By Manikanta Sai Teja
Machine learning (ML) is the study of computer algorithms that improve automatically through experience. It is seen as a subset of Artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop conventional algorithms to perform the needed tasks.
Machine Learning Types:
The computer is presented with example inputs and their desired outputs, given by a “teacher”, and the goal is to learn a general rule that maps inputs to outputs.
No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end .
A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle or playing a game against an opponent). As it navigates its problem space, the program is provided feedback that’s analogous to rewards, which it tries to maximise.
Best language for Machine Learning?
R is a workhorse for statistical analysis and by extension machine learning. Much talk is given to the learning curve, I didn’t really see the problem. It is the platform to use to understand and explore your data using statistical methods and graphs. It has an enormous number of machine learning algorithms, and advanced implementations too written by the developers of the algorithm.
I think you can explore, model and prototype with R. I think it suits one-off projects with an artifact like a set of predictions, report or research paper. For example, it is the most popular platform for machine Learning competitors such as kaggle.
Python if a popular scientific language and a rising star for machine learning. I’d be surprised if it can take the data analysis mantle from R, but matrix handling in NumPy may challenge MATLAB and communication tools like ipython are very attractive and a step into the future of reproducibility.
I think the SciPy stack for machine learning and data analysis can be used for one-off projects (like papers), and frameworks like Scikit-Learn are mature enough to be used in production systems.
Implementing a system that uses machine learning is an engineering challenge like any other. You need good design and developed requirements. Machine learning is algorithms, not magic. When it comes to serious production implementations, you need a robust library or you customize an implementation of the algorithm for your needs.
Python is the best language for a reason:
“Python” comes with an assortment of excellent libraries and tools for ML, including Scikit Learn, TensorFlow, ChatterBot, and much more. One of the oldest programming languages, C++ is highly suited for Machine Learning, thanks to its ML repositories like TensorFlow, LightGBM, and Turi Create.
Automation for everything;
A very powerful utility of Machine Learning is its ability to automate various decision-making tasks. This frees up a lot of time for developers to use their time to more productive use. For example, some common use we see in our daily life is social media sentiment analysis and chatbots. The moment a negative tweet is made related to a product or service of a Company, a chatbot instantly replies as first-level customer support. Machine Learning is changing the world with its automation for almost everything we can think of.
Trend’s and patterns identification;
This advantage is a no brainer. All of us interested in Machine Learning technology are well aware of how the various Supervised, Unsupervised and Reinforced learning algorithms can be used for various classification and regression problems. We identify various trends and patterns with a huge amount of data using this technology. For example, Amazon analyzes the buying patterns and search trends of its customers and predicts products for them using Machine Learning algorithms.
Wide range of applications;
Machine Learning is used in every industry these days, for example from Defence to Education. Companies generate profits, cut costs, automate, predict the future, analyze trends and patterns from the past data, and many more. Applications like GPS Tracking for traffic, Email spam filtering, text prediction, spell check and correction, etc are a few used widely these days. Machine Learning is a branch of Artificial Intelligence, the latest trends and applications can be found in Artificial Intelligence Trends in 2020.
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