Machine Learning Real World Case Studies - Hands-on Python

Machine Learning Real World Case Studies | Hands-on Python, available at $64.99, has an average rating of 4.75, with 85 lectures, based on 51 reviews, and has 10510 subscribers.

You will learn about 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 This course is ideal for individuals who are Data Scientists who want to apply their knowledge on Real World Case Studies or One who is curious about transitioning into Data Science, AI, Machine Learning etc.. It is particularly useful for Data Scientists who want to apply their knowledge on Real World Case Studies or One who is curious about transitioning into Data Science, AI, Machine Learning etc..

Enroll now: Machine Learning Real World Case Studies | Hands-on Python

Summary

Title: Machine Learning Real World Case Studies | Hands-on Python

Price: $64.99

Average Rating: 4.75

Number of Lectures: 85

Number of Published Lectures: 81

Number of Curriculum Items: 85

Number of Published Curriculum Objects: 81

Original Price: ?799

Quality Status: approved

Status: Live

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

Who Should Attend

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

One who is curious about transitioning into Data Science, AI, Machine Learning etc..

Target Audiences

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

One who is curious about transitioning into Data Science, AI, Machine Learning etc..

“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 is  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 theRent of an apartment using machine learning Regression algorithms..

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

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 thefull 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 topractice 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

Course Curriculum

Chapter 1: Introduction to this course

Lecture 1: Intro !!

Lecture 2: Utilize QnA Section ( Golden Opportunity ) !

Lecture 3: How to follow this course-must watch

Lecture 4: How to install Anaconda & Jupyter Notebook

Lecture 5: Quick Summary of Jupyter Notebook

Chapter 2: Introduction to Life-Cycle of Machine Learning Project !

Lecture 1: Part 1 : Business Understanding in Real World

Lecture 2: Part 2 : Data Collection & Cleaning !

Lecture 3: Part 3 : EDA(Exploratory Data Analysis) + Feature Engineering !

Lecture 4: Part 4 : Model Building & Deployment of Model !

Chapter 3: Project 1 :–>> Predict the Ratings of Applications on Play-store

Lecture 1: Introduction to Problem Statement

Lecture 2: How to access Datasets & Resources

Lecture 3: Understand the big Idea- how to collect data !

Lecture 4: Perform descriptive analysis on Data !

Lecture 5: Perform Exploratory Data Analysis to understand Patterns

Lecture 6: How to Automate your code !

Lecture 7: Automate your data Visualisation code ..

Lecture 8: Understand Hidden patterns from data..

Lecture 9: Analyse whether Google is Bias or not !

Lecture 10: Analysing distrbution of Ratings

Lecture 11: Perform Data Preparation for Analysing App Category

Lecture 12: Analysing Android version of data

Lecture 13: Lets Perform Data Cleaning..

Lecture 14: Lets Clean & ready our Rating & Installs feature

Lecture 15: Perform Data-Preparation on Size Feature..

Lecture 16: Perform Feature Selection algorithms to select important features

Lecture 17: How Feature selection works..

Lecture 18: What are outliers & how to find it..

Lecture 19: Outliers Detection using IQR..

Lecture 20: Outlier Detection in Install feature

Lecture 21: How to Impute Outliers

Lecture 22: what is Data Transformation

Lecture 23: What are Missing Values & how to fill Missing values ?

Lecture 24: What is Data Discretization & how to apply it in real-world ?

Lecture 25: What is Mean Encoding & how to apply it in real world?

Lecture 26: What is Target Guided Mean Encoding ?

Lecture 27: What is Label Encoding & how to apply it in real-world

Lecture 28: Applying Label Encoding & preparing your data for Data Modelling

Lecture 29: Intuition behind Logistic Regression-part 1

Lecture 30: Intuition behind Logistic Regression-part 2

Lecture 31: Building Logistic Regression Model

Lecture 32: Intuition Behind Decision Trees – Part 1

Lecture 33: Intuition Behind Decision Trees – Part 2

Lecture 34: Intuition Behind Decision Trees – Part 3

Lecture 35: Intuition Behind Decision Trees – Part 4

Lecture 36: Intuition Behind Decision Trees – Part 5

Lecture 37: Intuition Behind Decision Trees – Part 6

Lecture 38: Intuition Behind Random Forest – Part 1

Lecture 39: Intuition Behind Random Forest – Part 2

Lecture 40: Hypertune your Logistic Regression Model

Lecture 41: Hypertune your Random Forest Model

Chapter 4: Project 2 :–>> Predict House Prices using Regression & Ensemble Algos

Lecture 1: Datasets & Resources

Lecture 2: How to load data & fill missing values in data !

Lecture 3: Fix Missing values of Data !

Lecture 4: How to fill Missing values using Random Value Imputation

Lecture 5: Perform Wordcloud Analysis

Lecture 6: Lets Clean Description Feature

Lecture 7: Lets Prepare Description Feature using nltk !

Lecture 8: Perform Unigram , bigram & trigram analysis..

Lecture 9: Perform GeoSpatial Analysis

Lecture 10: Obtaining label distribution of data

Lecture 11: how to visualize outliers..

Lecture 12: Imputing the outliers..

Lecture 13: Perform In-depth analysis on data

Lecture 14: Extract important features using Co-relation..

Lecture 15: Most suitable Feature encoding technique In real-world ?

Lecture 16: Lets pre-process our data for Feature Encoding..

Lecture 17: Automate your Data Preparation stuffs !

Lecture 18: What is Frequency Encoding & how to apply it in Real-World ?

Lecture 19: Lets Build a Decision Tree Model

Lecture 20: Playing with Multiple Algorithms..

Lecture 21: Lets Hypertune our model..

Chapter 5: Project 3 :–>> Customer Segmentation Analysis of SuperMarket

Lecture 1: Datasets & Resources

Lecture 2: Lets Prepare our Data..

Lecture 3: Finding co-relation values of a matrix..

Lecture 4: Define own function to understand our Data !

Lecture 5: Perform In-Depth Analysis !

Lecture 6: Finding relationship in data !

Lecture 7: Lets explore our data !

Lecture 8: Data Preparation for Modelling !

Lecture 9: Build a Machine learning Model..

Chapter 6: Bonus lesson

Lecture 1: Bonus section

Instructors

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

Rating Distribution

1 stars: 0 votes

2 stars: 1 votes

3 stars: 4 votes

4 stars: 17 votes

5 stars: 29 votes

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