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Home » Technology » AI / Machine Learning » Python Machine Learning Blueprints

Course Skill Level:

Foundational

Course Duration:

3 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:

    PYMLBLL21E09

Who should attend & recommended skills:

Developers with basic python experience

Who should attend & recommended skills

  • This course is geared for developers and those wanting to discover a project-based approach to mastering machine learning concepts by applying them to everyday problems using libraries such as scikit-learn, TensorFlow, and Keras.
  • Skill-level: Foundation-level Python Machine Learning skills for Intermediate skilled team members. This is not a basic class.
  • Python: Basic (1-2 years)

About this course

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 will go on to understand how to apply advanced concepts and popular machine learning algorithms to real-world projects. Next, you will cover projects from domains such as predictive analytics to analyze the stock market and recommendation systems for GitHub repositories. In addition to this, you will 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 will 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 will even create an application using computer vision and neural networks. By the end of this course, you will be able to analyze data seamlessly and make a powerful impact through your projects.

Skills acquired & topics covered

  • Getting to grips with Python’s machine learning libraries including scikit-learn, TensorFlow, and Keras
  • Implementing advanced concepts and popular machine learning algorithms in real-world projects
  • Building analytics, computer vision, and neural network projects
  • The Python data science stack and commonly used algorithms
  • Building a model to forecast the performance of an Initial Public Offering (IPO) over an initial discrete trading window
  • NLP concepts by creating a custom news feed
  • Creating applications that will recommend GitHub repositories based on ones you have starred, watched, or forked
  • Gaining the skills to build a chatbot from scratch using PySpark
  • Developing a market-prediction app using stock data
  • Delving into advanced concepts such as computer vision, neural networks, and deep learning

Course breakdown / modules

  • Data science/machine learning workflow
  • Python libraries and functions for each stage of the data science workflow
  • Setting up your machine learning environment

  • Sourcing apartment listing data
  • Inspecting and preparing the data
  • Visualizing our data
  • Visualizing the data
  • Modeling the data
  • Extending the model

  • Sourcing airfare pricing data
  • Retrieving fare data with advanced web scraping
  • Parsing the DOM to extract pricing data
  • Identifying outlier fares with anomaly detection techniques
  • Sending real-time alerts using IFTTT
  • Putting it all together

  • The IPO market
  • Data cleansing and feature engineering
  • Binary classification with logistic regression
  • Generating the importance of a feature from our model

  • Creating a supervised training set with Pocket
  • Using the Embedly API to download story bodies
  • Basics of Natural Language Processing
  • Support Vector Machines
  • IFTTT integration with feeds, Google Sheets, and email
  • Setting up your daily personal newsletter

  • What does research tell us about virality?
  • Sourcing shared counts and content
  • Exploring the features of shareability
  • Building a predictive content scoring model

  • Types of market analysis
  • What does research tell us about the stock market?
  • How to develop a trading strategy
  • Building the regression model

  • Image-feature extraction
  • Convolutional neural networks
  • Building a convolutional neural network to classify images in the Zalando Research dataset, using Keras

  • The Turing Test
  • The history of chatbots
  • The design of chatbots
  • Building a chatbot
  • Sequence-to-sequence modeling for chatbots

  • Collaborative filtering
  • Content-based filtering
  • Hybrid systems
  • Building a recommendation engine

  • Summary of the projects

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