Fundamentals
Hello, I'm delighted to share my learning experiences in Python machine learning with everyone. As a Python developer, I have a strong interest in machine learning algorithms and have been continuously learning and practicing. Today, let's talk about the fundamentals of Python machine learning!
First, if you're a complete beginner, you might want to start with Andrew Ng's open course on Coursera. Professor Andrew explains the theoretical knowledge of machine learning very clearly and understandably. Even if your foundation in mathematics and statistics isn't very solid, you can still learn and grasp it step by step. In addition to video courses, there are corresponding assignments for hands-on practice to deepen your understanding.
Learning theoretical knowledge is important, but just watching videos is far from enough. You also need a lot of coding practice. Sebastian Raschka's book "Python Machine Learning" is excellent practical material. This book not only introduces common machine learning algorithms but also includes a large number of example codes that you can run, debug, and modify on your computer, learning while programming. Of course, besides this book, there are many high-quality Python machine learning tutorials on Udemy, and you can also choose courses that interest you.
Core Tools
When it comes to Python machine learning, we can't avoid mentioning several core tools and libraries. The first one is Scikit-learn, a library that integrates common machine learning algorithms and is very convenient to use. Whether it's supervised learning or unsupervised learning, from data preprocessing to model evaluation, Scikit-learn provides comprehensive support.
Besides Scikit-learn, you also need to master Python data analysis tools such as NumPy, Pandas, and Matplotlib. NumPy excels at efficient mathematical operations on numerical arrays, Pandas is a powerful tool for handling tabular data, and as for Matplotlib, it can help you generate intuitive data visualization graphs. Personally, I think these three libraries are the "iron triangle" of Python machine learning, indispensable. You need to spend time mastering them proficiently to navigate smoothly on the path of machine learning.
Data Preprocessing
Alright, when you have a certain understanding of the theoretical foundations of machine learning and Python tools, you can start practicing model development. The first step in model development is data preprocessing. As the saying goes, "garbage in, garbage out," the quality of preprocessing directly affects the performance of the final model.
Data preprocessing mainly includes two stages: feature engineering and dataset splitting. The purpose of feature engineering is to extract features from raw data that are useful for model training, such as word segmentation for text data or size normalization for image data. Here's a small question for you readers: when preparing input data for neural networks, do you prefer to put features in rows or columns? Personally, I prefer to have features as columns, with each row representing a sample, which is more in line with the convention of tabular data. Of course, the specific method should also be determined according to the requirements of the model.
Dataset splitting is also a very important step. Usually, we divide the entire dataset into three parts: training set, validation set, and test set. The training set is used for model training, the validation set is used for model tuning, and the test set is the "gold standard" for evaluating the final model performance. Reasonable dataset splitting helps us detect and prevent problems such as overfitting.
Model Selection
After data preprocessing is complete, the next step is to choose an appropriate machine learning algorithm and train the model. If you're new to machine learning, I suggest starting with simple and effective traditional algorithms, such as the well-known random forest.
Random forest is an ensemble learning method composed of multiple decision trees, with high accuracy and robustness. Training a random forest model using Scikit-learn is very simple, requiring only a few lines of code to get started. However, in practical applications, you will still encounter some problems and challenges, such as how to optimize the hyperparameters of the random forest.
I remember when working on a project before, I tried to use random search to optimize the hyperparameters of the random forest, but found that the optimal values of some parameters always ran to the boundary values. After some investigation, I discovered that it might be due to improper setting of the parameter ranges. This made me realize that even relatively simple algorithms require careful debugging and control to achieve the best results.
Neural Networks
If you have already mastered traditional machine learning algorithms and want to further improve the performance and generalization ability of the model, then you should learn about neural networks and deep learning. Deep learning performs exceptionally well in fields such as computer vision and natural language processing, and is the mainstream direction of current artificial intelligence.
When learning deep learning, the Keras framework is definitely the first choice. Keras's model definition syntax is very concise and easy to understand, allowing for quick building and training of neural network models. However, you may encounter some "pitfalls" in actual use. For example, I once tried to combine image data, image masks, and CSV data as input to a Keras model in a project, and encountered some errors. After some investigation, I found that it was caused by data format issues. So, when using any framework, it's very important to understand its data format requirements.
Besides Keras, you also need to understand some important concepts in the neural network training process, such as epochs and steps. In machine learning model training, epochs represent the number of complete traversals of the entire training set, while steps represent the number of mini-batch samples used in each epoch. Note that the value of steps is usually rounded down because the number of samples may not be divisible by the batch size. These two concepts are very important for controlling the training process. If set improperly, it's likely to lead to underfitting or overfitting.
Model Evaluation
Whether it's traditional algorithms or neural networks, after training is complete, we need to evaluate the model's performance. Common evaluation metrics include accuracy, precision, recall, F1 score, etc. Personally, I prefer to use the F1 score because it balances precision and recall well.
However, you may encounter some "tricky situations" in actual use. For example, once I trained a binary classification model using Keras, but the calculated F1 score was far from what I expected. After careful inspection, I found that it might be due to issues with the dataset or model settings. In such situations, we need to sort out and debug from multiple dimensions such as data preprocessing, model architecture, hyperparameter settings, etc., to find the root cause.
For regression tasks, the R2 score is also a commonly used evaluation metric. When learning, I had some doubts about the usage of K-fold cross-validation, model training, and R2 scoring. Fortunately, through continuous practice, I gradually understood and mastered them. If you also encounter confusion in practice, don't be discouraged. Maintain curiosity and be diligent in thinking, and you will gradually get better.
Practical Applications
Theoretical knowledge is important, but if you want to go further on the path of Python machine learning, practical experience is equally indispensable. Next, let me share some problems and insights encountered in actual projects.
In a computer vision project, I used the mmdet object detection framework. Although the official documentation of mmdet is very detailed, I still encountered some trouble when inferencing custom pre-trained models. Finally, through continuous attempts and debugging, I found the correct configuration method. From this process, I learned that no matter what framework or tool you use, you need to have sufficient understanding of it to efficiently solve problems encountered in practice.
Besides computer vision, recommender systems are also a popular application area of machine learning. I once did a question recommendation system based on Stack Overflow data, aiming to recommend related questions to users based on the questions they bookmarked. I tried using vote and tag information from the Stack Exchange dataset as input features and used traditional methods such as collaborative filtering and matrix factorization, but the results were not ideal.
Later, I realized that perhaps I should try content-based recommendation methods and perform appropriate preprocessing and feature extraction on the data. For example, tokenizing the question titles and descriptions, removing stop words, etc., to mine more valuable semantic information. Of course, the specific approach still needs further exploration and practice, and I will continue to delve into this direction.
In Conclusion
Alright, that's all I'll share with everyone today. The road of Python machine learning is long, requiring us to learn and practice persistently. Of course, although the process is arduous, as long as we maintain curiosity and passion, we will definitely go further on this road and gain a sense of achievement with half the effort.
Finally, I'd like to ask you, have you encountered any problems or insights worth sharing in your learning and practice of Python machine learning? Feel free to leave a comment in the comment section. Let's exchange ideas, inspire each other, and progress together!
