In general everybody learn something from their past experience. Same way here machine learning something from past experience. Almost all companies using machine learning like Google, Amazon, Flipkart, etc... for their product recommendations.
Examples:
1.Build a model for you tube video or movie recommended system
Here need to build an model that recommends particular category videos to certain people based on some parameters. like Age, Location, Previous watching's, Trending videos etc.. .take these parameters use algorithms and build model that recommends correctly.
Building a model is easy but how much accuracy our model giving is most important.
2.Build a Predictive Analysis model of 2nd hand car prices based on parameters like showroom price, how many kilometers reading, type of car weather it is automatic or manual , uses petrol or diesel or CNG, in which year car model etc..
based past data train a model and need to give accurate results.
check out these for car price prediction model
Types of Machine Learning Algorithms:
1.Supervised algorithms
2.Unsupervised algorithms
3.Reinforcement algorithms
1.Supervised algorithms:
Here we already know the output. the dataset contains labeled data.
example: we know like if a person contains long hair on head, and no beard ,no moustache then that one is lady. same way if person contains short hair on head with beard and moustache then that is men. here already we know the output .So we need to build a model which predicts if new person came into class based on these parameters it should tell whether girl or boy.
Supervised learning classified in to two types:
I) Classification: In this classification test data has specific categories.
example: gender can be classify like Male or female two categories
Based on data we will choose which algorithms which gives better results for our data.
different types of Classification algorithms:
1.Random Forest algorithm
2.K-nearest neighbor algorithm
3.Decision trees algorithm
4.SVM (Support vector machine algorithm)
5.Linear classification algorithm
II) Regression: In this test data contains be continuous values
example: Heights of people in an area. like 5.1 cm,4.6 cm,6.1 cm here test data has continuous values.
Based on data we will choose which algorithms which gives better results for our data.
different types of Classification algorithms:
1.Logistical Regression
2.Linear Regression
3. Polynomial Regression
4.Ridge Regression
5.Lasso Regression
2.Unsupervised algorithms:
Here there is no labeled data .
That means we will not tell to machine like this boy and like this girl .machine learning model should learn automatically and categorize these are boys and these are girls based similar features.
two types of Unsupervised learning:
I)Clustering: here test data objects grouping into cluster that means similar objects into one groups in to one cluster and less similar objects into another cluster.
II)Association: It is an unsupervised learning it finds relation among variables.one person belongs two places
Types of Unsupervised algorithms:
1.K means clustering
2. Neural Networks
3.Heirarchal clustering
4.KNN
5.Singular value decomposition
6.Independent Component
3.Reinforcement algorithms:
It is different to both Supervised and Unsupervised learning .
Here if system chooses correct answer then gives rewards and other wise no reward. Based on this system should automatically builds a model.
as of now Reinforcement learning very difficult .
Advantages of Machine Learning:
Disadvantages of Machine Learning :
Applications of Machine Learning:
1.Image Recognition applications: In few application we need to identify the image and need to collect data from image . in that scenario it will useful. for example take mobile phone face lock . to identify our face image is same as stored in that database to unlock screen. in that situation these models used to identify image and verify correctly.
2.Medical Reports: In medical report analysis also these models are using to identify the reports and find out proper disease. by using these in medical reports like x-ray images we will get more accurate results and helpful in diagnosis.
3.Email Spam detection: Email spam detection also it used , it identifies email is belongs to spam or not . It will take some keywords used in that mail based on that it identifies and keeps in separate folder . As of now in our google email we can see .email system automatically filtering our emails and keeps in separate folders
4. Automation driving cars: Automation driving cars or self driving cars this machine learning and AI works more. it reduces human intervention.
5.Speech recognition: System identifies speech and responding according to questions. in this speech recognition systems it is used , and virtual assistance in this virtual assistance based on question what we typed on chat system understands the situation and provides proper answer . no need to wait for agent .it saves the time and efforts.
6.Recommendations: Product recommendations machine learning used mostly. based on previous data it recommends what mostly they likes.
for examples in you tube video recommendation. based on previous searches and watched videos it suggests to end user. and same in any online marketing like amazon and flipkart these recommended systems helps their products sales improvement.
7.Stock markets: In stock market predictions these machine learning and AI recommended systems predicts market trending's and helps to customer investments.
How to start Data science ? please follow these channel
Lilly
ReplyDeleteGood information , thanks
ReplyDeleteThank you Mahendra
DeleteThis comment has been removed by the author.
ReplyDeleteThis comment has been removed by the author.
ReplyDelete