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Machine learning, a subset of data science, is the rage right now. Every major company from Amazon to Google is working on machine learning. This has led to an increased interest in learning the subject. In the eagerness to study machine learning training, many students make basic mistakes that can be easily avoided.
If you know what to watch out for, you can avoid making these mistakes. Here are the most common avoidable mistakes made by machine learning novices and how to avoid them.
Using a Small Data Set
One of the most crucial steps in machine learning is training. You need to supply enough data to the algorithm so it can process the data, learn from it, and make predictions based on it.
Many students try to start off with a data set that is too small and doesn’t effectively train the algorithm. This leads to faulty predictions. This can be avoided by just using a big enough data set.
Using Too Large a Data Set
While the above point stressed the importance of supplying enough data to the algorithm, there is a downfall to having too much data, especially when you’re in the learning phase. If you use a data set that is too large to try out what you learned, you run the risk of wasting too much time. The program may take too long to run.
To understand how the various parameters affect the outcome, you need to able to change them and see the results for yourself. But if the data set is too large and the program takes too long to run, you might run out of patience and won’t experiment properly, thereby hindering your understanding of the subject. To avoid this, take a qualified and representative subset of a bigger data set and use it while practicing.
Not Dealing With Outliers Properly
Every data set will have outliers. While some of them are important and can have valuable insights, some need to be ignored. Identifying the difference is crucial to how well your machine learning program predicts the results. You can do so by understanding the data that you’re using. For example, if the data is the output of a sensor, there may be a few outliers due to sensor error, and these can be ignored.
Overfitting of data is another side of this mistake. Many students of machine learning with Python course concentrate solely on minimizing the error to the maximum extent possible. Minimization of errors is important, but you need to know when to draw the line. There are other ways to deal with this problem. Cross-validation, ensembling, regularization, training with more data, and early stopping are a few ways to avoid overfitting.
Using the Wrong Loss Function
Most students learn a few loss functions and think they can be applied in all scenarios. Loss functions, like the mean squared error, work in many scenarios, but there are also places where they fail.
Real world problems require their own unique loss functions to be defined. Be open to this and don’t limit yourself to just one or two loss functions.
Too Many or Too Few Algorithms
Algorithms are at the heart of machine learning. To expand their knowledge base and to show that they’re aware of many algorithms, many students of machine learning with Python training tend to focus on just knowing the basics of each algorithm and don’t go in-depth into any. The drawback of doing this is that you become the jack of all trades but the master of none.
You should try to find a balance. You need to know a few algorithms in depth and understand every little aspect about them. But aiming to do so with all algorithms is not practical. At the same time, you need to be aware of what the different algorithms are and where and how to use them. You can look up the specifics when you really need to use the algorithm.
Get Machine Learning With Python Training
A machine learning with Python course is a very exciting training. It’s also one of the best courses you can take to progress in your career. However, many students don’t get the results they need because they make one or more of the above mentioned mistakes. To be successful, you need to be proactive and enroll in a course with the right awareness and frame of mind.
