Machine learning an integral component of Artificial Intelligence algorithms has reshaped businesses and our life’s for quite some time for now! Be it be from opening your phone by facial recognition to the more complex recommender algorithms which assists you what you will watch or shop next, machine learning is making quite a noise for now. In simple words, machine learning is defined as making machines learn to initiate human actions, through complex coding done in Python, R, C, C#, Java and so on.
There is nothing like a perfect machine learning roadmap, a path which is filled with trial and error. Data Scientists, Data Analysts who are ML experts constantly tweak and alter their algorithms and models for the desired accuracy. Challenges do arise during this process ranging from building data pipelines, determining data ownership to choosing the right model, and zeroing on the desired accuracy levels.
The Steps to Build a Machine Learning Model
When building a machine learning model, the first step is to acknowledge that real-world data is imperfect, requiring different approaches and tools, and trade-offs are common when determining the right model. Here are a few common steps adhered to while a team of Data Analyst, Data Scientist and Machine Learning expert build an ML model-
1. Data Collection
This process involves the collection of data that originates from different sources both structured and unstructured. The speed at which modern data originates is also described by the term Big Data.
2. Data Storage
This process involves data analysts storing data to easily archive, manage, and protect the valuable data for future business use. To meet the modern business needs, data storage is available for storage on AI & Big Data workloads on cloud premises.
3. Data Transformation
Data transformation is the process of data conversion from one format or structure into another format or structure. Data Transformation tasks integrate data wrangling, data integration, application integration and data warehousing. Data Science performs Data transformation, a key step in ETL or ELT data integration.
4. Data Labelling
Data Labelling is an indispensable stage of data pre-processing in supervised learning. Data labelling brings together data classification, moderation, transcription, processing, annotation and data tagging.
5. Model Building
Data scientists build a model or set of models, to address the business problem. Easiest and popular classification model building algorithms include the decision tree classification based on features characteristics. K-Nearest Neighbour classification is another Machine Learning algorithms based on Supervised Learning technique that compares new points to the training data and returns the most frequent class of the “K” nearest points. Another option that data scientists may deploy is the multiclass support vector machine (SVM) to build stronger and powerful machine learning models.
6. Model Training
This process involves training the model by passing it through different data inputs. The key aim here is to maximize model performance while safeguarding against overfitting. Data Scientists have separate training and test subsets of dataset usually divided in the ratio of 80:20 or 70:30. The key is if the model performs well on the training data but poorly on the test data, then it is an overfit. In this case, data scientists go back to step #5.
7. Model Assessment
Model validation and assessment during training is an important step evaluating different metrics for determining if a data scientist has a winning supervised machine learning model. Model assessment is a critical step in practice, since it guides the choice of learning method or model, and gives a performance measure of the quality of the ultimately chosen model.
8. Model Accuracy Improvement
The accuracy of an ML model depends on data chosen, feature selection, and the choice taken while deciding on ML algorithms while building the supervised learning model. Machine Learning experts improve the model accuracy by feature engineering, feature selection, algorithm tuning and ensemble methods deploying bagging and boosting.
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