The machine learning cycle
Hey there! I’m Colleen — your friendly Machine Learning specialist and hands-on tech enthusiast. If you've ever stared at a line of ML code wondering "Okay, but why does that work?" — you’re not alone. I still remember how confusing this world felt at first. Now, I’m here to make that journey smoother (and way more fun) for others. I believe machine learning should feel like an exciting adventure, not like decoding ancient scrolls (looking at you, gradient descent). My mission? To make ML accessible, practical, and genuinely enjoyable — especially for beginners and self-learners. On my little corner of the internet, you'll find: Easy-to-follow tutorials that break down complex concepts without the jargon overload. Hands-on projects built with Python and PyTorch to turn theory into something you can actually build and use. Real talk about ML, AI trends, and the ups and downs of learning it all. Whether you’re here to learn, build, or debug life with ML, you’re in the right place. Let’s roll up our sleeves, share a few “Aha!” moments, and make some machines learn — without losing our minds. Let’s do this, together.🌸
Hey lovelies 🌸
The machine learning cycle refers to the step-by-step process that many follow to train and deploy a machine learning model. Here are the steps in the cycle:
1. Problem Definition:
At this stage, you define the problem you want the machine to solve. For example, you might want a machine learning algorithm to classify customer buying patterns to support marketing strategies.
2. Data Collection:
This is where you gather the data needed to train, evaluate, and test your machine learning model.
3. Data Preprocessing:
Here, you clean and prepare the data. Some models are sensitive to issues like outliers or missing values. This step may also include encoding—converting categorical data into numerical form.
4. Exploratory Data Analysis (EDA):
In this step, you explore the data to understand its patterns, trends, and relationships.
5. Model Selection:
You choose a suitable machine learning model based on your data and the problem you're solving.
6. Model Training:
Now you train your model using the training data and tune the hyperparameters to help it perform well.
7. Model Evaluation:
To check if the model is learning effectively, you evaluate its performance using metrics. This helps determine whether adjustments are needed before moving to testing.
8. Model Testing:
You test the model with new data it has never seen before to check if it can generalize well and make accurate predictions.
9. Model Tuning:
If the model doesn’t perform well on the test data, you may need to fine-tune the hyperparameters to improve its ability to handle unseen data.
10. Model Deployment:
Once you're satisfied with the model's performance, you integrate it into a system so it can solve the real-world business problem identified at the beginning.
11. Model Monitoring and Maintenance:
After deployment, you continuously monitor the model’s performance in the real world and make updates as necessary to keep it effective over time.
We’ll tackle each step of the machine learning cycle in detail.
Thank you for reading through! See you in the next one 🌸
