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Catalog: machine learning

Educative Answers Team

Machine learning is ubiquitous in just about every industry right now. Every time you browse Facebook, Youtube, or Amazon, your recommended feeds are generated using machine learning.

Our catalog of quick Machine learning shots is ever-evolving. Our current selection of shots is organized by:

  1. Theory
  2. Code starters (Python)
  3. Understanding results
  4. Good to know

Disclaimer: A catalog shot links together all the shots on a particular topic and outlines how they fit together. A catalog does not attempt to cover the scope of a topic. It is only a catalog of the shots we have on the topic thus far.

1. Theory

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How do computers know what they're looking at?

Machine learning leverages data to answer questions that may not be easy to define computationally. Instead of us having to define what a cat looks like to a computer, we can have the computer understand on its own by looking at many pictures of cats on the internet. The implications of what this technology can do for us include self-driving cars, machines that can check for cancer, and more!

Here are some shots on the theory of machine learning to get you started:



2. Code starters (Python)

Much of the code for machine learning applications is done in the Python programming language. With Python, you can use utilize powerful machine learning libraries such as NumPy, pandas, TensorFlow, and PyTorch to abstract away most of the work (mathematics) for you.

Python libraries for machine learning

Learn more about popular Python libraries for machine learning:
What is NumPy?
What is pandas in Python?
What is PyTorch?
Essential Python libraries for machine learning


Get familiar with the technical terms in the language:
Sparse matrices in Python
One-hot encoding in Python

Key operations in Python for ML

Learn more about key operations in machine learning:

  • Data science is a broad field of study aimed at maintaining data sets and deriving meaning out of them before feeding this data to any machine learning model.
    Data Science complete guide (CC)
  • To ensure that the scales of your feature set (e.g., a scale of 1-100 vs. 1-5) do not skew results, we normalize data.
    Data normalization in Python
  • You’ll want to vectorize your data before further processing it.
    CountVectorizer in Python

3. Understanding results

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What do results like this mean?

After you have trained your models, you need to understand how it’s performing:

Before applying any of the techniques mentioned, be sure they apply to your model first.

  • One way to understand the trade-off between the true positive rate (TPR) and the false positive rate (FPR) of your model is to use Receiver Operating Characteristic (ROC) curves.
    What are ROC curves?
  • You can get a tabular overview of the performance of your model by looking at its confusion matrix (how confused is your model?).
    What is a confusion matrix?

4. Good to know

In this section, we will go over miscellaneous topics:


Certain languages may be better suited to a particular use case:

5. Application

We can use machine learning for automating interesting stuff.

Note: CC stands for community contributor. Interested in writing your own Edpresso article? Click here.

Further learning

Course Track: Become a Machine Learning Engineer


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