Python for Machine Learning & Data Science

What it covered — 17 modules

1. 01

   Course Introduction

   Welcome to the Course! · Course Overview · Python Installation and Setup

   4/4 lectures
2. 02

   NumPy - Numerical Python

   Introduction to NumPy · NumPy Arrays · NumPy Indexing and Selection

   5/5 lectures
3. 03

   Pandas - Data Analysis

   Introduction to Pandas · Series · DataFrames - Part 1, 2, and 3

   9/9 lectures
4. 04

   Matplotlib - Data Visualization

   Introduction to Matplotlib · Matplotlib Functional Method · Matplotlib Object Oriented Method

   5/5 lectures
5. 05

   Seaborn - Statistical Data Visualization

   Introduction to Seaborn · Distribution Plots · Categorical Plots

   7/7 lectures
6. 06

   Data Analysis Capstone Project

   Capstone Project Overview · Capstone Project Solutions

   2/2 lectures
7. 07

   Machine Learning Overview

   What is Machine Learning? · Types of Machine Learning · The Machine Learning Workflow

   3/3 lectures
8. 08

   Linear Regression

   Linear Regression Theory · Linear Regression with Scikit-Learn · Evaluation Metrics (MAE, MSE, RMSE)

   5/5 lectures
9. 09

   Cross Validation and Grid Search

   Train | Test Split · Cross Validation Theory · Grid Search for Hyperparameter Tuning

   3/3 lectures
10. 10

    Logistic Regression

    Logistic Regression Theory · Binary Classification · Multi-Class Classification

    4/4 lectures
11. 11

    K-Nearest Neighbors (KNN)

    KNN Theory · Choosing K Value · KNN with Scikit-Learn

    3/3 lectures
12. 12

    Support Vector Machines (SVM)

    SVM Theory · Kernel Trick · SVM for Classification

    3/3 lectures
13. 13

    Decision Trees and Random Forests

    Tree Based Methods Theory · Decision Trees with Scikit-Learn · Random Forest Classifiers

    4/4 lectures
14. 14

    Boosting Methods

    AdaBoost Theory · Gradient Boosting · XGBoost Basics

    3/3 lectures
15. 15

    Unsupervised Learning - Clustering

    K-Means Clustering Theory · Choosing Number of Clusters (Elbow Method) · Hierarchical Clustering

    4/4 lectures
16. 16

    Principal Component Analysis (PCA)

    PCA Theory · Dimensionality Reduction with PCA

    2/2 lectures
17. 17

    Model Deployment

    Introduction to Model Deployment · Pickling Models · Creating a Simple API/Web Interface (Streamlit or Flask basics)

    3/3 lectures

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