Machine learning is a growing component of data science related to artificial intelligence. With machine learning, computer systems can perform tasks, identify patterns, and make predictions. Essentially, you’re programming the computer to learn from its past experiences. Computer algorithms can access and classify data based on a specific model. The programs learn from the input that is provided and make predictions based on the data. Approaches to machine learning include supervised, unsupervised, and reinforced learning. Supervised learning uses labeled data to train the model, whereas unsupervised learning uses unlabeled data. Reinforcement learning works using a reward and feedback model.
A recent study shows that 76 percent of organizations surveyed are placing machine learning higher on the list of priorities than other IT areas. This same study showed that 83 percent of companies have increased their budgets for machine learning. Why are businesses so focused on this initiative? Improving customer service was one of the main reasons cited in the survey. Detecting fraud, managing inventory, and reducing expenses were also high on the list. Machine learning can be used by businesses in a multitude of ways to increase revenue and cut costs.
All types of industries use machine learning, from the financial sector to agriculture. Machine learning can be used in the medical field to assist with diagnosing. Banking and investment companies use it for stock training, insurance purposes, and fraud detection. It is frequently used to provide personalized product suggestions, for example with Amazon or Netflix recommendations. Online chatbots use machine learning to provide customer service and programs like Siri use it for speech recognition. Uber uses differential pricing in real-time based on machine learning. Games, robotics, and even self-driving cars are other uses of machine learning. Having the ability to work with different machine learning languages will give you an advantage when it comes to finding work in the development field.
Great for beginner programmers, Python is quick to learn and easy to understand. It is an open-source language so there are plenty of resources to learn from. Python is the most widely used language by developers. It is often described as being more intuitive than other languages and it can be used for a variety of different applications. Python has a broad assortment of libraries for machine learning including TensorFlow, Pandas, and Scikit-learn. Many companies choose to use Python because of its simplicity and breadth of functionalities. Amazon, Apple, Google, Facebook, and Netflix are examples of companies that use Python for machine learning. Python is the world’s fastest-growing programming language. It has a very straightforward syntax and is widely used for back-end web development and scripting. Almost half of all developers use Python at work and the popularity of this machine learning language is increasing rapidly.
Most useful for exploratory work, statistics, and data analysis, R programming has an extensive set of techniques for model evaluation, visualization, and sampling. This machine learning language can be slow and challenging to learn but can be helpful when dealing with large data sets. Many companies use R programming, so this can be a useful machine learning language to know. This program is designed for numerical computations and is open-sourced. It is comprehensive and is considered by some developers to be the future of programming languages.
Easy to use, Julia was created especially for data analytics and statistical tasks. This makes it a great choice for a machine learning language. While Julia is a relatively new programming language, it is gaining in popularity in recent years, especially in the data science industry. It is free, open-source, and extremely fast. Julia has both object-oriented and functional programming. Since Julia is newer than some of the more established machine learning languages, it has a smaller support community. However, it is anticipated that the demand for Julia will continue to grow.
C++ is one of the best performing, fastest programming languages. It is a low-level language with a variety of applications. The use of this language has stayed relatively flat over the past two years. It is one of the most flexible machine learning languages and it has a vast selection of libraries. The execution speed of C++ is very fast and the structure of the program is well-defined. There can be a steep learning curve with C++ and it is harder to access libraries than with some of the other machine learning languages. Some companies that use C++ include Adobe, Linkedin, Mozilla, and Tesla.
Machine learning is a great skill for developers to have and is increasingly in demand by employers. With the vast array of machine learning languages available, there are many factors to consider when choosing which one to learn. Think about what projects you will be using the language for, as well as what your existing programming knowledge is, and what field you are working in. Having a clear idea of how you will be utilizing machine learning will help you choose the best language to work with.