Hands-On Python for Finance
Learn and implement quantitative finance using popular Python libraries like NumPy, pandas, and Keras
Key Takeaways
About the Author
Krish Naik works as a lead data scientist, pioneering in machine learning, deep learning, and computer vision, and is an artificial intelligence practitioner, an educator, and a mentor, with over 7 years' experience in the industry. He also runs a YouTube channel where he explains various topics on machine learning, deep learning, and AI with many real-world problem scenarios. He has implemented various complex projects involving complex financial data with predictive modeling, machine learning, text mining, and sentiment analysis in the healthcare, retail, and e-commerce domains. He has delivered over 30 tech talks on data science, machine learning, and AI at various meet-ups, technical institutions, and community-arranged forums.
Mastering OpenCV 4 with Python
Create advanced applications with Python and OpenCV, exploring the potential of facial recognition, machine learning, deep learning, web computing and augmented reality.
Key Takeaways
About the Author
Alberto Fernandez Villan is a software engineer with more than 12 years of experience in developing innovative solutions. In the last couple of years, he has been working in various project related to monitoring systems for industrial plants applying both IoT and Big Data technologies. He has a PhD in Computer Vision (2017), a Deep Learning Certification (2018) and has several publications in connection with computer vision and machine learning in journals such as Machine Vision and Applications, IEEE Transactions on Industrial Informatics, Sensors, IEEE Transactions on Industry Applications, IEEE Latin America Transactions, and more. Since 2013 he is a registered user ("albertofernandez") on the Q&A OpenCV forum with a dynamic activity.
Building Serverless Microservices in Python
A practical guide for developing end-to-end serverless microservices in Python for developers, DevOps, and architects.
Key Takeaways
About the Author
Richard Takashi Freeman has an M.Eng. in computer system engineering and a PhD in machine learning and natural language processing from the University of Manchester, United Kingdom. His current role is as a lead data engineer and architect, but he is also a data scientist and solutions architect. He has been delivering cloud-based, big data, machine learning, and data pipeline serverless and scalable solutions for over 14 years, and has spoken at numerous leading academic and industrial conferences, events, and summits.
Learn and implement quantitative finance using popular Python libraries like NumPy, pandas, and Keras
Key Takeaways
- Clean financial data with data preprocessing
- Visualize financial data using histograms, color plots, and graphs
- Perform time series analysis with pandas for forecasting
- Estimate covariance and the correlation between securities and stocks
- Optimize your portfolio to understand risks when there is a possibility of higher returns
- Calculate expected returns of a stock to measure the performance of a portfolio manager
- Create a prediction model using recurrent neural networks (RNN) with Keras and TensorFlow
About the Author
Krish Naik works as a lead data scientist, pioneering in machine learning, deep learning, and computer vision, and is an artificial intelligence practitioner, an educator, and a mentor, with over 7 years' experience in the industry. He also runs a YouTube channel where he explains various topics on machine learning, deep learning, and AI with many real-world problem scenarios. He has implemented various complex projects involving complex financial data with predictive modeling, machine learning, text mining, and sentiment analysis in the healthcare, retail, and e-commerce domains. He has delivered over 30 tech talks on data science, machine learning, and AI at various meet-ups, technical institutions, and community-arranged forums.
Mastering OpenCV 4 with Python
Create advanced applications with Python and OpenCV, exploring the potential of facial recognition, machine learning, deep learning, web computing and augmented reality.
Key Takeaways
- Handle files and images, and explore various image processing techniques
- Explore image transformations, including translation, resizing, and cropping
- Gain insights into building histograms
- Brush up on contour detection, filtering, and drawing
- Work with Augmented Reality to build marker-based and markerless applications
- Work with the main machine learning algorithms in OpenCV
- Explore the deep learning Python libraries and OpenCV deep learning capabilities
- Create computer vision and deep learning web applications
About the Author
Alberto Fernandez Villan is a software engineer with more than 12 years of experience in developing innovative solutions. In the last couple of years, he has been working in various project related to monitoring systems for industrial plants applying both IoT and Big Data technologies. He has a PhD in Computer Vision (2017), a Deep Learning Certification (2018) and has several publications in connection with computer vision and machine learning in journals such as Machine Vision and Applications, IEEE Transactions on Industrial Informatics, Sensors, IEEE Transactions on Industry Applications, IEEE Latin America Transactions, and more. Since 2013 he is a registered user ("albertofernandez") on the Q&A OpenCV forum with a dynamic activity.
Building Serverless Microservices in Python
A practical guide for developing end-to-end serverless microservices in Python for developers, DevOps, and architects.
Key Takeaways
- Discover what microservices offer above and beyond other architectures
- Create a serverless application with AWS
- Gain secure access to data and resources
- Run tests on your configuration and code
- Create a highly available serverless microservice data API
- Build, deploy, and run your serverless configuration and code
About the Author
Richard Takashi Freeman has an M.Eng. in computer system engineering and a PhD in machine learning and natural language processing from the University of Manchester, United Kingdom. His current role is as a lead data engineer and architect, but he is also a data scientist and solutions architect. He has been delivering cloud-based, big data, machine learning, and data pipeline serverless and scalable solutions for over 14 years, and has spoken at numerous leading academic and industrial conferences, events, and summits.
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