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Practical AI and Machine Learning Projects in Python

Introduction

Welcome to the Course (1:59)
Prerequisites (1:25)

Predicting Car Prices

Downloading the Dataset (4:34)
Loading and Examining the Dataset (8:39)
Fixing the Year Column (3:52)
Splitting Data for Training and Testing (10:19)
Preprocessing Data (11:27)
Creating the Machine Learning Flow Using Pipeline (5:26)
Training the Model (10:48)
Evaluating the Model (6:59)
Saving Model/Pipeline Using Joblib (4:13)
Source Code

Lung Cancer Classification

Downloading the Dataset (4:10)
Converting GENDER and LUNG_CANCER Columns to Binary Columns (4:36)
Preprocessing and Pipeline (10:01)
Accuracy Score and RandomForestClassifier (14:29)
Source Code

Customer Segmentation Using K Means Clustering

Understanding the Scenario (3:10)
Cleaning the Dataset (4:59)
Preprocessing and Pipeline (10:40)
Plotting Graph Using Matplotlib (7:42)
Elbow Method (9:27)
Testing the Model with New Customer (9:00)
Source Code

Image Classification Using Deep Learning

Downloading and Understanding the Dataset (3:08)
Configuring Training and Validation Dataset (7:14)
Creating Neural Network Layers (19:28)
Compiling and Training the Model (10:16)
Validating the Model (8:56)
Source Code

Image Classification Using Pre-trained Models

What is ResNet50 Pre-Trained Model? (2:58)
Predicting Images Using ResNet50 (11:50)
Source Code

Natural Language Processing (NLP)

Downloading the Dataset (1:22)
Training the Model (13:13)
Using a Pre-Trained Model (3:07)
Source Code

Computer Vision Using YOLO

Image Object Detection Using YOLO (10:06)
Video Object Detection Using YOLO (16:06)
Source Code

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Prerequisites

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