Course curriculum

  1. 01

    • Course Content

    • Some basic terms

    • Python IDE

    • IDE Installation

    • Spyder Interface

    • Installation of Required Libraries
  2. 02

    • NumPy1

    • NumPy2

    • NumPy3

    • NumPy4

    • NumPy5

    • NumPy6

    • Pandas1

    • Pandas2

    • Pandas3

    • Pandas4

    • Matplotlib1

    • Matplotlib2

    • Matplotlib3

    • Matplotlib4

    • Matplotlib5
  3. 03

    • Reading and Modifying a Dataset

    • Statistics1

    • Statistics2

    • Statistics3 - Covariance

    • Missing Values1

    • Missing Values2

    • Outlier Detection1

    • Outlier Detection3

    • Concatenation

    • Outlier Detection2

    • Dummy Variable

    • Normalization
  4. 04

    • Learning Types
  5. 05

    • Supervised Learning Models - Introduction and Understanding the Data

    • k-NN Concepts

    • k-NN Model Development

    • k-NN Training-Set and Test-Set Creation

    • Decision Tree Concepts

    • Decision Tree Model Development

    • Decision Tree - Cross Validation

    • Naive Bayes Concepts

    • Naive Bayes Model Development

    • Logistic Regression Concepts

    • Logistic Regression Model Development

    • Model Evaluation Concepts

    • Model Evaluation - Calculating with Python
  6. 06

    • Simple and Multiple Linear Regression Concepts

    • Multiple Linear Regression - Model Development

    • Evaluation Metrics - Concepts

    • Evaluation Metrics - Implementation

    • Polynomial Linear Regression Concepts

    • Random Forest Concepts

    • Polynomial Linear Regression Model Development

    • Support Vector Regression Concepts

    • Random Forest Model Development

    • Support Vector Regression Model Development
  7. 07

    • Unsupervised Learning - Introduction

    • K-means Concepts1

    • K-means Concepts2

    • K-means Model Development1

    • K-means Model Development2

    • K-means - Model Evaluation

    • DBSCAN Concepts

    • DBSCAN Model Development

    • Hierarchical Clustering Concepts

    • Hierarchical Clustering Model Development
  8. 08

    • Hyper Parameter Introduction

    • Support Vector Regression - Model Tuning

    • K-Means - Model Tuning

    • k-NN - Model Tuning

    • Overfitting and Underfitting