Python is an open-source community-supported, general-purpose programming language that, over the years, has also become one of the bastions of data science. Thanks to its flexibility and vast popularity that data analysis, visualization, and machine learning can be easily carried out with Python. This course will help you learn the tools necessary to perform data science. You will explore coding on real-life datasets, and implement your knowledge on projects. By the end of this course, you’ll have embarked on a journey from data cleaning and preparation to creating summary tables, from visualization to machine learning and prediction.
- Access 8 lectures & 3.65 hours of content 24/7
- Learn data analysis, manipulation, & visualization using the Pandas library
- Create statistical plots using Matplotlib & Seaborn to help you get insights into real size patterns hidden in data
- Gain an in-depth understanding of the various packages so as to perform data analysis & implement machine learning models
This video course will get you up-and-running with one of the most cutting-edge deep learning libraries: PyTorch. Written in Python, PyTorch is grabbing the attention of all data science professionals due to its ease of use over other libraries and its use of dynamic computation graphs. In this course, you will learn how to accomplish useful tasks using Convolutional Neural Networks to process spatial data such as images and using Recurrent Neural Networks to process sequential data such as texts. Throughout this journey, you will implement various mechanisms of the PyTorch framework to do these tasks.
- Access 6 lectures & 4.7 hours of content 24/7
- Explore PyTorch & the impact it has made on Deep Learning
- Build your neural network using Deep Learning techniques in PyTorch
- Perform basic operations on your dataset using tensors & variables
- Build artificial neural networks in Python w/ GPU acceleration
- See how CNN works in PyTorch w/ a simple computer vision example
- Train your RNN model from scratch for text generation
Considered the Holy Grail of automation, data analysis, and robotics, Artificial Intelligence has taken the world by storm as a major field of research and development. Python has surfaced as a dominate language in AI/ML programming because of its simplicity and flexibility, in addition to its great support for open source libraries such as spaCy and TensorFlow. This video course is built for those with a basic understanding of artificial intelligence, introducing them to advanced artificial intelligence projects as they go ahead.
- Access 3 lectures & 2.03 hours of content 24/7
- Extract names, places, & more and their relationships from text
- Build a recommendation engine for finding new music
- Use deep reinforcement learning to build an AI that plays arcade games
- Employ the SpaCy & textacy libraries for natural language processing
- Use popular libraries such as Keras& TensorFlow for reinforcement learning
This course will teach you how to use Python on parallel architectures. You’ll learn to use the power of NumPy, SciPy, and Cython to speed up computation. Then you will get to grips with optimizing critical parts of the kernel using various tools. You will also learn how to optimize your programmer using Numba. You’ll learn how to perform large-scale computations using Dask and implement distributed applications in Python; finally, you’ll construct robust and responsive apps using Reactive programming. By the end, you will have gained a solid knowledge of the most common tools to get you started on HPC with Python.
- Access 8 lectures & 4.02 hours of content 24/7
- Master using NumPy, SciPy, & Cython to speed up your numerical computations
- Leverage the power of multiprocessing & multithreading in Python for parallelism
- Master using Dask to handle large data in a distributed setting & reactive applications in Python
Machine learning and artificial intelligence are the new big data—at least as far as buzzwords in the workplace go. The scikit-learn library is one of the most popular platforms for everyday Machine Learning and data science because it is built upon Python, a fully-featured programming language. This course will help you discover the magical black box that is Machine Learning by teaching a practical approach to modeling using Python along with the Scikit-Learn library.
- Access 5 lectures & 2.65 hours of content 24/7
- Split data effectively using the Scikit-Learn package
- Explore, organize, manipulate, & analyze your data (including some visual and descriptive statistics techniques)
- Enhance your model performance using cross-validation
- Build & design model pipelines using Scikit-Learn paired w/ your custom transformers
- Tune & optimize hyperparameters to select the best model for the job
- Persist a model for use in production
Computer vision solutions are becoming increasingly common, making their way into fields such as health, automobile, social media, and robotics. This book will help you explore TensorFlow 2, the brand new version of Google’s open-source framework for machine learning. You will understand how to benefit from using convolutional neural networks (CNNs) for visual tasks. In the process, you will acquire advanced insights into transfer learning, data augmentation, domain adaptation, and mobile and web deployment, among other key concepts.
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- 372-page eBook
- Create your own neural networks from scratch
- Classify images with modern architectures including Inception and ResNet
- Detect and segment objects in images with YOLO, Mask R-CNN, and U-Net
- Tackle problems faced when developing self-driving cars and facial emotion recognition systems
- Boost your application’s performance with transfer learning, GANs, and domain adaptation
- Use recurrent neural networks (RNNs) for video analysis
- Optimize & deploy your networks on mobile devices and in the browser
Deep learning is one of the most popular domains in the AI space that allows you to develop multi-layered models of varying complexities. This book introduces you to popular deep learning algorithms—from basic to advanced—and shows you how to implement them from scratch using TensorFlow. Throughout the book, you will gain insights into each algorithm, the mathematical principles involved, and how to implement it in the best possible manner. By the end of this book, you will be equipped with all the skills you need to implement deep learning in your own projects.
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- 512-page eBook
- Get up to speed with building your own neural networks from scratch
- Gain insights into the mathematical principles behind deep learning algorithms
- Implement popular deep learning algorithms such as CNNs, RNNs, & more using TensorFlow
This eBook will show you how to analyze your data, get started with machine learning, and work effectively with Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. By the end of this book, you will be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets.
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- 740-page eBook
- Perform efficient data analysis & manipulation tasks using pandas
- Apply pandas to different real-world domains w/ the help of step-by-step demonstrations
- Get accustomed to using pandas as an effective data exploration tool
Web scraping is an essential technique used in many organizations to gather valuable data from web pages. This book will enable you to delve into web scraping techniques and methodologies. The book will introduce you to the fundamental concepts of web scraping techniques and how they can be applied to multiple sets of web pages. You’ll use powerful libraries from the Python ecosystem such as Scrapy, lxml, pyquery, and bs4 to carry out web scraping operation. It adopts a practical approach to web scraping concepts and tools, guiding you through a series of use cases and showing you how to use the best tools and techniques to efficiently scrape web pages.
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- 350-page eBook
- Learn different scraping techniques using a range of Python libraries such as Scrapy & Beautiful Soup
- Build scrapers and crawlers to extract relevant information from the web
- Automate web scraping operations to bridge the accuracy gap & manage complex business needs
Object-oriented programming (OOP) is a relatively complex discipline to master, and it can be difficult to see how general principles apply to each language’s unique features. With the help of the latest edition of Mastering Objected-Oriented Python, you’ll be shown how to effectively implement OOP in Python, and even explore Python 3.x. Complete with practical examples, the book guides you through the advanced concepts of OOP in Python, and demonstrates how you can apply them to solve complex problems in OOP.
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- 770-page eBook
- Explore a variety of different design patterns for the __init__() method
- Learn to use Flask to build a RESTful web service
- Discover SOLID design patterns & principles
- Use the features of Python 3’s abstract base
- Create classes for your own applications
- Design testable code using pytest & fixtures
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