Learn To Build & Deploy 10 Data Science & Machine Learning Apps Project With Python & Django In Heruko, Streamlit(Cloud)

What you'll learn
Build Powerful Data Science Web Applications & Deploy Them In Cloud
Build Amazing AI Projects Portfolio & Become Industry Ready
Build Real World Data Science & Machine Learning Projects
Have a great intuition of many data science models
Make robust data science models
Implement Machine Learning Algorithms

Requirements
Knowledge Of Data Science

Description
Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data. But how is this different from what statisticians have been doing for years?

The answer lies in the difference between explaining and predicting.

A Data Analyst usually explains what is going on by processing history of the data. On the other hand, Data Scientist not only does the exploratory analysis to discover insights from it, but also uses various advanced machine learning algorithms to identify the occurrence of a particular event in the future. A Data Scientist will look at the data from many angles, sometimes angles not known earlier.

So, Data Science is primarily used to make decisions and predictions making use of predictive causal analytics, prescriptive analytics (predictive plus decision science) and machine learning.

Predictive causal analytics - If you want a model that can predict the possibilities of a particular event in the future, you need to apply predictive causal analytics. Say, if you are providing money on credit, then the probability of customers making future credit payments on time is a matter of concern for you. Here, you can build a model that can perform predictive analytics on the payment history of the customer to predict if the future payments will be on time or not.

Prescriptive analytics: If you want a model that has the intelligence of taking its own decisions and the ability to modify it with dynamic parameters, you certainly need prescriptive analytics for it. This relatively new field is all about providing advice. In other terms, it not only predicts but suggests a range of prescribed actions and associated outcomes.
The best example for this is Google's self-driving car which I had discussed earlier too. The data gathered by vehicles can be used to train self-driving cars. You can run algorithms on this data to bring intelligence to it. This will enable your car to take decisions like when to turn, which path to take, when to slow down or speed up.

Machine learning for making predictions - If you have transactional data of a finance company and need to build a model to determine the future trend, then machine learning algorithms are the best bet. This falls under the paradigm of supervised learning. It is called supervised because you already have the data based on which you can train your machines. For example, a fraud detection model can be trained using a historical record of fraudulent purchases.

Machine learning for pattern discovery - If you don't have the parameters based on which you can make predictions, then you need to find out the hidden patterns within the dataset to be able to make meaningful predictions. This is nothing but the unsupervised model as you don't have any predefined labels for grouping. The most common algorithm used for pattern discovery is Clustering.
Let's say you are working in a telephone company and you need to establish a network by putting towers in a region. Then, you can use the clustering technique to find those tower locations which will ensure that all the users receive optimum signal strength.

Project-1 Sentiment Analysis App (With Deployment)

Project-2 Attrition Rate Django App

Project-3 App To Find Legendary Pokemon (With Deployment)

Project-4 Face Detection App (Deploy In Streamlit)

Project-5 Cats Vs Dogs Classification App

Project-6 Customer Revenue Prediction App (With Deployment)

Project-7 Gender From Voice Prediction App

Project-8 Restaurant Recommendation System

Project-9 Happiness Ranking App

Project-10 Forest Fire Prediction App

Who this course is for:
Beginners In Data Science

Citación :

https://rapidgator.net/file/00fab39d76f075a703031de5d396d8c2/8horj.Data.Science.Workshop.2021.10.Real.Projects.From.Scratch.part1.rar.html
https://rapidgator.net/file/a763e316fec1bc2344408e4660b251e6/8horj.Data.Science.Workshop.2021.10.Real.Projects.From.Scratch.part2.rar.html
https://rapidgator.net/file/b1ef2bacd22be32451019617accfde3d/8horj.Data.Science.Workshop.2021.10.Real.Projects.From.Scratch.part3.rar.html
https://rapidgator.net/file/ca3bd236c0452455b2394ed81695d84a/8horj.Data.Science.Workshop.2021.10.Real.Projects.From.Scratch.part4.rar.html
https://rapidgator.net/file/ee7a35c202f8a3220304719d89938e32/8horj.Data.Science.Workshop.2021.10.Real.Projects.From.Scratch.part5.rar.html


https://uploadgig.com/file/download/c8e6c4304f139543/8horj.Data.Science.Workshop.2021.10.Real.Projects.From.Scratch.part1.rar
https://uploadgig.com/file/download/3a1592F12531C830/8horj.Data.Science.Workshop.2021.10.Real.Projects.From.Scratch.part2.rar
https://uploadgig.com/file/download/702d05C7dcD617E0/8horj.Data.Science.Workshop.2021.10.Real.Projects.From.Scratch.part3.rar
https://uploadgig.com/file/download/b7f0908804fcbcE5/8horj.Data.Science.Workshop.2021.10.Real.Projects.From.Scratch.part4.rar
https://uploadgig.com/file/download/bc6Ea3eAD8667b12/8horj.Data.Science.Workshop.2021.10.Real.Projects.From.Scratch.part5.rar


https://nitroflare.com/view/C24012155F4DE7E/8horj.Data.Science.Workshop.2021.10.Real.Projects.From.Scratch.part1.rar
https://nitroflare.com/view/8A078D372BB273F/8horj.Data.Science.Workshop.2021.10.Real.Projects.From.Scratch.part2.rar
https://nitroflare.com/view/F2224D641854CBE/8horj.Data.Science.Workshop.2021.10.Real.Projects.From.Scratch.part3.rar
https://nitroflare.com/view/420E23580B62E5E/8horj.Data.Science.Workshop.2021.10.Real.Projects.From.Scratch.part4.rar
https://nitroflare.com/view/BBD65A66D19DAB2/8horj.Data.Science.Workshop.2021.10.Real.Projects.From.Scratch.part5.rar