Hello and Welcome from 360DigiTMG. This is a video playlist to explain the life cycle of a data science project. In the previous video we talked about Data Preparation. Now let us discuss about the next phase in the data science lifecycle – Model Development.

Model Development: This is the core of a data science life cycle. When most people think about data science – they think algorithms, testing out various strategies, tools and techniques – which forms the basis of this phase.

In this phase, you select a machine learning algorithm that is appropriate for your problem and then train the ML model. As part of that training, you provide the algorithm with the training data to learn from and set the model parameters to optimize the training process.

Typically, a training algorithm computes several metrics, such as training error and prediction accuracy. These metrics help determine whether the model is learning well and will generalize well for making predictions on unseen data. Metrics reported by the algorithm depend on the business problem and on the ML technique that you used. For example, a classification algorithm can be measured by a confusion matrix that captures true or false positives and true or false negatives, while a regression algorithm can be measured by root mean square error (RMSE).
Settings that can be tuned to control the behavior of the ML algorithm and the resulting model architecture are referred to as hyperparameters. The number and type of hyperparameters in ML algorithms are specific to each model. Some examples of commonly used hyperparameters are: Learning Rate, Number of Epochs, Hidden Layers, Hidden Units, and Activation Functions. Hyperparameter tuning, or optimization, is the process of choosing the optimal model architecture.

Best Practices for model development:

• Generate a model testing plan before you train your model
• Have a clear understanding of the type of algorithm that you need to train
• Make sure that the training data is representative of your business problem
• Use managed services for your training deployments
• Apply incremental training or transfer learning strategies
• Stop training jobs early when the results, as measured by the objective metric, are not improving significantly to avoid overfitting and reduce cost
• Closely monitor your training metrics, because model performance may degrade over time

That concludes this video. In the next video we will talk about the next phase in the life cycle – Model Evaluation.


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360digiTMG is a 5-year-old training & consulting organization led by stalwarts of the industry who are alumnus of premier institutions like the Indian Institute of Technology, Indian Institute of Management and Indian School of Business. 360digiTMG since its inception has been the forerunner in the space of management and niche programs that aid in up-skilling and cross skilling executives across various levels and domains. 360digiTMG has been conducting training programs across the globe for corporate and individuals alike.
360DigiTMG is one stop solution to all the trainings in emerging technologies such as Artificial Intelligence, Machine Learning, Big Data, Project Management, Quality Management, etc. 360DigiTMG is a training company, which is a division of the analytics consulting firm Innodatatics Inc.

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