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Working in a hands-on learning environment, led by our Python Machine Learning Blueprints expert instructor, students will learn about and explore:
Get to grips with Python's machine learning libraries including scikit-learn, TensorFlow, and Keras.Implement advanced concepts and popular machine learning algorithms in real-world projects.Build analytics, computer vision, and neural network projects.

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Unlimited Duration

Last Updated

July 29, 2021

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Machine learning is transforming the way we understand and interact with the world around us. This course is the perfect guide for you to put your knowledge and skills into practice and use the Python ecosystem to cover key domains in machine learning. This edition covers a range of libraries from the Python ecosystem, including TensorFlow and Keras, to help you implement real-world machine learning projects. The course begins by giving you an overview of machine learning with Python. With the help of complex datasets and optimized techniques, you’ll go on to understand how to apply advanced concepts and popular machine learning algorithms to real-world projects. Next, you’ll cover projects from domains such as predictive analytics to analyze the stock market and recommendation systems for GitHub repositories. In addition to this, you’ll also work on projects from the NLP domain to create a custom news feed using frameworks such as scikit-learn, TensorFlow, and Keras. Following this, you’ll learn how to build an advanced chatbot, and scale things up using PySpark. In the concluding lessons, you can look forward to exciting insights into deep learning and you'll even create an application using computer vision and neural networks. By the end of this course, you’ll be able to analyze data seamlessly and make a powerful impact through your projects

Course Curriculum

    • The Python Machine Learning Ecosystem 00:00:00
    • Data science/machine learning workflow 00:00:00
    • Python libraries and functions for each stage of the data science workflow 00:00:00
    • Setting up your machine learning environment 00:00:00
    • Build an App to Find Underpriced Apartments 00:00:00
    • Sourcing apartment listing data 00:00:00
    • Inspecting and preparing the data 00:00:00
    • Visualizing our data 00:00:00
    • Visualizing the data 00:00:00
    • Modeling the data 00:00:00
    • Extending the model 00:00:00
    • Build an App to Find Cheap Airfares 00:00:00
    • Sourcing airfare pricing data 00:00:00
    • Retrieving fare data with advanced web scraping 00:00:00
    • Parsing the DOM to extract pricing data 00:00:00
    • Identifying outlier fares with anomaly detection techniques 00:00:00
    • Sending real-time alerts using IFTTT 00:00:00
    • Putting it all together 00:00:00
    • Forecast the IPO Market Using Logistic Regression 00:00:00
    • The IPO market 00:00:00
    • Data cleansing and feature engineering 00:00:00
    • Binary classification with logistic regression 00:00:00
    • Generating the importance of a feature from our model 00:00:00
    • Create a Custom Newsfeed 00:00:00
    • Creating a supervised training set with Pocket 00:00:00
    • Using the Embedly API to download story bodies 00:00:00
    • Basics of Natural Language Processing 00:00:00
    • Support Vector Machines 00:00:00
    • IFTTT integration with feeds, Google Sheets, and email 00:00:00
    • Setting up your daily personal newsletter 00:00:00
    • Predict whether Your Content Will Go Viral 00:00:00
    • What does research tell us about virality? 00:00:00
    • Sourcing shared counts and content 00:00:00
    • Exploring the features of shareability 00:00:00
    • Building a predictive content scoring model 00:00:00
    • Use Machine Learning to Forecast the Stock Market 00:00:00
    • Types of market analysis 00:00:00
    • What does research tell us about the stock market? 00:00:00
    • How to develop a trading strategy 00:00:00
    • Building the regression model 00:00:00
    • Classifying Images with Convolutional Neural Networks 00:00:00
    • Image-feature extraction 00:00:00
    • Convolutional neural networks 00:00:00
    • Building a convolutional neural network to classify images in the Zalando Research dataset, using Keras 00:00:00
    • Building a Chatbot 00:00:00
    • The Turing Test 00:00:00
    • The history of chatbots 00:00:00
    • The design of chatbots 00:00:00
    • Building a chatbot 00:00:00
    • Sequence-to-sequence modeling for chatbots 00:00:00
    • Build a Recommendation Engine 00:00:00
    • Collaborative filtering 00:00:00
    • Content-based filtering 00:00:00
    • Hybrid systems 00:00:00
    • Building a recommendation engine 00:00:00
    • What’s Next? 00:00:00
    • Summary of the projects 00:00:00

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