Computer Programming: 4 manuscript: Machine Learning for Beginners, Machine Learning with Python, Deep Learning with Python, Python for Data
| AUTHOR | Gron, Daniel |
| PUBLISHER | Tiger Gain Ltd (01/19/2021) |
| PRODUCT TYPE | Hardcover (Hardcover) |
Do want to learn how machine learning and neural networks work quickly and simply? Do you want to know how to build a machine learning model and you have no programming skill? Do you know a bit of Python coding and want to learn more about how this deep learning works?
This bundle is going to guide you to the basics and the principles behind machine learning. Machine learning is an active research domain and includes several different approaches. This bundle is going to help you understand the different approaches of machine learning and neural networks. It will guide you through the steps you need to build a machine learning model.
This bundle is intended to address all these questions. In fact, the aim of this book is providing the absolute beginners or other programmers that has no experience with Python programming the basic and fundamental tools of the Python language.
Through this bundle, you will learn:
- Principles of machine learning
- Types of machine learning: supervised, unsupervised, semi-supervised, and reinforcement learning
- Advantages of each type of machine learning
- Principle and types of neural networks
- Steps to develop and fit artificial neural network model
- Getting started and installing Python
- Tools and platforms for Python programming
- How to use pandas, NumPy and matplotlib Python libraries
- How to develop a simple linear and logistic machine learning model
- How to develop and train a multi-layer artificial neural network two ways: from scratch and using the Python libraries
- When to use each type of machine learning
- The general concept of artificial neural networks
- Activation function in artificial neural network and to choose an activation function within an artificial neural network
- The 5 main types of artificial neural network
- The best function to be used to train artificial neural networks.
- the 2 main concepts to know in the training process of the artificial neural network
- the main variants and algorithms for the formation of an artificial neural network and a machine learning model in general.
- The basics of the three main Python languages that will help you get the work done-including TensorFlow, Keras, and PyTorch;
- How to install the three Python libraries to help you get started;
- How to install and use magic command in Ipython
- Functionalities of NumPy library for numerical programming
- Functionalities of Pandas library for data analysis
Even if you don't have any background in machine learning and Python programming, this book will give you the tools to develop machine learning models.
Do want to learn how machine learning and neural networks work quickly and simply? Do you want to know how to build a machine learning model and you have no programming skill? Do you know a bit of Python coding and want to learn more about how this deep learning works?
This bundle is going to guide you to the basics and the principles behind machine learning. Machine learning is an active research domain and includes several different approaches. This bundle is going to help you understand the different approaches of machine learning and neural networks. It will guide you through the steps you need to build a machine learning model.
This bundle is intended to address all these questions. In fact, the aim of this book is providing the absolute beginners or other programmers that has no experience with Python programming the basic and fundamental tools of the Python language.
Through this bundle, you will learn:
- Principles of machine learning
- Types of machine learning: supervised, unsupervised, semi-supervised, and reinforcement learning
- Advantages of each type of machine learning
- Principle and types of neural networks
- Steps to develop and fit artificial neural network model
- Getting started and installing Python
- Tools and platforms for Python programming
- How to use pandas, NumPy and matplotlib Python libraries
- How to develop a simple linear and logistic machine learning model
- How to develop and train a multi-layer artificial neural network two ways: from scratch and using the Python libraries
- When to use each type of machine learning
- The general concept of artificial neural networks
- Activation function in artificial neural network and to choose an activation function within an artificial neural network
- The 5 main types of artificial neural network
- The best function to be used to train artificial neural networks.
- the 2 main concepts to know in the training process of the artificial neural network
- the main variants and algorithms for the formation of an artificial neural network and a machine learning model in general.
- The basics of the three main Python languages that will help you get the work done-including TensorFlow, Keras, and PyTorch;
- How to install the three Python libraries to help you get started;
- How to install and use magic command in Ipython
- Functionalities of NumPy library for numerical programming
- Functionalities of Pandas library for data analysis
Even if you don't have any background in machine learning and Python programming, this book will give you the tools to develop machine learning models.