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Data Sciences

Building Machine Learning Systems with Python

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Working in a hands-on learning environment, led by our Machine Learning expert instructor, students will learn about and explore:
Develop your own Python-based machine learning system.Discover how Python offers multiple algorithms for modern machine learning systems.Explore key Python machine learning libraries to implement in your projects.

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Unlimited Duration

Last Updated

July 30, 2021

Students Enrolled

20

Total Reviews

Posted by

Ernesto Lee

Certification

Machine learning allows systems to learn things without being explicitly programmed to do so. Python is one of the most popular languages used to develop machine learning applications, which take advantage of its extensive library support. This third edition of Building Machine Learning Systems with Python addresses recent developments in the field by covering the most-used datasets and libraries to help you build practical machine learning systems. Using machine learning to gain deeper insights from data is a key skill required by modern application developers and analysts alike. Python, being a dynamic language, allows for fast exploration and experimentation. This lesson shows you exactly how to find patterns in your raw data. You will start by brushing up on your Python machine learning knowledge and being introduced to libraries. You'll quickly get to grips with serious, real-world projects on datasets, using modeling and creating recommendation systems. With Building Machine Learning Systems with Python, you’ll gain the tools and understanding required to build your own systems, all tailored to solve real-world data analysis problems. By the end of this lesson, you will be able to build machine learning systems using techniques and methodologies such as classification, sentiment analysis, computer vision, reinforcement learning, and neural networks.

Course Curriculum

    • Getting Started with Python Machine Learning 00:00:00
    • Machine learning and Python – a dream team 00:00:00
    • Classifying with Real-World Examples 00:00:00
    • The Iris dataset 00:00:00
    • Evaluation – holding out data and cross-validation 00:00:00
    • How to measure and compare classifiers 00:00:00
    • A more complex dataset and the nearest-neighbor classifier 00:00:00
    • Which classifier to use 00:00:00
    • Regression 00:00:00
    • Predicting house prices with regression 00:00:00
    • Multidimensional regression 00:00:00
    • Cross-validation for regression 00:00:00
    • Using Lasso or ElasticNet in scikit-learn 00:00:00
    • Regression with TensorFlow 00:00:00
    • Classification I – Detecting Poor Answers 00:00:00
    • Sketching our roadmap 00:00:00
    • Learning to classify classy answers 00:00:00
    • Fetching the data 00:00:00
    • Creating our first classifier 00:00:00
    • Deciding how to improve the performance 00:00:00
    • Using logistic regression 00:00:00
    • Looking behind accuracy – precision and recall 00:00:00
    • Slimming the classifier 00:00:00
    • Ship it! 00:00:00
    • Classification using Tensorflow 00:00:00
    • Dimensionality Reduction 00:00:00
    • Sketching our roadmap 00:00:00
    • Selecting features 00:00:00
    • Feature projection 00:00:00
    • Multidimensional scaling 00:00:00
    • Autoencoders, or neural networks for dimensionality reduction 00:00:00
    • Clustering – Finding Related Posts 00:00:00
    • Measuring the relatedness of posts 00:00:00
    • Preprocessing – similarity measured as a similar number of common words 00:00:00
    • Clustering 00:00:00
    • Solving our initial challenge 00:00:00
    • Tweaking the parameters 00:00:00
    • Recommendations 00:00:00
    • Rating predictions and recommendations 00:00:00
    • Splitting into training and testing 00:00:00
    • Normalizing the training data 00:00:00
    • A neighborhood approach to recommendations 00:00:00
    • A regression approach to recommendations 00:00:00
    • Combining multiple methods 00:00:00
    • Basket analysis 00:00:00
    • Association rule mining 00:00:00
    • Artificial Neural Networks and Deep Learning 00:00:00
    • Using TensorFlow 00:00:00
    • Saving and restoring neural networks 00:00:00
    • LSTM for predicting text 00:00:00
    • LSTM for image processing 00:00:00
    • Classification II – Sentiment Analysis 00:00:00
    • Sketching our roadmap 00:00:00
    • Fetching the Twitter data 00:00:00
    • Introducing the Naïve Bayes classifier 00:00:00
    • Creating our first classifier and tuning it 00:00:00
    • Cleaning tweets 00:00:00
    • Taking the word types into account 00:00:00
    • Topic Modeling 00:00:00
    • Latent Dirichlet allocation 00:00:00
    • Classification III – Music Genre Classification 00:00:00
    • Sketching our roadmap 00:00:00
    • Fetching the music data 00:00:00
    • Looking at music 00:00:00
    • Using FFT to build our first classifier 00:00:00
    • Improving classification performance with mel frequency cepstral coefficients 00:00:00
    • Music classification using Tensorflow 00:00:00
    • Computer Vision 00:00:00
    • Introducing image processing 00:00:00
    • Basic image classification 00:00:00
    • Computing features from images 00:00:00
    • Writing your own features 00:00:00
    • Using features to find similar images 00:00:00
    • Classifying a harder dataset 00:00:00
    • Local feature representations 00:00:00
    • Image generation with adversarial networks 00:00:00
    • Reinforcement Learning 00:00:00
    • Types of reinforcement learning 00:00:00
    • Excelling at games 00:00:00
    • Bigger Data 00:00:00
    • Learning about big data 00:00:00
    • Looking under the hood 00:00:00
    • Using jug for data analysis 00:00:00
    • Reusing partial results 00:00:00
    • Using Amazon Web Services 00:00:00
    • Creating your first virtual machines 00:00:00
    • Installing Python packages on Amazon Linux 00:00:00
    • Running jug on our cloud machine 00:00:00
    • Automating the generation of clusters with cfncluster 00:00:00

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