Data Science And Machine Learning Using Python

The Objective Of This Course is To

Data Science And Machine Learning Using Python

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The Objective Of This Course is To


  • Make a learner ready for solving real life problems using ML
  • Understand all concepts via coding for ML implementation
  • Participate in Kaggle competitions
  • Be ready to learn “Advanced Machine Learning” and “deep learning” in future
  • Code in Python and complete understanding of all ML algorithm’s

Module 1
Module 2
Module 3
Module 4
Module 5
Module 6
Module 1

Terminology & Concepts

  • Why Machine learning and what is it?
  • What is an Error function or loss function like Gradient Descent mean?
  • Some simple real life examples on :
  • Linear regression – Predicting the Price of a House.
  • Logistic regression – Classifying diabetic people from healthy ones.
  • Decision Tree – like Google Play store Recommending Apps to us.
  • Naive Bayes – like detecting Spam mails on Gmail / yahoo.
  • KNN – help Domino’s to open 2 new outlets in your locality.
  • 2 Types of Machine Learning : Supervised & Unsupervised
  • Machine Learning work flow steps.

Module 2

Python For Data Science

  • NumPy basics for Data Science
  • Pandas for Data Analysis
  • Matplotlib for Data Visualization
  • Scikit-Learn for Data Science

Module 3

Processing, Wrangling, And Visualizing Data

  • Handling Missing Values
  • Handling Duplicates
  • Encode Categorical
  • Normalizing Numeric Values
  • Data Summarization

Module 4

Feature Engineering And Selection

  • Feature Engineering Numeric Data
  • Feature Engineering Categorical Data
  • Feature Engineering Text Data
  • Feature Scaling
  • Feature Selection

Module 5

Machine Learning Algorithms For Supervised And Unsupervised Learning

— Supervised Algorithms – Maths part + Python Coding

  • Naive Bayes Classification
  • Linear Regression
  • Support Vector Machines
  • Decision Trees
  • Random Forests
  • KNN (K-Nearest Neighbors)

— Unsupervised Algorithms –Maths part + Python Coding

  • k-Means Clustering
  • Principal Component Analysis

Module 6

Applying Knowledge To Solve Real World Problems

Project 1: Consumer complaint classification for a Fin-Tech Company

Dataset source: Kaggle

Project 2: Doing Sentiment Analysis of live Twitter feeds for any current hot topic

Dataset source: extracted live through Twitter API

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  • Enrolled 50
Date : 11/10/2023 Language : Japanees

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Exam Details

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  • Exam duration: 90 minutes
  • Number of questions: 40
  • Pass mark: 65% (26 out of 40)
  • Open book/notes: no

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