Machine Learning with Python is one of the very accessible ways to get involved in the field, yet it has its own unique challenges. Understanding these and how to overcome them makes the learning process go smoothly and sets up for success in the field.
In this blog, we are going to discuss some of the common challenges that aspiring developers face in Machine learning with Python and we will also discuss the measures students can take to overcome them.
Understanding Data Preprocessing
Problem: Students think too little of data preparation because it deals with prefeeding the model by cleaning up the data, formatting them properly and transforming such raw data before feeding. Then, it may drastically malfunction.
Solution: First, learn the very basics of working libraries like Pandas and NumPy regarding data handling functions and how to deal with missing values, scale features, or encode categorical values. After all, learning about different kinds of datasets for data preprocessing can strengthen and prepare a person to solve real-world data problems.
Selecting the Right Machine Learning Algorithm
Problem: Too many algorithms for the first-time user to know which one to use. If the algorithm is wrong, then it might perform poorly and take a long time to troubleshoot.
Solution: Master a few algorithms first; linear regression, decision tree and k-nearest neighbours are the most accessible ones. As you start a project, identify the nature of your data, the kind of problem you have and how to measure your model’s performance. Understanding the pros and cons of each algorithm will simplify the selection process.
Avoiding Overfitting and Underfitting
Problem: The problem now arises since the model becomes either overfitting or underfitting. A model learns the training data so well that it picks the noise rather than the interest, while on the other hand, if a model fails to pick the pattern of the data, then the statement says that the given model is underfitting.
Solution: Avoid overfitting through cross-validation, regularisation and simplifying the model by removing redundant features. Avoid underfitting by ensuring that your model is complex enough to catch the patterns in data, avoiding an overly simplistic approach.
Hyperparameter Tuning
Problem: Hyperparameters can be tweaked to get really good performance out of a model, but they are confusing for beginners.
Solution: Scikit-Learn libraries, such as GridSearchCV and RandomizedSearchCV, can be employed to automate hyperparameter tuning for beginners. Try simple models and then try advanced models to build your experience in fine-tuning them.
Handling Imbalanced Datasets
Problem: In imbalanced datasets, one class is much stronger than others, so the algorithm predicts favouring the dominant class.
Solution: In oversampling the minority class, undersampling the majority class, or using an algorithm that is specifically developed for handling imbalance, the approach may be adopted as applied in Random Forest. Other evaluation metrics, such as F1 score, precision, and recall, will better represent the performance of the model on the data.
Interpreting Model Performance Metrics
Problem: A newcomer might struggle to grasp meaning beyond accuracy when a bunch of performance metrics include precision, recall or even F1-score.
Solution: Take the time to understand each metric, especially if you are working with classification problems. Accuracy alone may not always be reliable, so consider metrics based on the problem requirements and the consequences of false positives and false negatives.
Managing Computational Resources
Problem: The training is slow, as training requires complex models or very big datasets, especially in regular computers.
Solution: Work with smaller models and smaller datasets first to get used to the ML workflow. For larger projects, use cloud services such as Google Colab for free GPU resources or libraries such as Dask for large datasets.
Conclusion
Learning Machine learning with Python is a thrilling yet challenging endeavour. Anticipating and confronting common obstacles using practical solutions will give you confidence while learning this high-demand field. The London School of Emerging Technology (LSET) brings you their Machine Learning with Python course, where you can learn about the Challenges and address their solution on a practical level. Not only that, but you can also get an opportunity to participate in the LSET internship program and get into an internship for real work experience.