You must have gotten the opportunity to pay for parking at a mall, where a machine is able to tell the amount of money you owe depending on how long your car was in the parking lot and probably a few other features. However, have you ever wondered just how the parking meter is able to differentiate between currencies and give you the right change? Furthermore, have you ever wondered how applications such as Uber can predict the amount of time it will take you to get home to such a high degree of accuracy yet traffic can be so unpredictable?

If you have ever asked yourself questions about the basic or especially the complex predictions and conclusions machines are making these days, then your answer lies in Machine learning. Human beings have different ways in which they learn, some of the methods including experience or even having someone teach them. Therefore, to try to make machines even more useful to human beings, it is possible to teach machines to make decisions in several ways, and these can learn and have faster and more accurate output compared to how a human being would compete.

People usually understand the concept of how a machine will do something you have programmed it to do because people came to terms with that years ago. However, what still fascinates people is how a machine is able to make decisions independently by considering a situation and even making a prediction that turns out to be true.

Machine learning is at a very high-level today when you compare to a few years back, so that would make you wonder just how advanced machines will be in the next 20 to 30 years. It is highly likely that machines will become better versions of us, but we hope they will never get so independent and intelligent that they eventually decide to rule over us.

The objective of writing this book is to help a beginner to understand the basics of machine learning to the point of even training a machine to carry out some functions. This book also explains the advantages associated with using Python, since an individual does not necessarily have to be an expert coder to start using it.

Some of the main topics discussed in this book include:

• The history of machine learning
• Key machine learning definitions
• Application of machine learning
• Key elements of machine learning
• Types of artificial intelligence learning
• Mathematical notation for machine learning
• Terminologies in use for machine learning
• Roadmap for building machine-learning systems
• Using python for machine learning (and understanding variables, essential operator, functions, conditional statements, and loop)
• Types of artificial neural networks
• Artificial neural network layers
• Advantages and disadvantages of neural networks
• Machine learning classification
• Types of classifiers in python machine learning
• Machine learning classification models
• Metrics for evaluating machine learning classification models
• Machine learning training model
• Developing a machine learning model with python
• Training simple machine learning algorithms for classification
• Building good training sets

Would you like to know everything you need about Python Machine Learning?

Download this book and commence your journey to learning how to understand Python Machine Learning for Beginners and Artificial Intelligence.