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English

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Data Science Real World Projects in Python

Solve 3 real Business Problems, Build Robust AI, Ml , NLP and Time Series models for Airline, Security & Stock Domain..

4.39 (102 reviews)

Data Science Real World Projects in Python

27808

Students

8 hours

Content

Jan 2021

Last Update
$19.99
Regular Price


What you will learn

Hands on Real-World Projects on Various Domains of Data Science in Machine Learning, Natural Language Processing , Time Series Analysis

Develop Natural Language Processing Models to Customer Sentiments

Develop time series forecasting models to predict Prices of stocks


Description

  • Are you looking to land a top-paying job in Data Science?

  • Or are you a seasoned AI practitioner who want to take your career to the next level?

  • Or are you an aspiring data scientist who wants to get Hands-on  Data Science and Artificial Intelligence?


If the answer is yes to any of these questions, then this course is for you!

Data Science is one of the hottest tech fields to be in right now! The field is exploding with opportunities and career prospects. Data Science is widely adopted in many sectors nowadays such as banking, healthcare, Airlines, Logistic and technology.

The purpose of this course is to provide you with knowledge of key aspects of data science applications in business in a practical, easy and fun way. The course provides students with practical hands-on experience using real-world datasets.


1.Task #1 @Predict Price of Airlines Industry : Develop an AI model to predict Fare of Airlines at various Routes.

2.Task #2 @Predict the strength of a Password: Predict the category of Password whether it is Strong, Good or Weak.

3.Task #3 @Predict Prices of a Stock: Develop time series forecasting models to predict future Stock prices.


Why should you take this Course?

  • It explains Projects on  real Data and real-world Problems. No toy data! This is the simplest & best way to become a  Data Scientist/AI Engineer/ ML Engineer

  • It shows and explains the full real-world Data. Starting with importing messy data, cleaning data, merging and concatenating data, grouping and aggregating data, Exploratory Data Analysis through to preparing and processing data for Statistics, Machine Learning , NLP & Time Series and Data Presentation.


  • It gives you plenty of opportunities to practice and code on your own. Learning by doing.

  • In real-world projects, coding and the business side of things are equally important. This is probably the only course that teaches both: in-depth Python Coding and Big-Picture Thinking like How you can come up with a conclusion

  • Guaranteed Satisfaction: Otherwise, get your money back with 30-Days-Money-Back-Guarantee.









Content

Project 1-->> Predict Fare of Airlines Tickets using Machine Learning

Understanding Data & data-preprocessing

Extract Derived Features from Data

Perform Data Pre-processing

Handle Categorical Data & Feature Encoding

Perform Label Encoding on data

How to handle Outliers in Data

Select best Features using Feature Selection Technique

Applying Random Forest on Data & Automate predictions

Play with multiple Algorithms & dumping your model

Project 2----->> Predict Password Strength using Natural Language Processing

Exploring your data

Intuition behind TF-IDF --part 1

Intuition behind TF-IDF --part 2

Apply TF-IDF on data

Intuition behind Logistic Regression --part 1

Intuition behind Logistic Regression --part 2

Apply Logistic Regression on Data

Checking Accuracy of Model

Project 3-->> Predict Stock Prices using Time Series Analysis

Analyzing Time Series data

Data preparation for Time Series Forecasting

Intuition behind ARIMA --part 1

Intuition behind MA model --ARIMA part 2

Intuition behind AR model -- ARIMA part 3

Intuition behind Integrating -- ARIMA part 4

Apply Auto-Arima on data

Evaluating Time Series Model



Reviews

S
Sahaj24 January 2021

It was nice. The Hands on Coding Experience on AI Problems motivated me to tweak the these projects and modify it.



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3759560

Udemy ID

1/8/2021

Course created date

1/21/2021

Course Indexed date
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