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Building Machine Learning Systems with Python

  • Course Code: Data Science - Building Machine Learning Systems with Python
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 4 Days Audience: This course is geared for those who wants to get more from your data by creating practical machine learning systems with Python.

Course Snapshot 

  • Duration: 4 days 
  • Skill-level: Foundation-level Machine Learning skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to get more from your data by creating practical machine learning systems with Python. 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

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. 

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 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Build a classification system that can be applied to text, images, and sound 
  • Employ Amazon Web Services (AWS) to run analysis on the cloud 
  • Solve problems related to regression using scikit-learn and TensorFlow 
  • Recommend products to users based on their past purchases 
  • Understand different ways to apply deep neural networks on structured data 
  • Address recent developments in the field of computer vision and reinforcement learning 

Audience & Pre-Requisites 

This course is designed for developers wants to get more from your data by creating practical machine learning systems with Python. 

Pre-Requisites:  Students should have familiar with  

  • Basics of Python  
  • Knowledge of Python is assumed. 

Course Agenda / Topics 

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