With Machine learning (ML) being an indispensable skill within all industries these days, Python is one of the most preferred languages for creating machine learning models. However, beginners may encounter many obstacles when learning and putting machine learning with Python into practice. They understand these obstacles and learn how to overcome them.
In this blog, we’ll cover some of the most common challenges that beginners working with machine learning in Python would face and give practical solutions to resolve them.
Difficulty in Understanding Mathematical Foundations
Mathematical concepts necessary for understanding machine learning are often considered quite complex by the first obstacle many beginners have when learning ML. Those of us who work with machine learning models tend to rely on concepts like linear, algebra, calculus, probability and statistics, and not everyone starts out knowing these things.
How to Overcome It:
Start with the Basics: You should spend enough time slowly learning mathematical concepts. You don’t have to know all the models before getting started coding, but with a solid understanding of them, you will be able to understand how a model works under it.
Leverage Python Libraries: Many of the hard, mathematical calculations are also handled by Python libraries like NumPy and SciPy so that you don’t have to do them yourself.
Use Resources: Courses in the maths you need to get started with machine learning are offered by platforms such as Coursera.
Data Collection and Preprocessing
The main problem faced by beginners is cleaning and preparing data which will be used in machine learning models. If that data is inaccurate, you can potentially end up with misleading results, and sometimes, the preprocessing steps to make it work can be confusing and time-consuming.
How to Overcome It:
Use Open Datasets: There are a lot of datasets available online at sites such as Kaggle, UCI Machine Learning Repository and Google Dataset Search and many of them are cleaned and even ready to use for your experiments.
Practice Data Preprocessing: You will learn to handle missing values, scale data, and encode categorical variables through typical data science techniques using Python’s pandas and scikit-learn libraries.
Automate Preprocessing: Data Preprocessing is also built into libraries like scikit-learn, which provide libraries for normalisation, one-hot encoding and imputation of the data.
Choosing the Right Algorithm
Beginners often find it difficult to pick from such a large number of available algorithms for a problem. Choose the wrong algorithm, and you may end up with a bad model performance or lengthy computation times.
How to Overcome It:
Learn the Basics of Each Algorithm: You can start by understanding the most generally applied algorithms, such as linear regression, decision trees and nearest neighbours, as well as when to apply them.
Experiment: Trial and error is typical for machine learning. Employ cross-validation to test out different algorithms and see how they fare.
Use Tools: By not forcing you to reinvent the wheel, Python libraries such as Scikit Learn and TensorFlow offer a high-level interface to many algorithms and make it easier to try out different models, all without implementing them from the base.
Overfitting and Underfitting
If a model learns that noise in its training data overfits, it will do better on its training set but not on new data. However, underfitting occurs with a model that is too simple and can’t realise the underlying patterns in data.
How to Overcome It:
Regularisation: Lasso and Ridge regression are techniques that can reduce a model to help reduce overfitting, where it penalises large coefficients in the model.
Cross-Validation: Evaluation of cross-validation of how good your model is at generalising unseen data.
Simplify the Model: Therefore, if you are overfitting, consider making your model simpler or reducing the number of features you utilise.
Model Evaluation and Tuning
Beginners tend to do modelling-building work but fail to tune and evaluate the model-making. The model may or may not perform optimally without tuning hyperparameters and choosing the appropriate evaluation metrics.
How to Overcome It:
Use Evaluation Metrics: When you have classification problems, accuracy isn’t enough. Along with precision, recall, F1 score and AUC-ROC, use metrics to get a fuller picture of your model’s performance.
Hyperparameter Tuning: Grid search or random search the best hyperparameters for your model
Monitor Learning Curves: Plot your model learning curves to understand how your model is doing the training and catch when overfitting or underfitting.
Managing Large Datasets
When training becomes the bottleneck (which is often the case as they get larger), dealing with datasets becomes more difficult, and training models will take a really long time. For beginners, it’s overwhelming, and on limited hardware, it can be even more.
How to Overcome It:
Use Sampling: Instead of applying to all of your datasets at once, begin developing and testing your model with a small sample of the dataset. Once this model has been tuned, you can run the model on the full dataset.
Optimise Code: Make sure your operations are efficient, for example, by using vectorisation with NumPy arrays to speed up an operation, etc.
Leverage Cloud Platforms: This lets you train your models on powerful cloud-based hardware, relieving the local machines.
Conclusion
Learning Machine learning with Python tends to be a battle for beginners, even when you have already studied those complex mathematical concepts. There are challenges with selecting the correct algorithm and dealing with large datasets. However, the latter can be overcome with the help of Python libraries, practising data preprocessing, and using tools like cross-validation and hyperparameter tuning to perfect our machine-learning models.
These are some of the challenges that a learner can face while learning Python and that is why the London School of Emerging Technology brings you an in-depth Python course where you can understand the basics and advance. LSET provides hands-on experience in Python real-life projects and the opportunity to take advantage of an internship. This opportunity can help the student get job-ready for their career.