Machine Learning (ML) Model Training

‍Machine Learning (ML) models are critical for everything, from marketing, finance, healthcare, and retail to more specific use cases like self-driving cars. Nowadays, a few companies have not been affected by the Machine Learning revolution, which has ultimately changed how industries operate and run their decision process. But how can you train a Machine Learning model? In this blog post, you’ll get an introduction to the first steps to start model training your Machine Learning project. Don't wait any longer; it's time to start!

What is a Machine Learning Model?

Machine Learning Models are Artificial Intelligence (AI) systems used to recognize patterns in data and make predictions by training algorithms. In other words, they are mathematical representations that provide outcomes based on a training process that, once prepared, can learn from new data.  

Before diving into the steps of model training, we must first understand the different Machine Learning techniques. First, we can start with Unsupervised Learning, which contemplates several sub-divisions: the hierarchical clustering algorithm, association rule, and dimensionality reduction. In the second instance, we can mention Unsupervised Learning, which integrates the polynomial regression model and classification model. Finally, Reinforcement Learning is the last technique that does not present subdivisions.

Data Scientists can use different technologies for training methods, like Azure Machine Learning and Amazon Machine Learning. Additionally, they can follow different model architecture types like DeepLabV3 architecture, Change Detector architecture, and DETReg architecture.

How To Train a Machine Learning (ML) Model?

1. Collecting Data

Collecting data is a crucial step in building a successful Machine Learning model. The model's accuracy heavily relies on the data quality used to train it. There are several mini-steps involved in this process to ensure data quality. Firstly, clearly define the problem you want to address to determine the type of data you need to collect. Once you understand the problem, you can collect data from different sources, such as:

● Scraping: Beautiful Soup, Scrapy, and Selenium.
● APIs: You can access Application Programming Interfaces (APIs) data from different websites.
● Databases: SQL (like MySQL and PostgreSQL) and NoSQL databases (like MongoDB and Cassandra).
● Surveys and Forms: Tools like Google Forms or SurveyMonkey.
● Data Collection Libraries in Programming Languages: Libraries in Python (like pandas) and R (like dplyr).
● Pre-existing Datasets: Websites like Kaggle, UCI Machine Learning Repository, and Google Dataset Search. ‍

2. Preparing the Data

This stage starts by combining all the data to ensure even distribution and that its order doesn't impact the learning process. Then, you can perform the classification task for removing or correcting erroneous data, eliminating duplicates, dealing with missing values, and stratifying object categories. Afterward, you'll need to choose a format for training data, making it easily understandable by the Machine Learning model.

Lastly, you'll need to split the dataset into three sets: training set, validation set (optional), and test set. You will use the training set to train the model, the validation dataset for tuning parameters, and the test set to evaluate the model's performance. Remember that the better the data quality, the more relevant your Machine Learning Model will become, leading to more accurate predictions. Tools like Pandas, NumPy, Scikit-learn, DataPrep, Keras, TensorFlow Data Validation, and TensorFlow Transform will be useful during this stage. ‍

3. Choosing a Model

It's essential to consider the model’s assumptions; for instance, linear regression assumes a linear relationship between the input dataset and output variables. Another important factor to consider is the model's complexity. Complex models, such as Deep Learning Models, can capture more complex relationships in the data but require more data and computational resources to train. Simpler models, like linear regression or Decision Trees, may be more appropriate if you have limited data or resources. Choosing the right model is a complex decision that requires careful deliberation. In the next image, you can appreciate other factors to consider at this stage.

4. Training the Model

At this phase, the primary objective is to transmit and feed all the relevant data to the Machine Learning Algorithm to identify patterns and make accurate predictions. The data is thoroughly analyzed to ensure the model learns everything required to meet the established usability objectives. The model will gradually evolve, providing the system with more advanced features to predict outcomes. 

If you want to save time, you can use transfer learning, which allows you to transfer information from one model to another. It's key to strengthen Machine Learning models by cutting a lot of work. This stage requires meticulous attention to detail to ensure the model has the highest possible standard. Any slight deviation or error in the data fed to the model can lead to incorrect predictions, which could have significant implications. This phase will ensure the model is at its full potential when executed correctly. ‍

5. Evaluating the Model

Once you've built the model, you should test it using new and unseen data. Using the same data as used in the initial training process could result in inaccurate results. The model is already familiar with the training data and will find the same patterns as before, leading to disproportionately high precision. Therefore, testing the model using new data is necessary to ensure accuracy. It helps identify potential issues and make necessary adjustments before deploying it in the real world. 

Within this stage, you can use tools like Statsmodels, a Python module that provides functions for estimating different statistical models and conducting statistical tests and data exploration, or the open-source platform MLflow for managing the end-to-end Machine Learning lifecycle, which includes tools for tracking experiments, packaging code into reproducible runs, and sharing and deploying models.

6. Parameter Tuning

You can achieve a well-tuned model by adjusting its parameters, essentially the variables you have decided to use. Finding the specific values for these parameters while prioritizing accuracy is called parameter tuning. The goal of tuning a model is to boost its performance. It usually gets done through hyperparameter values, where the model is run multiple times with different default values and environment configurations in an iterative process.

Tools like GridSearchCV and RandomizedSearchCV in Scikit-learn can automate this process, cross-validating as it goes to determine the best performance. Use advanced methods like Bayesian Optimization, Gradient-based Optimization, and Evolutionary Algorithms for hyperparameter tuning. The ultimate goal is to balance bias and variance in the model to reach the F-beta score (value where outcomes don't change significantly).

7. Making Predictions‍

Making correct predictions based on new data is extremely difficult, especially for complex training models like Deep Neural Networks. So, if you achieve this stage, congratulations! However, training is a never-ending process, and the model must be up to date with new technologies and trends to offer an optimal level of usability according to your business requirements.

‍Final Thoughts

‍Consider the amount of data, elements, algorithms, and needed knowledge of Machine Learning to know where to start. Here, you have seven general detailed steps explaining how to succeed in the training process. Remember, once you reach the expected levels of precision, monitoring key performance metrics, the model's accuracy, and adjusting the training strategy are ongoing and much-needed processes to maintain model performance. So, if you want to know more about Machine Learning Models, how to create one, and all its processes, don’t hesitate to contact us.