What really is machine learning?

  • Definitions and concepts that you must know.
  • What is machine learning used for?
  • Types of machine learning algorithms.
  • Difference between machine learning, artificial intelligence, and deep learning.
  • Where do I have to start to learn machine learning?
  • More examples of daily use of machine learning

Definitions and concepts…

What is machine learning used for?

Types of algorithms

  • Classification: This is when a label is given to the input data. For example; two labels given for images, one being “dog” and the other “cat”. The purpose of the algorithm is to sort images according to their content and place them under the right classification. This is called binary classification. If more labels were specified, it would be a multi-class classification.
  • Regression: This is when a prediction is a continuous value. Imagine a linear graph, this is what regression looks like in machine learning.
  • Forecasting: This is when a prediction is made based on historical data or data that can be. An example of this is weather forecasting; machines are now using previous weather data, to forecast a climatic event.
  • Clustering: This is done by grouping data (cluster) so that it is similar compared to other groups. Analysis is then done to find patterns in each group.
  • Dimension Reduction: Reduces the number of variables under consideration in order to find the true, latent relationship of the data.

Difference between machine learning, artificial intelligence, and deep learning.

Where to start?

  1. Determination: Believe in yourself and believe that you can learn everything necessary to learn machine learning. Think about the things that may be holding you back and just let go of them. Look for tips from people who have gone through what you desire to go through. Stay focused and never, ever give up.
  2. Use thinking processes to evaluate and solve problems: A good thinking process could be the five steps of systematic processes. Step 1, define your problem; what is it that you are trying to solve, what is the main problem? Step 2 prepare your data; Sort or organize your useful resources and data in order to make it much easier when using it. Step 3, Spot check algorithm; use a lot of testing with different cases for your algorithms in order to spot any possible errors and making sure that you fix those problems in the long run. Step 4 improve your results; after successfully testing your algorithms and ensuring that you have covered every possible error, find ways to improve it in a way that makes your project just perfect for what it is needed. Lastly, step 5, present results; showcase your results and possibly get feedback from others on their opinion and how you can improve your problem solving.
  3. Use the tool that is right for you: Use available learning environments/ platforms based on your level of experience. There is “Weka workbench” for beginners, “Python ecosystem” for intermediate and “R platform” for advanced. You should also know that python is one of the most necessary languages for machine learning.
  4. Practice on various datasets: choose various datasets to use and practice during your learning process.
  5. Build a prototype: One of the most important pieces of advice for learning machine learning is to build your own prototype. Apply the knowledge that you have been using for your project to build a small prototype.

References

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