#### Overview of this book

Would you like to understand how and why machine learning techniques and data analytics are spearheading enterprises globally? From analyzing bioinformatics to predicting climate change, machine learning plays an increasingly pivotal role in our society. Although the real-world applications may seem complex, this book simplifies supervised learning for beginners with a step-by-step interactive approach. Working with real-time datasets, you’ll learn how supervised learning, when used with Python, can produce efficient predictive models. Starting with the fundamentals of supervised learning, you’ll quickly move to understand how to automate manual tasks and the process of assessing date using Jupyter and Python libraries like pandas. Next, you’ll use data exploration and visualization techniques to develop powerful supervised learning models, before understanding how to distinguish variables and represent their relationships using scatter plots, heatmaps, and box plots. After using regression and classification models on real-time datasets to predict future outcomes, you’ll grasp advanced ensemble techniques such as boosting and random forests. Finally, you’ll learn the importance of model evaluation in supervised learning and study metrics to evaluate regression and classification tasks. By the end of this book, you’ll have the skills you need to work on your real-life supervised learning Python projects.
Preface
1. Fundamentals
Free Chapter
2. Exploratory Data Analysis and Visualization
3. Linear Regression
4. Autoregression
5. Classification Techniques
6. Ensemble Modeling
7. Model Evaluation

# Artificial Neural Networks

The final type of classification model that we will be studying is Artificial Neural Networks (ANNs). Firstly, this class of model is inspired by how the human brain functions. More specifically, we try to mathematically emulate the interconnected-neurons architecture, hence the name – neural networks. Essentially, an artificial neural network architecture looks something like that shown in Figure 5.57:

Figure 5.57: Neural network architecture example

To the extreme left is the input data X, expanded into the `N0` different feature dimensions. This example has two hidden layers, `h1` and `h2`, having `N1` and `N2` number of neurons, respectively. Wait, what is a neuron? The nomenclature is derived from the human brain analogy, and a neuron in the context of an artificial neural network is essentially a node in the network/graph. And finally, in the figure, there is the output layer, Y, which consists of the N number of classes for the...