Machine Learning Real World Case Studies | Hands-on Python

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