Data science ,Analytics & AI Real world Project using Python

Data science ,Analytics & AI Real world Project using Python, available at $59.99, has an average rating of 4.8, with 46 lectures, based on 5 reviews, and has 1175 subscribers.

You will learn about Go from zero to hero in Entire Pipeline of AI/Data Science/Machine learning from Data Collection to building a Machine Learning Model Various Feature Engineering Techniques & how to apply it in Real-World How to Approach a problem in Real-world.. Solve any problem in your business, job or in real-time with powerful Data Sceince & Machine Learning algorithms Case studies This course is ideal for individuals who are Data Scientists who want to apply their knowledge on Real World Case Studies It is particularly useful for Data Scientists who want to apply their knowledge on Real World Case Studies.

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Summary

Title: Data science ,Analytics & AI Real world Project using Python

Price: $59.99

Average Rating: 4.8

Number of Lectures: 46

Number of Published Lectures: 45

Number of Curriculum Items: 46

Number of Published Curriculum Objects: 45

Original Price: ₹799

Quality Status: approved

Status: Live

What You Will Learn

Go from zero to hero in Entire Pipeline of AI/Data Science/Machine learning from Data Collection to building a Machine Learning Model
Various Feature Engineering Techniques & how to apply it in Real-World
How to Approach a problem in Real-world..
Solve any problem in your business, job or in real-time with powerful Data Sceince & Machine Learning algorithms
Case studies

Who Should Attend

Data Scientists who want to apply their knowledge on Real World Case Studies

Target Audiences

Data Scientists who want to apply their knowledge on Real World Case Studies

This is the first course that gives hands-onData Science, Analytics & AIReal world Projects using Python..

This is a practical course, the course I wish I had when I first started learning Data Science.

It focuses on understanding all the basic theory and programming skills required as a Data Scientist, but the best part is that it has Practical Case Studies covering so many common business problems faced by Data Scientists in the real world.

This course will cover the following topics:-

All basic stuffs of Python
Loops and conditionals
Functions
Working with Text data using Regular expressions
Numpy ,seaborn, matplotlib ,plotly and pandas library
Some Other fancy libraries like- fuzzywuzzy

Along with python programming, this course will cover other data analytics concepts such as

Data Visualization
Data cleaning
Query Analysis
Data Exploration
Statistics and Probability concepts
Feature Engineering
Featurization
Natural Language Processing
Machine Learning
Model Hypertuning

“Data Scientist has become the top job in the US for the last 4 years running!” according to Harvard Business Review & Glassdoor.

This course seeks to fill all those gaps in knowledge that scare off beginners and simultaneously apply your knowledge of Data Science , Machine Learning , Data analysis ,, Natural Language Processing to real-world business problems.

Who this course is for:

Beginners of Data Science
Data Analysts / Business Analysts who wish to do more with their data
College graduates who lack real world experience
Software Developers or Engineers who’d like to start learning Data Science
Anyone looking to become more employable as a Data Scientist
Anyone with an interest in using Data to Solve Real World Problems

Course Curriculum

Chapter 1: Welcome to this course !

Lecture 1: Introduction to course & its benefits

Lecture 2: Utilize QnA Section , (Golden Opportunity )

Lecture 3: How to follow this course – must watch !

Lecture 4: Introduction to Jupyter Notebook

Chapter 2: Understanding the business problem

Lecture 1: Datasets & Resources

Chapter 3: Data Analysis & Basic stats

Lecture 1: Perform Basic Stats on Data..

Lecture 2: Lets Understand more about data !

Lecture 3: Checking for Duplicates questions in our data !

Lecture 4: Finding occurrences of each question..

Lecture 5: Lets perform Text Analysis..

Lecture 6: Lets perform Semantic Analysis..

Chapter 4: Feature Extraction for Data Science

Lecture 1: find frequency of questions-ID

Lecture 2: Finding Length of questions

Lecture 3: How to Find common words in 2 strings..

Lecture 4: How to Find Total Words in 2 strings..

Lecture 5: Lets Create some features using basic featurization..

Lecture 6: Analysing distribution of data ( Basic EDA)

Lecture 7: Finding Boxplot & Violinplot of data ( Basic EDA)

Chapter 5: Data Pre-preocessing For Data Science/ML

Lecture 1: How to Overcome with the contractions of data !

Lecture 2: How to remove special characters from data

Lecture 3: Lets Remove Extra White-spaces in data..

Chapter 6: String matching in ML/NLP

Lecture 1: String Matching using fuzz ratio

Lecture 2: String Matching using fuzz Partial ratio

Lecture 3: String Matching using Token Sort ratio

Lecture 4: String Matching using Token set ratio.

Lecture 5: String Matching using Longest sub-string..

Chapter 7: Advance Feature Engineering in Data Science/NLP/ML

Lecture 1: Create some set of features like : [ first_word , last_word & length_diff ]

Lecture 2: What are stopwords & how to remove it from data. ?

Lecture 3: Finding common_word_count_min & common_word_count_max

Lecture 4: Lecture 25

Lecture 5: Lecture 26

Lecture 6: Lecture 27

Chapter 8: Advance Data Analysis

Lecture 1: Lecture 28

Lecture 2: Lecture 29

Lecture 3: Lecture 30

Chapter 9: NLP (Natural Language Processing )

Lecture 1: Lecture 31

Lecture 2: Lecture 32

Lecture 3: Lecture 33

Lecture 4: Lecture 34

Lecture 5: Lecture 35

Chapter 10: Machine Learning

Lecture 1: Lecture 36

Chapter 11: Hypertuning of Machine Learning Model

Lecture 1: Lecture 37

Lecture 2: Lecture 38

Lecture 3: Lecture 39

Chapter 12: Bonus lesson

Lecture 1: Bonus lecture

Instructors

Shan Singh
Top Rated & Best-Selling Udemy Instructor , Data Scientist

Rating Distribution

1 stars: 0 votes
2 stars: 0 votes
3 stars: 0 votes
4 stars: 1 votes
5 stars: 4 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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