Difference between AI ML DL
All what you have to know about AI; ML; DL and Data Science
This article will contain all the details you’re looking for, and save you so much time and energy. So read it carefully and follow me on medium if you want more great content.
So Let’s start !
Hey What’s Data Science?
Easy it is a great study of Where our information is coming from What it represents and How can we Turned into a valuable resource we can use for our benefit.
Okay Got it now what about AI ?
IT refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions.
IN FACT THERE ARE 3 TYPES OF “AI”
➣ Weak AI
Also known as Narrow AI or ANI (Artificial Narrow Intelligence) It does Multi tasks very well. And SIMULATE human behavior and Intelligence at completing specific tasks it’s programmed to do.
➣ Strong AI
General AI or AGI (Artificial General Intelligence) It MIMIC or REPLICATE human intelligence. So it can undrestand, think and act same way as human. ☛ Anything human can do Strong AI can do too.
➣ Super AI
ASI (Artificial Super Intelligence) AI surpasses human intelligence and ability.
Examples of AI : Speech Recognition (Siri and Alexa), Computer Vision (Camera in store to detect price of product) Natural Language Processing ( To detect feelings of others through the keyword sent in the msg) And Pattern Recognition ( To know more about your clients).
IT is an Algorithm that makes computer learn from data and make predictions, So in order to do that you must define the features(Inputs) and your expected Target(output).
IN FACT THERE ARE 3 TYPES OF “ML”
➣ Supervised Learning
Develop predictive Model based on both the INPUT and OUTPUT data.
➣ UnSupervised Learning
Group and interpret data based on INPUT data
➣ Reinforcement Learning
Trains Algorithm using a system of reward and punishment.
Before diving deeper into ML world, let’s see what is Deep Learning and how is different to Machine Learning?
DL got an Algorithm called (Artificial Neural) which is inspired by Structure and Functions of the Brain, So here you don’t have to label features mainly.
Here is the difference between ML and DL
And here is an example for you to undrestand better
To do DL you need 3 Things :
- a lots of labaled data
- High CPU performance
- Sophisticated Algorithms
Back to Supervised Machine Learning
We got 2 Types of Supervised Learning :
- Regression : It is to predict a numerical value based on previous observed data. example to predict Price of house based on location.
- Classification: It is to predict a category the data belongs to. example In Email to detect Spam.
And in each type we got different Algorithms
For Regression we got : 📈
♕ Linear Regression, ♕ MultiLinear Regression, ♕ Polynomial Regression, ♕ Decision Tree, ♕ Knn (k-nearest neighbors).
For Classification we got : 🧮
♕ Logistic Regression, ♕ Knn (k-nearest neighbors), ♕ Decision Tree, ♕Random Forest Tree.
⚠️ Yesssss As you noticed some Algorithms exist in both types.⚠️
For Unsupervised Machine Learning where we got 2 Types
♕ Divide data by Similarity.
♕Discovering interesting relations between variables in large DBs
The classic example of association rules is Market Basket Analysis and it is one of the key techniques used by large retailers to reveal associations between items. It works by looking for combinations of items that occur together frequently in transactions. To put it another way, it allows retailers to identify relationships between the items that people buy.
✅ Okay Great you got the basics But how does things work now❓
What is Data set?
DataSet is our data that we collected we split it into 2 parts to train our data and to test the rest of the data based on our training.
Why do we prepare data?
Would it be logical to use all the different data collected inside of a machine ?
Why do we Clean data?
So we can deal with the missing values, Nan, Na, “ ”,…
Why do we have missing data ?
They are missing maybe because the user forgot to fill the field or maybe data was lost while transfering Manualy from old DB.
What types of data we’ve got ?
We have Categorical Data which already contains 2️⃣types !
- Nominal (Labels are Unordered) like Red, Blue, Mazda, Toyota,etc.
- Ordinal (Labels are ordered) like Big, Much, Small, etc.
We have Numerical Data which also contains 2️⃣types !
- Discrete : expl 23; 40.
- Continous : expl 22323253532.06546565; 3205658.065465
Best of luck with your Machine Learning Journey And Have Fun , If this content was useful for you the don’t forget to Clap Clap 👏🏻👏🏻