Trends and Transition in the Machine Learning (ML) Space Introduction to Machine Learning Motivations for Machine Learning Algorithms Data Analytics and Machine Learning Supervised Learning Classification Naive Bayes (NB) Support Vector Machine (SVM) Classifier Types of Learners Eager Learners Document Classification Illustration Logistic Regression Practical Use Cases Weather Prediction Patient's Health Monitoring Unsupervised Learning Gaussian Mixture Models Hidden Markov Models Types of Unsupervised Learning Applications of Unsupervised Learning Process of Machine Learning Gathering Input Data Data Preparation Choosing a Model Training Evaluation Parameter Tuning Prediction Machine Learning - Practical Use Cases ML in Banking ML in Agriculture ML in Health Care ML in Automobile Industry Implementation Pandas Matplotlib Jupyter Notebook Google Colab scikit-learn Conclusion References 3. Convolutional Neural Network in Computer Vision Introduction Convolutional Neural Network (CNN) Distinctive Properties of CNN Activation Functions for CNN Loss Function Datasets and Errors Bias and Variance Overfitting and Underfitting Understanding Padding and Stride Padding Stride Parameters and Hyper Parameters CONV Layer Filter Feature Map Convolution Operation Key Points about Convolution Layers and Filters Pooling Layer Key Points about Pooling Layer Types of Pooling Forward Propagation Calculating the Parameters Activation Shape and Size Backward Propagation Optimizers Other Ways to Improve the Performance of CNN Image Data Augmentation Deeper Hidden Layers Early Stopping Application of CNN Image Classification RCNN and Object Detection Region-Based Convolutional Neural Network (R-CNN) Fast R-CNN Faster R-CNN You Only Look Once (YOLO) Transfer Learning Transfer Learning for CNN Fine-Tuning or Freezing? Neural Style Transfer (NST) 2. Table of contents : Cover Half Title Title Page Copyright Page Contents Contributors 1.

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