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Home » Technology » AI / Machine Learning » Building Machine Learning Systems with Python

Course Skill Level:

Foundational

Course Duration:

4 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:

    BMLSWPL21E09

Who should attend & recommended skills:

Python experienced developers who want to create machine learning systems

Who should attend & recommended skills

  • This course is designed for developers who want to get more from your data by creating practical machine learning systems with Python.
  • Skill-level: Foundation-level Machine Learning skills for Intermediate skilled team members. This is not a basic class.
  • Python: Basic (1-2 years’ experience)

About this course

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 will 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 will 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.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Machine Learning expert instructor, participants will learn about and explore:
  • Developing your own Python-based machine learning system
  • Discovering how Python offers multiple algorithms for modern machine learning systems
  • Exploring key Python machine learning libraries to implement in your projects
  • Building a classification system that can be applied to text, images, and sound
  • Employing Amazon Web Services (AWS) to run analysis on the cloud
  • Solving problems related to regression using scikit-learn and TensorFlow
  • Recommending products to users based on their past purchases
  • Understanding different ways to apply deep neural networks on structured data
  • Addressing recent developments in the field of computer vision and reinforcement learning

Course breakdown / modules

  • Machine learning and Python – a dream team

  • 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

  • Predicting house prices with regression
  • Multidimensional regression
  • Cross-validation for regression
  • Using Lasso or ElasticNet in scikit-learn
  • Regression with TensorFlow

  • 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

  • Sketching our roadmap
  • Selecting features
  • Feature projection
  • Multidimensional scaling
  • Autoencoders, or neural networks for dimensionality reduction

  • Measuring the relatedness of posts
  • Preprocessing – similarity measured as a similar number of common words
  • Clustering
  • Solving our initial challenge
  • Tweaking the parameters

  • 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

  • Using TensorFlow
  • Saving and restoring neural networks
  • LSTM for predicting text
  • LSTM for image processing

  • 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

  • Latent Dirichlet allocation

  • 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

  • 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

  • Types of reinforcement learning
  • Excelling at games

  • 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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