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Python Machine Learning Blueprints

  • Course Code: Data Science - Python Machine Learning Blueprints
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 3 Days Audience: This course is geared for those who wants 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.

Course Snapshot 

  • Duration: 3 days 
  • Skill-level: Foundation-level Python 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 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. 
  • 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 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. 

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 

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

  • Understand the Python data science stack and commonly used algorithms 
  • Build a model to forecast the performance of an Initial Public Offering (IPO) over an initial discrete trading window  
  • Understand NLP concepts by creating a custom news feed 
  • Create applications that will recommend GitHub repositories based on ones you’ve starred, watched, or forked 
  • Gain the skills to build a chatbot from scratch using PySpark 
  • Develop a market-prediction app using stock data 
  • Delve into advanced concepts such as computer vision, neural networks, and deep learning 

Audience & Pre-Requisites 

This course is designed for developers wants 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 

Pre-Requisites:  Students should have familiar with  

  • Basics of Python  
  • Knowledge of Python is assumed. 

Course Agenda / Topics 

  1. The Python Machine Learning Ecosystem 
  • The Python Machine Learning Ecosystem 
  • Data science/machine learning workflow 
  • Python libraries and functions for each stage of the data science workflow 
  • Setting up your machine learning environment 
  1. Build an App to Find Underpriced Apartments 
  • Build an App to Find Underpriced Apartments 
  • Sourcing apartment listing data 
  • Inspecting and preparing the data 
  • Visualizing our data 
  • Visualizing the data 
  • Modeling the data 
  • Extending the model 
  1. Build an App to Find Cheap Airfares 
  • Build an App to Find Cheap Airfares 
  • 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 
  1. Forecast the IPO Market Using Logistic Regression 
  • Forecast the IPO Market Using Logistic Regression 
  • The IPO market 
  • Data cleansing and feature engineering 
  • Binary classification with logistic regression 
  • Generating the importance of a feature from our model  
  1. Create a Custom Newsfeed 
  • Create a Custom Newsfeed 
  • 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 
  1. Predict whether Your Content Will Go Viral 
  • Predict whether Your Content Will Go Viral 
  • What does research tell us about virality? 
  • Sourcing shared counts and content 
  • Exploring the features of shareability 
  • Building a predictive content scoring model 
  1. Use Machine Learning to Forecast the Stock Market 
  • Use Machine Learning to Forecast the Stock Market 
  • Types of market analysis 
  • What does research tell us about the stock market? 
  • How to develop a trading strategy 
  • Building the regression model 
  1. Classifying Images with Convolutional Neural Networks 
  • Classifying Images with Convolutional Neural Networks 
  • Image-feature extraction 
  • Convolutional neural networks 
  • Building a convolutional neural network to classify images in the Zalando Research dataset, using Keras 
  1. Building a Chatbot 
  • Building a Chatbot 
  • The Turing Test 
  • The history of chatbots 
  • The design of chatbots 
  • Building a chatbot 
  • Sequence-to-sequence modeling for chatbots 
  1. Build a Recommendation Engine 
  • Build a Recommendation Engine 
  • Collaborative filtering 
  • Content-based filtering 
  • Hybrid systems 
  • Building a recommendation engine 
  1. What’s Next? 
  • What’s Next? 
  • Summary of the projects 
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