Table of Contents
Chapter 1: Introduction and Environment Set Up
1.1.Difference between Data Science and Machine Learning?
1.2.Steps in Learning Data Science and Machine Learning
1.3.4.Using Google Colab Cloud Environment
Chapter 2: Python Crash Course
2.1.Writing Your First Program
2.2.Python Variables and Data Types
2.8.Data Science and Machine Learning Libraries
Project 1: House Price Prediction Using Linear Regression
1.4.Divide Data into Features and Labels
1.5.Divide Data into Training and Test Sets
1.6.Training Linear Regression Algorithm
1.7.Evaluating the Performance of a Trained Model
1.8.Making Predictions on a Single Data Point
Project 2: Filtering Spam Email Messages Using Naive Bayes’ Algorithm
2.1.Installing the Required Libraries
2.8.Evaluating Model Performance
2.9.Making Predictions on Single Instance
Project 3: Predicting Used Car Sale Price Using Feedforward Artificial Neural Networks
3.1.Installing the Required Libraries
3.4.Data Visualization and Preprocessing
3.5.Converting Categorical Columns to Numerical
3.6.Dividing Data into Training and Test Sets
3.7.Creating and Training Neural Network Model with Tensor Flow Keras
3.8.Evaluating the Performance of a Neural Network Model
3.9.Making Predictions on a Single Data Point
Project 4: Predicting Stock Market Trends with RNN (LSTM)
4.1.Recurrent Neural Networks (RNN)
4.1.1.What Is an RNN and LSTM?
4.2.Predicting Future Stock Prices via LSTM in TensorFlow Keras
4.2.1.Training the Stock Prediction Model
4.2.2.Testing the Stock Prediction Model
Project 5: Language Translation using Seq2Seq Encoder-Decoder LSTM
5.1.Creating Seq2Seq Training Model for Language Translation
5.2.Making Predictions Using Seq2Seq
Project 6: Classifying Cats and Dogs Images Using Convolutional Neural Networks
6.1.How CNN Classifies Images?
6.2.Cats and Dogs Image Classification with a CNN
6.2.1.Creating Model Architecture
6.2.3.Dividing Data into Training & Test Sets
6.2.5.Making Prediction on a Single Image
Project 7: Movie Recommender System Using Item-Based Collaborative Filtering
7.1.What Is Collaborative Filtering?
7.2.Importing the Required Libraries
7.6.Item-based Collaborative Filtering
7.6.1.Finding Recommendations Based on a Single Movie
7.6.2.Finding Recommendations Based on Multiple Movies
Project 8: Face Detection with OpenCV in Python
8.2.Installing the Libraries and Importing Images
8.6.Face Detection from Live Videos
Project 9: Handwritten English Character Recognition with CNN
9.1.Importing the Required Libraries
9.3.Data Analysis and Preprocessing
9.4.Training and Fitting CNN Model
9.6.Making Predictions on a Single Image
Project 10: Customer Segmentation Based on Income and Spending
10.2.Importing the Required Libraries
10.6.Elbow Method for Finding K Value