• Post category:StudyBullet-3
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Build 3 Real World Practical Projects and Go from Zero to Hero in Machine Learning by following Entire Life-cycle of ML

What you will learn

Hands on Real-World Projects on Various Domains of Machine Learning

How to apply Machine learning Algorithms in Real Life Challenges

How to build your skiils in Data science , Machine Learning

How to tackle real world challenges & how to show-case insights

Description

“Data Science and Machine Learning are one of the hottest tech fields to be in right now! The field is exploding with opportunities and career prospects. It isΒ  widely used in several sectors nowadays such as banking, healthcare technology etc..

As there are tonnes of courses on Machine Learning already available over Internet , this is not One of them..

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 Ratings of Application : Develop an Machine Learning model to predict Ratings of Play-store applications.

2.Task #2 @Predict Rent of an apartment : Predict the Rent of an apartment using machine learning Regression algorithms..


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3.Task #3 @Predict Sales of a Super-market: Develop an Machine Learning model to predict sales ofΒ  a Super-Market..

Why should you take this Course?

  • It explains Projects onΒ  real Data and real-world Problems. No toy data! This is the simplest & best way to become aΒ  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.

Who this course is for:

  • Data Scientists who want to apply their knowledge on Real World Case Studies
  • Data Analyst who want to get more Practical Assignments..
  • Machine Learning Enthusiasts who look to add more projects to their Portfolio
English
language

Content

Introduction to this course
Intro !!
How to follow this course-must watch
How to install Anaconda & Jupyter Notebook
Quick Summary of Jupyter Notebook
Project 1 :–>> Predict the Ratings of Applications on Play-store
Introduction to Problem Statement
How to access Datasets & Resources
Understand the big Idea- how to collect data !
Perform descriptive analysis on Data !
Perform Exploratory Data Analysis to understand Patterns
How to Automate your code !
Automate your data Visualisation code ..
Understand Hidden patterns from data..
Analyse whether Google is Bias or not !
Analysing distrbution of Ratings
Perform Data Preparation for Analysing App Category
Analysing Android version of data
Lets Perform Data Cleaning..
Lets Clean & ready our Rating & Installs feature
Perform Data-Preparation on Size Feature..
Perform Feature Selection algorithms to select important features
How Feature selection works..
What are outliers & how to find it..
Outliers Detection using IQR..
Outlier Detection in Install feature
How to Impute Outliers
what is Data Transformation
What are Missing Values & how to fill Missing values ?
What is Data Discretization & how to apply it in real-world ?
What is Mean Encoding & how to apply it in real world?
What is Target Guided Mean Encoding ?
What is Label Encoding & how to apply it in real-world
Project 2 :–>> Predict Rent of Apartment using Regression & Ensemble Algos
Datasets & Resources
How to load data & fill missing values in data !
Fix Missing values of Data !
How to fill Missing values using Random Value Imputation
Perform Wordcloud Analysis
Lets Clean Description Feature
Lets Prepare Description Feature using nltk !
Perform Unigram , bigram & trigram analysis..
Perform GeoSpatial Analysis
Obtaining label distribution of data
how to visualize outliers..
Imputing the outliers..
Perform In-depth analysis on data
Extract important features using Co-relation..
Most suitable Feature encoding technique In real-world ?
Lets pre-process our data for Feature Encoding..
Automate your Data Preparation stuffs !
What is Frequency Encoding & how to apply it in Real-World ?
Lets Build a Decision Tree Model
Playing with Multiple Algorithms..
Lets Hypertune our model..
Project 3 :–>> Categorizing the customers considering Supermarket data
Datasets & Resources
Lets Prepare our Data..
Finding co-relation values of a matrix..
Define own function to understand our Data !
Perform In-Depth Analysis !
Finding relationship in data !
Lets explore our data !
Data Preparation for Modelling !
Build a Machine learning Model..