Machine learning (ML) has taken on a life of its own in recent years. As businesses leaders, marketing teams, and economists discover entire new applications for the technology.
As a result, diving into this level of data science requires a firm understanding of the meaning of machine learning. This includes some basic concepts and terminology.
What Is Machine Learning?
At a high level, machine learning is an extension of artificial intelligence (AI). Because of this, this is based on the premise that computers and software models can learn from iterations of data occurrences and decision models.
Furthermore, have you had the experience of shopping online and found that the web site suggested additional purchases that you may be interested in? Especially relevant, this is one example of the application of machine learning. Programs utilizing ML algorithms have learned from experience and behavior. This shows what traits can be identified for recommending other products and services.
Image source: https://www.xenonstack.com/blog/log-analytics-with-deep-learning-and-machine-learning
Taking ML a significant step further, you find the advent of self-driving cars. This is a technology that continuously ‘learn’ from data gathered as they encounter new surroundings or situations.
As a result, machine learning is a powerful tool that even with basic, simple algorithms can perform. Along with sophisticated predictive analysis from complex sets of data.
Terminology of Machine Learning and Data Sciences
There are several common terms that apply to machine learning. In which every course or book on the subject will delve into in great detail:
Supervised learning – programming that includes “training” of decisions based on a pre-defined set of data, such that decisions can be made accurately when new data is introduced.
Unsupervised learning – characterized by the ability of a program to automatically detect patterns and/or relationships in datasets.
Classification – assignment of a label based on data input, typically yes/no determination.
Regression – differs from classification as cases where a value is not recognized as yes/no, but a value such as “how many” or “how much”.
Generative model – model utilized to generate data values in cases where some parameters may be hidden. This can serve as a step in conditionally formulating a probability density function.
Discriminative model – models the dependence of one variable to another. Also referred to as conditional models.
Deep learning – a technology that applies complex layered machine learning algorithms, often utilizing artificial neural networks for generation of models. Deep learning has proven to be effective particularly where datasets are very large, with the targeted function being complex.
Getting Started with Machine Learning Projects
Furthermore, to get the most out of your work with ML projects, there are several considerations that apply to meaningful progress and benefits from your efforts:
A project of Interest
This ensures that your will understand the initial data as well as the results generated from the project. Initially, stick with a topic you’re either familiar with, or want to become knowledgeable about for potential career positions.
Repository
The UCI Machine Learning Repository provides a wealth of datasets that can be utilized in selecting a project case that interests you. Therefore, pick from topics that cover everything from commercial films to wine selection to health issues and potential treatments. Other topics include sports, current events, and weather. A quick web search will reveal many other sources for ML projects and datasets.
Education
Education is, of course, critical to beginning your adventure into ML projects. Therefore, Python has become the preferred programming language for development of data science functionality. Many excellent courses and texts have been created relevant to ML Python programming. Developer forums are available to exchange techniques that are efficient and time-proven.
Keep it Simple
Don’t re-invent the wheel. As ML has matured, many base algorithms and routines have been shared on public web sites that can be repurposed for your project. Even if some refinement may be needed for your specific dataset or project purposes.
For the Beginner
Just starting out? For a ML beginner, it’s important to reign in your initial projects to a limited set of datatypes, attributes, and attribute types. This will allow a gradual introduction into the variations and models that you will encounter as you progress into a broader scope of problems and solutions.
Let’s be Real
Keep your expectations realistic. Don’t try to absorb every technical nuance of machine learning before getting started with a project or two. It’s better to start applying what you’ve learned with a real-life project. Take a deeper dive into additional aspects as you develop your skills and understanding of data science.
Applying Machine Learning Projects
Image source: https://www.udemy.com/machinelearning/
The basis to understanding machine learning coding and benefits is to get started with hands-on projects that give you experience with the technology, pitfalls, and methodology.
Projects are essential for several reasons:
- Real-world understanding of the technology. It’s one thing to read a book or two on a given subject, and quite another to apply it in practice.
- Create a useful application that can benefit businesses or that you can apply to your own personal research or decisions.
- Honing your skills in programming and data sciences. Adding to your portfolio or resume for career advancement or entirely new positions.
What Makes Machine Learning Projects a Success?
Every machine learning project is successful. Knowledge related to how the science and programming technology can be applied to business problems, economics, and decision sciences.
Programming skills acquired through the execution of these projects also contribute to an understanding of business needs, algorithmic principals, and coding best practices. Projects may even open the door to exciting career opportunities in a growing market.
The future appears bright for skilled technologists well-versed in ML technology. Data-intensive applications such as voice recognition, analysis of social networking activity, and predictive analysis are just a few of the areas that will continue to benefit from future development in data science and ML.
