Data Science Real-World Case Studies – Hands On Python

Course Curriculum

Chapter 1: Introduction to this course

Lecture 1: Introduction to this course & Course Benefits

Lecture 2: Utilize this oppurtunity , QnA section !!

Lecture 3: Installation of Anaconda Navigator

Lecture 4: Quick Summary of Jupyter Notebook

Chapter 2: Introduction to Life-Cycle of Data Science 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 !

Lecture 5: What is Data Science?

Chapter 3: Project 1–>> Predict success of a Zomato Restaurant using Machine Learning

Lecture 1: Datasets & Resources

Lecture 2: How to read data from Sqlite database

Lecture 3: Perform Data Cleaning on your data

Lecture 4: Perform Wordcloud Analysis !

Lecture 5: Handling Missing Values !

Lecture 6: How to fill missing values ..

Lecture 7: Lets Perform Feature extraction :

Lecture 8: Perform Feature Importance on data

Lecture 9: How to select best Feature Encoding Technique

Lecture 10: Perform (One-hot) Feature encoding on your data.

Lecture 11: Performing Mean Encoding on data !

Lecture 12: What are Outliers & how to Detect it !

Lecture 13: How to Handle Outliers !

Lecture 14: Intuition Behind Random Forest Part-1

Lecture 15: Intuition Behind Random Forest Part-2

Lecture 16: Building Machine Learning Model

Lecture 17: Intuition behind Cross validation – Part 1

Lecture 18: Intuition behind Cross validation – Part 2

Lecture 19: How to optimise Machine Learning Model !

Lecture 20: Intuition behind Logistic Regression –part 1

Lecture 21: Intuition behind Logistic Regression –part 2

Lecture 22: Intuition Behind Naive Bayes-Part 1

Lecture 23: How Naive Bayes works with Text Data..

Lecture 24: Intuition Behind Decision Tree- Part 1

Lecture 25: Intuition Behind Decision Tree- Part 2

Lecture 26: Intuition Behind Decision Tree- Part 3

Lecture 27: Intuition Behind Decision Tree- Part 4

Lecture 28: Intuition Behind Decision Tree- Part 5

Lecture 29: Intuition Behind Decision Tree- Part 6

Lecture 30: Intuition Behind KNN- Part 1

Lecture 31: Intuition Behind KNN- Part 2

Lecture 32: Intuition Behind KNN- Part 3

Lecture 33: Intuition Behind KNN- Part 4

Chapter 4: Project 2–>> Predict whether News is Fake or not ( NLP Use-case)

Lecture 1: Datasets & Resources

Lecture 2: Understand your data

Lecture 3: How to Perform Stemming on your data

Lecture 4: Intuition behind Bag of Words

Lecture 5: Applying Bag of words on data..

Lecture 6: Apply Naive Bayes on data..

Lecture 7: Analyse your data & Playing with More algorithms

Chapter 5: Project 3–>> Predict sales of a Product ( Time Series Use-case)

Lecture 1: Datasets & Resources

Lecture 2: Perform Data Cleaning on Time Series Data..

Lecture 3: Checking Stationary Trend in Data

Lecture 4: How to make your Data Stationary ?

Lecture 5: Intuition behind ARIMA –part 1

Lecture 6: Intuition behind MA model –ARIMA part 2

Lecture 7: Intuition behind AR model — ARIMA part 3

Lecture 8: Intuition behind Integrating — ARIMA part 4

Lecture 9: Apply ARIMA Algorithm on data..

Lecture 10: Applying SARIMAX Algorithm on data

Lecture 11: Doing Predictions on Future date..

Chapter 6: Bonus Lesson

Lecture 1: Bonus Lecture