A Beginner’s Guide to Machine Learning

A Beginner’s Guide to Machine Learning

In this post, you will explore core machine learning concepts and algorithms behind all the technology that powers much of our day-to-day lives. By the end of this, you would be able to describe how it functions at the conceptual level and be equipped with the tools to start building similar models and applications yourself.

In this post, you will explore core machine learning concepts and algorithms behind all the technology that powers much of our day-to-day lives. By the end of this, you would be able to describe how it functions at the conceptual level and be equipped with the tools to start building similar models and applications yourself.

Who should read this?

  • Technical people who wish to revisit machine learning quickly.
  • Non-technical people who want an introduction to machine learning but have no idea about where to start with.
  • Anybody who thinks machine learning is “hard.”

    Why Machine Learning?

Artificial Intelligence will shape our future more powerfully than any other innovation, this century. The rate of acceleration of AI is already astonishing. After two AI winters over the past four decades, rapidly growing volumes and varieties of available data, computational processing that is cheaper and more powerful, and affordable data storage, the game is now changing.

Prerequisites to start with machine learning?

To understand the concepts presented, it is recommended that one meets the following prerequisites:

  • Technical people who wish to revisit machine learning quickly.
  • Non-technical people who want an introduction to machine learning but have no idea about where to start with.
  • Anybody who thinks machine learning is “hard.”
  1. NumPy
  2. Pandas
  3. SciKit-Learn
  4. SciPy
  5. Matplotlib (and/or Seaborn)

The Semantic Tree:

Artificial intelligence is the study of agents that perceive the world around them, form plans, and make decisions to achieve their goals.

Machine learning is a subfield of artificial intelligence. Its goal is to enable computers to learn on their own. A machine’s learning algorithm enables it to identify patterns in observed data, build models that explain the world, and predict things without having explicit pre-programmed rules and models.

The semantic tree

What is Machine Learning?

Arthur Samuel described machine learning as: “The field of study that gives computers the ability to learn without being explicitly programmed.” This is an older, informal definition which now holds little meaning.

Tom Mitchell provides a more modern definition: “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.”

In simpler terms, Machine learning is the idea that there are generic algorithms that can tell you something interesting about a set of data without you having to write any custom code which is specific to a problem. Instead of writing code, you feed data to the generic algorithm and it builds its own logic based on the data.

You can think of machine learning algorithms as falling into one of the three categories — Supervised Learning and Unsupervised Learning and Reinforcement Learning.

  • Technical people who wish to revisit machine learning quickly.
  • Non-technical people who want an introduction to machine learning but have no idea about where to start with.
  • Anybody who thinks machine learning is “hard.”

The workflow in machine learning

Roadmap to begin with Machine Learning:

  1. NumPy
  2. Pandas
  3. SciKit-Learn
  4. SciPy
  5. Matplotlib (and/or Seaborn)
  • Technical people who wish to revisit machine learning quickly.
  • Non-technical people who want an introduction to machine learning but have no idea about where to start with.
  • Anybody who thinks machine learning is “hard.”

Algorithm Implementation Roadmap:

  1. NumPy
  2. Pandas
  3. SciKit-Learn
  4. SciPy
  5. Matplotlib (and/or Seaborn)

At this stage, I highly recommend you to go through the short theory of each algorithm that I have uploaded on my Github with each implementation. You can also go through Joel Grus’s Github, where he has documented all the implementations from his book, “Data Science from Scratch”.

3.** Visualise the data!** Python has various libraries such as Matplotlib and Seaborn that help us plot the data and then the final result, to help us get a better intuition of your data and what you will be doing. (and of course, makes the model look fancy!)

  1. Tune the algorithm. All the models that we implement, have tons of buttons and knobs to play around with, better known as hyper-parameters. The learning rate, the k value, etc. all can be changed to get the best possible model.

  2. *Evaluate the model. *The Python library, SKLearn provides a lot of tools to evaluate your model and check for metrics such as accuracy, f1 score, precision etc.

Side notes:

All that I discussed above can be found on my Github, in the Machine-Learning repository. All the algorithms are systematically arranged, with both implementations from scratch and using SciKit-Learn. The data sets used are provided with each and also there is a short theory report about how the algorithms work, along with a real-life example.

I personally decided to get thorough with machine learning first before starting off with Deep Learning and I advise you to do the same, it is not necessary to go with the herd, just because Deep Learning is the new, hot topic in the industry.

Feel free to point out any mistakes you find, constructive criticism does no harm.

Thanks for reading

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