Accenture Machine Learning Engineer Interview Questions, Process, and Tips

Bias is error due to overly simplistic assumptions, variance is error due to overly complex models.

• Bias is error introduced by approximating a real-world problem, leading to underfitting.

• Variance is error introduced by modeling the noise in the training data, leading to overfitting.

• High bias can cause a model to miss relevant relationships between features and target variable.

• High variance can cause a model to be overl...read more

Learning rate is a hyperparameter that controls how much we are adjusting the weights of our network with respect to the loss gradient.

• Learning rate determines the size of the steps taken during optimization.

• A high learning rate can cause the model to converge too quickly and potentially miss the optimal solution.

• A low learning rate can cause the model to take a long time to converge or get stuck in a local minimum.

• Com...read more

Transformers are models that process sequential data by learning contextual relationships between words.

• Transformers are a type of deep learning model commonly used in natural language processing tasks.

• They are based on the attention mechanism, allowing them to focus on different parts of the input sequence.

• Examples of transformer models include BERT, GPT, and TransformerXL.

I am a passionate Machine Learning Engineer with a strong background in computer science and a proven track record of developing innovative ML solutions.

• Completed a Master's degree in Computer Science with a focus on machine learning algorithms

• Worked on projects involving natural language processing, computer vision, and predictive modeling

• Proficient in programming languages such as Python, R, and Java

• Experience with p...read more

Yes, I have worked as a consultant before in the field of data science and machine learning.

• Worked as a consultant for a data science firm, providing expertise on machine learning models

• Collaborated with clients to understand their business needs and develop customized solutions

• Delivered presentations and reports to communicate findings and recommendations

• Provided ongoing support and guidance to clients post-implementa

Bias variance tradeoff is a key concept in machine learning that deals with the balance between underfitting and overfitting.

• Bias refers to the error that is introduced by approximating a real-life problem, while variance refers to the amount by which the estimate of the target function will change if different training data was used.

• High bias means the model is too simple and underfits the data, while high variance me...read more

Ensemble learning is a technique of combining multiple machine learning models to improve the overall performance.

• Ensemble learning can be done in two ways: bagging and boosting.

• Bagging involves training multiple models independently on different subsets of the data and then combining their predictions.

• Boosting involves training models sequentially, with each model trying to correct the errors of the previous model.

• Ens...read more