Your Guide to Learning Naive Bayes from Scratch for Machine Learning

Naive Bayes ranks in the top echelons of the machine learning algorithms pantheon. It is a popular and widely used machine learning algorithm and is often the go-to technique when dealing with classification problems.

The beauty of Naive Bayes lies in it’s incredible speed. You’ll soon see how fast the Naive Bayes algorithm works as compared to other classification algorithms. It works on the Bayes theorem of probability to predict the class of unknown datasets. You’ll learn all about this inside the course!

So whether you’re trying to solve a classic HR analytics problem like predicting who gets promoted, or you’re aiming to predict loan default - the Naive Bayes algorithm will get you on your way.

Wondering if you should learn Naive Bayes? If you can’t answer any of the below questions, this course is for you!

  • What is Naive Bayes?
  • How are Naive Bayes and Probability related?
  • What assumptions should we use before applying the Naive Bayes algorithm in machine learning?
  • What are the different types of Naive Bayes algorithms?
  • Is Naive Bayes only meant for binary classification problems?
  • How does Naive Bayes hold up against other classification algorithms?
  • What are the main advantages of Naive Bayes over other machine learning algorithms?
  • What are some key applications of the Naive Bayes algorithm?

 

You should be able to answer these questions with aplomb if you’re preparing yourself for a data scientist role.

Learn all about the Naive Bayes algorithm from scratch in this course. We will discuss what the Naive Bayes algorithm is, how it works, the different types of Naive Bayes algorithms, and also implement Naive Bayes on a real-world dataset. This is your perfect starting point to learning an industry-loved machine learning algorithm!

Who is the Naive Bayes from Scratch Course for?

This course is for anyone who:

  • Wants to learn the Naive Bayes algorithm from scratch
  • Wants to expand their skillset to learn a popular machine learning algorithm
  • Is curious about solving a classification problem using a different approach


The Naive Bayes algorithm is a classic that’s much loved by data scientists all around the world. It's easy to learn and will serve you well in your data science journey.


What do you need to get started with the Naive Bayes course?

  • A working laptop/desktop with 4 GB RAM
  • A working Internet connection
  • Basic knowledge of Machine Learning
  • Basic knowledge of Python / R - check out this Course first, if you are new to Python

Common Questions Beginners Ask About Naive Bayes

We saw these questions earlier. Let’s quickly go through them one by one.

What is Naive Bayes?

In machine learning, the Naive Bayes is a classification algorithm based on the concept of Bayes Theorem. Bayes theorem is one of the fundamental theorems in probability. We will be learning all these concepts in the course quite thoroughly

 

How are Naive Bayes and probability related?

The Bayes Theorem forms the backbone of the Naive Bayes algorithm. We use conditional probability to classify the data - thus, the Naive Bayes algorithm basically gives us the probability of a record being in a particular class, given the values of the features. This is nothing but conditional probability!

 

What are the assumptions we need to keep in mind for using the Naive Bayes algorithm?

The most important assumption that Naive Bayes makes is that all the features are independent of each other. You might also need to convert the continuous variables into discrete variables. 


What are the different types of Naive Bayes algorithms?

There are 3 main types of Naive Bayes algorithms:

  • Gaussian Naive Bayes
  • Multinomial Naive Bayes
  • Bernoulli Naive Bayes

We will be covering all these techniques comprehensively and with Python code in this course.

  

Can Naive Bayes be used only for binary classification?

Not at all - in fact, the Naive Bayes classifier is one of the most popular algorithms for multiclass classification. Depending on the data we are dealing with, we can use any of the 3 types of the Naive Bayes algorithms

 

How does Naive Bayes hold up against other classification algorithms?

Though classification algorithms like Logistic Regression, random forest, etc are quite popular, Naive Bayes holds its own among them. It is faster than Random forest, since it can adapt to changing data pretty quickly. If the assumptions of Naive Bayes hold true, then it is much faster than logistic regression as well.

 

What are the main advantages of Naive Bayes?

The main advantages are:

  • It is fast, and easy to understand
  • It is not prone to overfitting
  • It does not need much training data

 

What are the main advantages of Naive Bayes?

Like all other algorithms, Naive Bayes has its own set of cons as well:

  • It does not work that well when the number of features is very high
  • The assumption that input features are independent of each other may not always hold true
  • You might lose important information while discretising the continuous variables.

 

What are the applications of the Naive Bayes algorithm?

Though the algorithm is relatively simple to understand, you cannot underestimate the power of the Naive Bayes classifier! It is fast, intuitive, and is used for text classification tasks. Since it can be used for multiclass classification as well, it is considered a very versatile and flexible classifier. A majority of research papers on text classification start off by using the Naive Bayes classifier as the baseline model.

Course curriculum

  • 1
    Probability
    • Key Terms and Definitions
    • Introduction to Probability
    • Quiz: Introduction to probability
    • Calculating Probabilities of events
    • Quiz: Calculating Probabilities of events
  • 2
    The Naive Bayes Algorithm
    • Introduction to Naive Bayes
    • Quiz: Introduction to Naive Bayes
    • Conditional Probability and Bayes Theorem
    • Working of Naive Bayes
    • Quiz: Conditional Probability and Naive Bayes
    • Math Behind Naive Bayes
    • Types of Naive Bayes
    • Quiz: Types of Naive Bayes
    • Implementing Naive Bayes
    • Pros and Cons of Naive Bayes
    • Applications of Naive Bayes
    • Improve your Naive Bayes Model
  • 3
    What Next?
    • More Resources and Next Steps

Instructor(s)

FAQ

Common questions related to the Naive Bayes from Scratch course

  • Who should take the Naive Bayes course?

    We have designed this course in a way that’s easy to understand for beginners in machine learning. You will get to learn Naive Bayes from scratch and also implement it using Python and R. Take this course if you want to learn a popular machine learning algorithm and expand your repertoire of techniques to solve classification problems.

  • I have decent programming experience but no background in machine learning. Is this course right for me?

    You would need to have an idea of the core machine learning algorithms. Naive Bayes is a slightly advanced algorithm so you would at least need to know what is supervised and unsupervised learning, and the difference between regression and classification problems.

  • What is the fee for the course?

    This course is free of cost!

  • How long would I have access to the “Naive Bayes from Scratch” course?

    Once you register, you will have 6 months to complete the course. If you visit the course 6 months after your initial registration, you will need to enrol in the course again. Your past progress will be lost.

  • How much effort do I need to put in for this course?

    You can complete the “Naive Bayes from Scratch” course in a few hours.

  • I’ve completed this course and have a good grasp on Naive Bayes. What should I learn next?

    The next step in your journey is to build on what you’ve learned so far. We recommend taking the popular “Applied Machine Learning” course.

  • Can I download the videos in this course?

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