Machine Learning: The Most Complete Guide for Beginners to Mastering Deep Learning, Artificial Intelligence and Data Science with Python

If you want to learn more about Machine Learning and Data Science or how to master them with Python quickly and easily, then keep reading.

Data Science and Machine Learning are one of the biggest buzzwords in the business world nowadays. Many businesses know the importance of collecting information, but as they can collect so much data in a short period, the real question is: "what is the next step?"

Data Science includes all the different steps that you take with the data: collecting and cleaning them, analyzing them, applying Machine Learning algorithms and models, and then presenting your findings from the analysis with some good Data Visualizations.

Machines and automation represent a huge part of our daily life. They are becoming part of our experience, and existence. Artificial Intelligence is currently one of the most thriving fields any programmer would wish to delve into, and for a good reason: this is the future

Simply put, Machine Learning is about teaching machines to think and make decisions as we would. The difference between the way machines learn and the way we do is that while for the most part we learn from experiences, machines learn from data.

• What is Machine Learning and how it is applied in real-world situations
• Understanding the differences between Machine Learning, Deep Learning, and Artificial Intelligence
• Machine learning training models, Regression techniques and Linear Regression in Python
• How to use Lists and Modules in Python
• The 12 essential libraries for Machine Learning in Python
• Artificial Neural Networks
• And Much More

• What Data Science is all about and why so many companies are using it to give them a competitive edge.
• Why Python and how to use it to implement Data Science
• The main Data Structures & Object-Oriented Programming, Functions and Modules in Python with practical codes and exercises
• The 7 most important algorithms and models in Data Science
• Data Aggregation, Group Operations, Databases and Data in the Cloud
• 9 important Data Mining techniques in Data Science
• And So Much More

Where most books only focus on how collecting and cleaning the data, this book goes further, providing guidance on how to perform a proper analysis in order to extract precious information that may be vital for a business.

Don't miss the opportunity to master the key points of Machine Learning technology and understand how researchers are breaking the boundaries of Data Science to mimic human intelligence in machines.

Even if some Machine Learning concepts and algorithms can appear complex to most computer programming beginners, this book takes the time to explain them in a simple and concise way. Understanding Machine Learning and Data Science is easier than it looks. You just need the right guidance. And this bundle provides all the knowledge you need in a simple and practical way. Regardless of your previous experience, you will learn the techniques to manipulate and process datasets, the principles of Python programming, and its most important real-world applications.