Data is the fuel that drives a business. Data-driven analytics help to decide whether an organization is keeping up with the competition or falling behind. In order to unlock the true value of corporate and customer data and make the best decisions, machine learning is the answer.
Machine Learning Process
There are five main steps in the machine learning process:
Fig: Machine learning process (source)
Step 1: Data Acquisition
The first step in the machine learning process is to get the data. This will depend on the type of data you are gathering and the source of data. This can be either static data from an existing database or real-time data from an IoT system or data from other repositories.
Step 2: Data Cleaning
All real-world data is often unorganized, redundant, or has missing elements. In order to feed data into the machine learning model, we need to first clean, prepare and manipulate the data. This is the most crucial step in the machine learning workflow and takes up the most time as well. Having clean data means that you can get a more accurate model down the road.
Data can be in any format - CSV, XML, JSON, etc. After cleaning the data, you need to then convert these data into valid formats that can be fed onto the machine learning platform. Finally, these datasets are further divided into training and testing datasets. The training dataset is used to train the model. The testing dataset is used to validate the model.
Here are some things to keep in mind while splitting the dataset into training and testing sets:
- The split range is usually 20% to 80% between the testing and training stages
- You cannot mix or reuse the same data for the testing and training dataset
- Using the same data for both datasets can result in a faulty model
Step 3: Model Training
The next step in the machine learning workflow is to train the model. A machine learning algorithm is used on the training dataset to train the model. This algorithm leverages mathematical modeling to learn and predict behaviors. These algorithms can fall into three broad categories - binary, classification, and regression.
Step 4: Model Testing
After the model is trained, we need to test and validate it for further processing. By using the testing dataset obtained from Step 3, we can check the accuracy of the model. If the results are not satisfactory, the model should be further improved. The model is trained and improved over and over again until the results are satisfactory.
Here are some things you can do to refine and improve the model:
- Review the model with the business stakeholders and take in their inputs
- Reconsider the algorithm you have chosen to train the model
- Adjust the parameters of the algorithm you have chosen (even small adjustments can have significant impacts)
Step 5: Deployment
Once the model is trained, deploy and pipeline it to production for application consumption.
The machine learning process that we have outlined here is a fairly standard process. As you go through this process on your own with your own problems, you will start to discover a few more machine learning steps that might work for you. For example, as you clean your data, you may find better questions to ask or feed the model. As you tune your model, you may realize you need more data, and so on. The important part is to keep iterating until you find a model that fits your project the most.
Machine Learning Approaches
Machine learning has two main types of approaches - supervised learning and unsupervised learning.
Supervised machine learning trains a model on known input and output data so that future outputs can be predicted. Once the model is trained using known data, you can use unknown data in the future and predict the responses.
Here is the list of top algorithms currently being used for supervised learning:
- K-nearest neighbors
- Linear regression
- Logistic regression
- Naive Bayes
- Polynomial regression
- Random forest
- Decision trees
In unsupervised learning, the data used to train the model is unknown and unlabeled. This means that the data has never been worked on before. It is mostly used to find hidden patterns or structures in the data.
Here is the list of top algorithms currently being used for unsupervised learning:
- Principal component analysis
- Fuzzy means
- Partial least squares
- Singular value decomposition
- K-means clustering
- Hierarchical clustering
Which Algorithm to Choose?
There are so many algorithms out there and choosing the right one can seem overwhelming at times. There is no one size that fits all and finding the best algorithm is partly a trial and error method. However, the algorithm selection does depend on the type and size of the datasets and the insights you want to derive from the data.
Here are some guidelines on choosing between supervised and unsupervised machine learning:
- Supervised learning algorithms can be used if you want to train a model to make a prediction or a classification. For example, identifying cars from web footage, predicting stock prices, etc.
- Unsupervised learning algorithms can be used if you want to explore the data that you have and find a good internal representation. For example, splitting a dataset into clusters.
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What Can You Do Next?
Machine learning is a highly interactive process that learns from past experiences. The thing with the machine learning process is that it is all about asking the right questions. After that, you need the right data to answer the questions and then begin the testing iterations until you get the desired model. In order to become a machine learning expert, you need to be trained in all of these steps. If you are interested to learn more about machine learning, Simplilearn’s Machine Learning Certification Course will provide you with all the skills required to become a machine learning engineer. This program contains 58 hrs of applied learning, interactive labs, 4 hands-on projects, and mentoring. Get started with this course today to ensure your success in this field.