Machine Learning For A Startup: 10 Things You Should Know

Machine Learning is considered to be an invention in Artificial Intelligence that allows the computer-based systems to learn from its experience instead of being programmed with every decision it should make for every condition. It’s being implemented in many algorithms such as facial, speech, and object recognition, natural language processing and predictive analytics.

Listed below are 10 things you should know about Machine Learning for startups:

1. Are Machine Learning and Data Mining, the same thing?

Not at all. There is a huge confusion between ML and Data Mining, though they have many similarities ML has a lot of statistical tools and a good knowledge of mathematics is involved in it. The main differentiation between the two is that data mining is all about penetrating down into a dataset to find useful information that will further help any organization when analyzing the data. Whereas Machine Learning is about utilizing that useful information to work out how to predict future outcomes which then helps to train a machine to perform a set of tasks. You can learn more about it by taking machine learning training.

2. Machine Learning roams around data and algorithms.

In Machine Learning, the data is the most important or you can say a fundamental element that makes Machine Learning achievable in real life. You can practice Machine Learning without complex algorithms, but not without good and efficient data.

3. Do not use a complex model/algorithm unnecessary.

By analyzing the patterns and exploring your data, ML trains the model. If your data exploration is too huge, you’ll overfit to your training data and train a model that doesn’t generalize beyond it. So its no use to have a complex model, you should always keep your models as easy as possible.

4. ML model will depend on the data you provide.

Whatever data you provide to your machine, the quality of the model will be based on your data only. Machine learning can only recognize patterns that are being in your training data. For supervised machine learning assignments like classification, you’ll need a strong collection of accurately labeled, well-featured training data.

5. ML model works on illustrative data.

While building a model, the training set is being considered to have a unique set of labeled data that provides known data that is used in supervised learning to establish a classification or regression model. When a model is constructed, we need to examine it in order to compare it with another model. So, the past performance is no guarantee of future outcomes as it is only guaranteed to work for the data generated by the same distribution of the test and train dataset.

Be careful of changes between training data and production data, and retrain your models periodically so they don’t become old and keep them updated. This indicates that the Machine Learning model needs a representative or illustrative data.

6. Data transformation is the most laborious task in Machine Learning.

Machine Learning is not only about selecting and tuning algorithms. The main work while creating a Machine Learning model is the data cleansing and feature engineering in which we transform the raw features/data from the dataset into features that have a better representation of the signal in your data.

7. Machine Learning is not Artificial Intelligence.

The fact is that Machine Learning is the subset of Artificial Intelligence and it utilizes the experience that the model has so that it can look for a specific pattern that the model learned from the past training whereas Artificial Intelligence uses the experience and the AI algorithm knows how to apply that knowledge for new situations.

8. Machine Learning is not a threat to human society.

In today’s world, most of the AI projects are based on Machine Learning that solely helps to recognize patterns within the datasets and that algorithm tries to make predictions based on the existing data. AI is not about recreating the human brain. It is roughly about developing a system that functions or operates like a human.

9. Machine Learning totally depends on humans.

ML is all about pattern recognition from the data that computers can learn without being programmed to achieve some specific tasks. They learn from past estimations to produce stable, repeatable conclusions and outcomes. When the machine learning model fails, it is because of the machine learning algorithm like human error into the training data, creating bias or some other systematic error which has been created by a human. Thus it totally depends on the human.

10. Machine Learning is all around us.

Currently, one of the most extensively used AI techniques for the advancement of new applications is known as Machine Learning. What ML tries to do is to present as much data as possible to algorithms that the ML developers create which then enables the model to improve the experience and make recommendations automatically.

In today’s world the tech-based companies like Google, Microsoft, Apple and many more have consolidated smart systems into their products and are directing toward a future where there will be automated real-time interpretation during a call, suggestions of what you want to purchase online, or the identification of your voice while communicating with your mobile phone.

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