level.
So, just to provide a very brief overview for understanding, Machine Learning or ML for short is one of the hottest and the most trending technologies in the world at the moment, which is actually derived from and works as a subsidiary application of the field of Artificial Intelligence. It involves making use of abundant pieces of discrete datasets in order to make the power systems and computers of today sophisticated enough to understand and act the way humans do. The dataset that we give to it as the training model works on various underlying algorithms in order to make computers even more intelligent than they already are and help them to do things in a human way: by learning from past behaviors.
Challenges in creating Machine Learning Models:
Quality data: Many people and programmers often take the wrong step in this crucial juncture thinking that the quality of the data would not affect the program much. Sure, it would not affect the program but will be the key factor in determining the accuracy of the same.
Scalability: Absolutely no ML program/project worth its salt in the entire world can be wrapped up in a single go. As technology and the world change, day by day so does the data of the same world change at torrid paces. Which is why the need to increase/decrease the capacity of the machine in terms of its size and scale is highly imperative.
Model designing: The final model that has to be designed at the end of the project is the final piece in the jigsaw, which means there cannot be any redundancies in it. But many times it happens that the ultimate model nowhere pertains to the ultimate need and aim of the project.
When we talk or think of Machine Learning, we should keep in mind that the learning part of it is the deciding factor which is done by humans only. So here are some things to keep in mind in order to make this learning part more efficient:
Choose the right data set: one that pertains and sticks to your needs and does not wander off from that course in high magnitudes. Say, for example, your model needs images of human faces, but rather your data set is more of an assorted set of various body parts. It will only lead to poor results in the end.
Make sure that your device/workstation is devoid of any pre-existing bias which would be impossible for any kind of math/statistics to catch. Say, for example, a system contains a scale that has been trained to round-off a number to its nearest hundred. In the event your model contains precise calculations where even a single decimal digit would cause high fluctuations, it would be highly troublesome. Test the model on various devices before proceeding.
The processing of data is a machine process, but creating its dataset is a human process. And as such, some amount of human bias can consciously or unconsciously be blended into it. So, while creating large datasets, it is important that one try and keep in mind of all the possible setups possible in the said dataset.
Being one of the most sought after skill set in the current market and industry scenario, the need and importance for ML experts and professionals is at an all-time high and only bound to increase in the coming years. Join data science training in Bangalore ASAP and reap its benefits.
Machine learning (ML) algorithms allow computers to define and apply rules which were not described explicitly by the developer. There are quite a lot of articles devoted to machine learning algorithms. Here is an attempt to make a "helicopter view" description of how these algorithms are applied in different business areas. This list is not an exhaustive list of course. The first point is that ML algorithms can assist people by helping them to find patterns or dependencies, which are not visible by a human.
Numeric forecasting seems to be the most well-known area here. For a long time computers were actively used for predicting the behavior of financial markets. Most models were developed before the 1980s when financial markets got access to sufficient computational power. Later these technologies spread to other industries. Since computing power is cheap now, it can be used by even small companies for all kinds of forecasting, such as traffic (people, cars, users), sales forecasting and more.
Anomaly detection algorithms help people scan lots of data and identify which cases should be checked as anomalies. In finance, they can identify fraudulent transactions. In infrastructure monitoring, they make it possible to identify problems before they affect business. It is used in manufacturing quality control.
The main idea here is that you should not describe each type of anomaly. You give a big list of different known cases (a learning set) to the system and system use it for anomaly identifying.
Object clustering algorithms allow grouping a big amount of data using a wide range of meaningful criteria. A man can't operate efficiently with more than few hundreds of object with many parameters. The machine can do clustering more efficient, for example, for customers/leads qualification, product lists segmentation, customer support cases classification etc.
Behavior prediction algorithms give us the opportunity to be more efficient interacting with customers or users by offering them exactly what they need, even if they have not thought about it before. Recommendation systems works really bad in most of the services now, but this sector will be improved rapidly very soon.
The second point is that machine learning algorithms can replace people. The system makes an analysis of people's action, build rules basing on this information and apply this rules acting instead of people.
This is about all types of standard decisions making. There are a lot of activities which require standard actions in standard situations. People make some "standard decisions" and escalate cases which are not standard. There are no reasons, why machines can't do that: documents processing, cold calls, bookkeeping, first line customer support etc.
And again, the main feature here is that ML does not require explicit rules definition. It "learns" from cases, which are already resolved by people during their work, and it makes the learning process cheaper. Such systems will save a lot of money for business owners, but many people will lose their job.
Another fruitful area is all kinds of data harvesting. Google knows a lot. But when you need to get some aggregated structured information from the web, you still need to attract a human to do that. Information aggregation, structuring, and cross-validation, based on your preferences and requirements, will be automated thanks to ML. Qualitative analysis of information will still be made by people.
Finally, all these approaches can be used in almost any industry. We should take it into account when predicting the future of some markets and of our society in general.