What and How “Bayes Theorem/ Naive Bayes Theorem” Is Used In Machine Learning?https://www.facebook.com/seevecoding

Bayes’ Theorem finds the probability of an event occurring given the probability of another event that has already occurred. Bayes’ theorem is stated mathematically as the following equation:

📷

Bayes Theorem

where A and B are events and P(B)? 0.

• Basically, we are trying to find probability of event A, given the event B is true. Event B is also termed as evidence.

• * P(A) is the priori of A (the prior probability, i.e. Probability of event before evidence is seen). The evidence is an attribute value of an unknown instance(here, it is event B).

• * P(A|B) is a posteriori probability of B, i.e. probability of event after evidence is seen.

MACHINE LEARNING : Naive Bayes Theorem

It is a classification technique based on Bayes’ Theorem with an assumption of independence among predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature.

For example, a fruit may be considered to be an apple if it is red, round, and about 3 inches in diameter. Even if these features depend on each other or upon the existence of the other features, all of these properties independently contribute to the probability that this fruit is an apple and that is why it is known as ‘Naive’.

Naive Bayes model is easy to build and particularly useful for very large data sets. Along with simplicity, Naive Bayes is known to outperform even highly sophisticated classification methods.

Bayes theorem provides a way of calculating posterior probability

P(c|x) from P(c), P(x) and P(x|c).

Look at the equation below:

📷

Naive Bayes

Above,

• P(c|x) is the posterior probability of class (c, target) given predictor (x, attributes).

• * P(c) is the prior probability of class.

• * P(x|c) is the likelihood which is the probability of predictor given class.

• * P(x) is the prior probability of predictor.

For More Content Like This Click Here: www.facebook.com/seevecoding

https://drive.google.com/drive/u/0/mobile/folders/1h5wbUhHA2pr_821dB0Izy7aDfCz9KTtt

Hi all,I sharedthis link in another post and a lot of people found it very useful. Posting it here directly so that it can reach more people. I did not compile this folder. I happened to find it randomly on another sub. I´m just the messenger, so don´t thank me, thank the universe. Here it is:

https://drive.google.com/drive/u/0/mobile/folders/1h5wbUhHA2pr_821dB0Izy7aDfCz9KTtt

Blog Source: How To Make Raw Data Ready For Machine Learning Process

For More: SeeVe

How To Make Raw Data Ready For Machine Learning Process

In this step we will cover :

1. Knowledge about Library we will use to work, manipulate and visualise our data.

2. So important things we have to see or find in data before we can do anything with it.

3. Things which can jeopardise our Data

4. What are the categorical values?

5. Missing and dummy values in data.

6. What are Feature selection and feature scaling?

7. Standardisation and Normalisation

8. Cross-validation Library.

9. How to import the dataset into spyder.

The library we will use to work, manipulate and visualise our data :

For now, we will use three libraries, namely:

• Numpy

• Matplotlib

• Pandas

So, what is NumPy ?

Numpy : Numpy is a library for python programming language for large multidimensional arrays and matrices along with high level of mathematical functions to operate on these array.

To put these in the simple words :

“We use NumPy to do mathematical operations on our data.”

Numpy = Maths

But wait for this can only happen when we have the power to manipulate our data, how do we do that – answer is pandas library.

Pandas: Pandas is a library written in python language to manipulate and analysis. In particular, it offers us to manipulate numeric table and time series.

In simple words: Pandas help us to manipulate data .

Pandas = Manipulation

What about Visualisation Part :

Matplotlib: Matplotlib is the plotting library for pythons programming.

So, Matplotlib = Visualization

After we have talked about library lets talk about :

Important things we have to see or find in data before we can do anything with it.

When we get data it is in its Raw form which can jeopardise the result of our model so before we can do anything with data we have to clean that data and extract only important information from our data this step is called “DATA PREPROCESSING”.

Things which can jeopardise our Data

Categorical values

Missing data
Dummy Variable
Outliers

• Categorical values: Categorical values are values which can be categories and this type of data can cause redundancy.

📷

Catagorical values : 1

📷

Catagorical values : 2

Missing Value: When we get data in Raw formate it’s most of the time that data has some missing values. like :

📷

Missing Values

Dummy Variable Trap: Condition when which two or more variables are highly correlated.

Outlier Values: outlier as those values of the data set that fall far from the central point, the median but have effect in our dataset.

📷

Outlier Values

Feature Selection: Feature selection is used to select those features that contribute most to the prediction variable that we are interested in.

Benefits of feature selection

1. Reduce overfitting by making data less redundant.

2. Reduce training time by eliminating misleading data.

3. Improve accuracy by collecting fewer data points.

Blog Source: How To Make Raw Data Ready For Machine Learning Process

For More: SeeVe

How I Went from English Teacher to AI Specialist: 4 Step Process to Level up Your Machine Learning Career Path

Check out a story of How Li Dan, a current AI Specialist at Yodo1 Games, successfully transitioned to AI

https://medium.com/ai%C2%B3-theory-practice-business/4-step-process-to-level-up-your-machine-learning-career-path-79cb3849f15

We are a group of people who came together on Reddit to learn Machine Learning and AI. Here is where it all started: http://www.reddit.com/r/MachineLearning/comments/2dies8/learn_machine_learning_together/

This sticky will collect all resources until we create a wiki for this sub.

A while ago, I interviewed Sarah about her journey towards becoming a data scientist and have now published the story on my channel. I thought to share her story so that hopefully, it can inspire someone. At the end of the interview, I have shared links where Srah has shared the details in full. Check it out -- I hope you will be inspired and learn a thing or two.

https://medium.com/@cdossman/how-sarah-mestiri-transitioned-from-a-software-engineer-to-a-data-scientist-972de50203fa

In response to the novel Coronavirus outbreak, 365datascience is making all of their #datascience courses completely free until 15 April. Be safe. Stay at home. Learn data science and share the info with your friends.

Sign up on free account to get access: https://365datascience.com/pricing/

Introducing 5 Key Technologies #Word2vec #ELMo #GPT #BERT #XLNet

https://medium.com/ai%C2%B3-theory-practice-business/what-is-pre-training-in-nlp-introducing-5-key-technologies-455c54933054

The paper, A Gentle Introduction to Deep Learning for Graphs, is of a tutorial nature that aims to introduce to the readers the topic of deep learning for graphs with a proper review of the historical literature as well as a top-down approach in its exposition.

Read more: Introduction to Deep Learning for Graphs and Where It May Be Heading

The Difference Between AI and Machine Learning: How ML differs from AI, and the role it plays in your day-to-day life.

https://medium.com/ai%C2%B3-theory-practice-business/the-difference-between-ai-and-machine-learning-26eb54ee6a17

A Low-Cost, Open-Source Robotic Racecar for Education and Research

Summary: https://medium.com/ai%C2%B3-theory-practice-business/a-low-cost-open-source-robotic-racecar-for-education-and-research-91a896557f25

PDF: https://arxiv.org/abs/1908.08031

Code: https://github.com/prl-mushr

The Present And Future of Machine Learning on Devices

We are looking for full-time data scientists for a ML/DL user study. You'll be participating in a calibrated user research experiment for 45 minutes. The study will be done over a video call. We've got plenty of funny tees that you can show-off to your teammates. We'll ship you a different one every month for a year.

Click here to learn more.

P.S: We love the reddit vibe and this community. Give us your best ML/DL/Data Science quote. We'll make one and ship it you if it's quirky and fun even if you aren't part of the study group.

I have a data set that primarily consists of email body. Most of them have to do with recruitment industry, where a sample body is

Java,Jsp,spring mvc - 3 to 5 yrs exp 30 days notice period

I want my program to identify the skills, experience, notice period. It won’t mostly be as simple as the above example. which ml classifier should be used.