Part 1: Getting Started with Google Cloud Platform.-
Chapter 1: What Is Cloud Computing?.-
Chapter 2: An Overview of Google Cloud Platform Services.-
Chapter 3: The Google Cloud SDK and Web CLI.-
Chapter 4: Google Cloud Storage (GCS).-
Chapter 5: Google Compute Engine (GCE).-
Chapter 6: JupyterLab Notebooks.-
Chapter 7: Google Colaboratory.-
Part 2: Programming Foundations for Data Science.-
Chapter 8: What is Data Science?.-
Chapter 9: Python.-
Chapter 10: Numpy.-
Chapter 11: Pandas.-
Chapter 12: Matplotlib and Seaborn.-Part 3: Introducing Machine Learning.-
Chapter 13: What Is Machine Learning?.-
Chapter 14: Principles of Learning.-
Chapter 15: Batch vs. Online Learning.-
Chapter 16: Optimization for Machine Learning: Gradient Descent.-
Chapter 17: Learning Algorithms.-
Part 4: Machine Learning in Practice.-
Chapter 18: Introduction to Scikit-learn.-
Chapter 19: Linear Regression.-
Chapter 20: Logistic Regression.-
Chapter 21: Regularization for Linear Models.-
Chapter 22: Support Vector Machines.-
Chapter 23: Ensemble Methods.-Chapter 24: More Supervised Machine Learning Techniques with Scikit-learn.-
Chapter 25: Clustering.-
Chapter 26: Principal Components Analysis (PCA).-
Part 5: Introducing Deep Learning.-
Chapter 27: What is Deep Learning?.-
Chapter 28: Neural Network Foundations.-
Chapter 29: Training a Neural Network.-
Part 6: Deep Learning in Practice.-
Chapter 30: TensorFlow 2.0 and Keras.-
Chapter 31: The Multilayer Perceptron (MLP).-
Chapter 32: Other Considerations for Training the Network.-
Chapter 33: More on Optimization Techniques.-
Chapter 34: Regularization for Deep Learning.-
Chapter 35: Convolutional Neural Networks (CNN).-
Chapter 36: Recurrent Neural Networks (RNN).-
Chapter 37: Autoencoders.-
Part 7: Advanced Analytics/ Machine Learning on Google Cloud Platform.-
Chapter 38: Google BigQuery.-
Chapter 39: Google Cloud Dataprep.-
Chapter 40: Google Cloud Dataflow.-
Chapter 41: Google Cloud Machine Learning Engine (Cloud MLE).-
Chapter 42: Google AutoML: Cloud Vision.-
Chapter 43: Google AutoML: Cloud Natural Language Processing.-
Chapter 44: Model to Predict the Critical Temperature of Superconductors.-Part 8: Productionalizing Machine Learning Solutions on GCP.-
Chapter 45: Containers and Google Kubernetes Engine.-
Chapter 46: Kubeflow and Kubeflow Pipelines.-
Chapter 47: Deploying an End-to-End Machine Learning Solution on Kubeflow Pipelines.-
About the Author: Ekaba Bisong is a Data Science Lead at T4G. He previously worked as a data scientist/data engineer at Pythian. In addition, he maintains a relationship with the Intelligent Systems Labs at Carleton University with a research focus on learning systems (encompassing learning automata and reinforcement learning), machine learning, and deep learning. He is a Google Certified Professional Data Engineer and a Google Developer Expert in machine learning.
