Nus Machine learning refers to creating pieces of software that learn and improve overtime. In certain cases, this process can be used to guarantee software creators other pieces of software which themselves can complete key tasks. In this guide, we’ll explore the seven steps required to explore machine learning for the first time.
1. Collecting Data
During machine learning, software learns based on the data you provide. That’s why the first critical step is to gather data which is valuable to the learning process. The best data pools will mean your software can discover patterns. The quality of your data source will always impact your overall model accuracy.
2. Preparing The Data
Once you have collected your data, the next step is to prepare it. You can prepare the data by putting it together and then randomizing it. The objective is to guarantee your data is evenly distributed.
You should also remove any unwanted data including duplicate values and visualize the data source to understand the structure.
3. Choosing Your Model
Once you have prepared the data, you now need to choose a model. It is vital that you choose a model which matches your tasks or objectives. Researchers have developed a long list of models which may be suitable. There are a lot of factors to consider. For instance, you need to think whether the model is designed for numerical or categorical data.
4. Train Your Model
Arguably, training your model is the most important step when exploring machining learning. To do this, you’ll need to pass the data through your model to find predictions and key patterns. The model will then ‘learn’ from the data provided and theoretically accomplish the desired task.
5. Evaluate Your Model
Once you have trained your model, you then need to evaluate it. To do this, you should run the model on data that has not been seen or used before. Using the model on test data will provide an accurate measurement of your model speed as well as how it will perform.
6. Parameter Tuning
After you have evaluated the model, you should then check if you can make any improvements. Parameter tuning refers to altering the variables in the model decided upon by the programmer. Adjusting a parameter to a specific value will result in the maximum possible accuracy in your machine learning model.
7. Don’t Overthink Your Data Models
Finally, you can start using your data in the real world based on the model that you have selected. However, you should continue to test your model to maintain a high level of accuracy and guarantee the validity of your results.
Conclusion
While machine learning may seem like a complex progress, breaking it down into these key steps makes it far easier to understand and dissect. In doing so, you will soon be able to start using these models to make accurate predictions that can have a tremendous impact on your business.
If you are eager to begin this journey, we recommend exploring some of the best tools available online. There are plenty to choose from. For instance, if you need basics in coding then W3Schools offers a solid starting point. This provides a platform where users can learn through trial and error.
Alternatively, you can jump right into creating models with Microsoft Azure. Microsoft Azure Machine Learning is a cloud platform which will allow you to instantly start building, training and deploying AI models. There are also constant upgrades which make the software more user-friendly for newcomers.
