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Why is machine learning so hard to learn and understand?
It requires creativity, experimentation and tenacity. Machine learning remains a hard problem when implementing existing algorithms and models to work well for your new application. Debugging for machine learning happens in two cases: 1) your algorithm doesn’t work or 2) your algorithm doesn’t work well enough.
How long does it take to get good at machine learning?
Machine learning courses vary in a period from 6 months to 18 months. However, the curriculum varies with the type of degree or certification you opt for. You stand to gain sufficient knowledge on machine learning through 6-month courses which could give you access to entry-level positions at top firms.
Where can I learn machine learning?
If you’ve ever wondered who can learn machine learning, the answer is – you can! And if you’ve asked yourself where to learn machine learning, here’s your answer: upGrad offers a course in Machine Learning and AI, and it teaches you, among other things, NLP, Deep Learning, Reinforcement Learning and Graphical Models.
Is your machine learning model “just right”?
If you can generate a model with overall low error in both your train (past) and test (future) datasets, you’ll have found a model that is “Just Right” and balanced the right levels of bias and variance. Even when you have high accuracy, it’s possible that your machine learning model may be susceptible to other types of error.
Why is there such a high demand for machine learning?
This number is likely to have both increased – due to the number of jobs that have been created – and decreased, due to the fact that people are getting skilled in ML everyday. But the matter still remains, that the supply far exceeds the demand, in this scenario.
Is your machine learning model overfit to the training data?
If your model is overfit to the training data, it’s possible you’ve used too many features and reducing the number of inputs will make the model more flexible to test or future datasets. Similarly, increasing the number of training examples can help in cases of high variance, helping the machine learning algorithm build a more generalizable model.