|Book name||Python machine learning|
|Publisher||Packt Publishing; 1 edition (September 23, 2015)|
If you want to gather knowledge about mechanism and data so this book will be a good source of information. It will help you in learning different techniques and data visualization.
This book is best for extending data science knowledge. Let’s explore the key features, description and content table of Python machine learning Sebastian raschka pdf.
- This book is known as one of the most powerful sources of data visualization, data learning, and data wrangling.
- You will get the answer to all queries related to robust statistical models.
- Via this book, you can explore and learn the usage of different types of machines.and can ask different questions about data.
- The usage of years and thenos can be learned by which you will be able to create neural networks.
- Writing of python code will be learned from this book.you will be able to see the power of your algorithms.
- Increased accessibility of your web application can be done with the help of this book.
- You can discover hidden structures and patterns in data with manageability.
- By using pre-processing techniques you can organize your data.
- Critical understanding about data can be built by understanding the concepts given in Python machine learning Sebastian raschka pdf.
- This book provides you an access to predictive analytics and demonstrates.
- A wide range of python libraries such as Keras and Theanos are included in this book.
Go through this content table to get perfect knowledge about this book.
1: GIVING COMPUTERS THE ABILITY TO LEARN FROM DATA
2: TRAINING MACHINE LEARNING ALGORITHMS FOR CLASSIFICATION
3: A TOUR OF MACHINE LEARNING CLASSIFIERS USING SCIKIT-LEARN
4: BUILDING GOOD TRAINING SETS – DATA PREPROCESSING
5: COMPRESSING DATA VIA DIMENSIONALITY REDUCTION
6: LEARNING BEST PRACTICES FOR MODEL EVALUATION AND HYPERPARAMETER TUNING
7: COMBINING DIFFERENT MODELS FOR ENSEMBLE LEARNING
8: APPLYING MACHINE LEARNING TO SENTIMENT ANALYSIS
9: EMBEDDING A MACHINE LEARNING MODEL INTO A WEB APPLICATION
10: PREDICTING CONTINUOUS TARGET VARIABLES WITH REGRESSION ANALYSIS
11: WORKING WITH UNLABELED DATA – CLUSTERING ANALYSIS
12: TRAINING ARTIFICIAL NEURAL NETWORKS FOR IMAGE RECOGNITION
13: PARALLELIZING NEURAL NETWORK TRAINING WITH THEANO
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