Data Science and machine learning
Data Science and machine learning
Artificial intelligence is the education and expansion by which a computer and its systems are given the ability to successfully performs tasks that would characteristically require a human’s intelligent performance. Machine learning is a part of the process of providing the ability to computer work intelligently, by coding etc. It’s the technology and process by which we train the computer to accomplish the said task.
Machine learning techniques are evolving and continuously exploring new things. Some models for training a computer are already recognized and used. The idea to be recalled here is that different models can be used when training a computer. Different business problems require different models.
For a workstation to accomplish a task with AI, it needs put into practice and variation. A machine learning model needs to be trained using information, which can be in any form and, in most cases, with a little human help. As more records is swallowed by the system, the more perfectly the computer can return to it, in simple words, the you’re your system is fed the more it will perform accurately. More accuracy in understanding the data means a better chance to successfully accomplish its given task or to increase its degree of confidence when providing predictive insight.
Quick example:
- Entry data is chosen and prepared along with input conditions (e.g. credit card transactions).
- The machine learning algorithm is built and trained to accomplish a specific task (e.g. detect fraudulent transactions).
- The training data is augmented with the desired output information (e.g. these transactions appear fraudulent, these do not).
Let’s look at the training process itself to better understand how machine learning can create value with data.
- Collect: To make the Machine learn, it requires the detailed and relevant information to be fed to the system. Collecting the knowledge from different sources, in any form is the very first step of Machine Learning process.
- Clean: Data can be engendered by different sources, contained in different file formats, and expressed in different languages. It might be required to add or remove information from your data set, as some instances might be missing information while others might contain undesired or irrelevant entries. Wiping off the unwanted information and keeping the required information is done in this step of cleaning. There are algorithms to perform the cleaning process done. Its preparation will impact its usability and the reliability of the outcome.
- Split: Contingent on the size of the data set, only a portion might be required. As the name suggests split, in this point of the process, segregating the groups that re suitable for future use. From the chosen sample, data should be split into two groups: one to train the algorithm and the other to calculate it.
- Train: This stage fundamentally aims at discovering the statistical function that will accurately accomplish the chosen goal. Training takes on different forms depending on the type of model used. Fitting a line in a simple linear regression model can be seen as training; generating the decision trees for a Random Forest Algorithm is also training; changing the questions in a decision tree is effectively adjusting the parameters of the model.
- Evaluate: Once the algorithm performs well on the training data, its presentation is measured again with data that it has not yet seen. Additional adjustments are made when needed. This process allows you to prevent overfitting, which happens when the learning algorithm performs well but only with your training data.
- Optimize: The model is optimized for integration within the destined application to ensure it is as lightweight and as fast as possible.
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