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Python Machine Learning By Example

  • Course Code: Artificial Intelligence - Python Machine Learning By Example
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 3 Days Audience: This course is geared for those who wants to Grasp machine learning concepts, techniques, and algorithms with the help of real-world examples using Python libraries such as TensorFlow and scikit-learn

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 Grasp machine learning concepts, techniques, and algorithms with the help of real-world examples using Python libraries such as TensorFlow and scikit-learn 
  • 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. 

The surge in interest in machine learning (ML) is due to the fact that it revolutionizes automation by learning patterns in data and using them to make predictions and decisions. If you’re interested in ML, this course will serve as your entry point to ML. Python Machine Learning By Example begins with an introduction to important ML concepts and implementations using Python libraries. Each lesson of the course walks you through an industry adopted application. You’ll implement ML techniques in areas such as exploratory data analysis, feature engineering, and natural language processing (NLP) in a clear and easy-to-follow way. With the help of this extended and updated edition, you’ll understand how to tackle data-driven problems and implement your solutions with the powerful yet simple Python language and popular Python packages and tools such as TensorFlow, scikit-learn, gensim, and Keras. To aid your understanding of popular ML algorithms, the course covers interesting and easy-to-follow examples such as news topic modeling and classification, spam email detection, stock price forecasting, and more. By the end of the course, you’ll have put together a broad picture of the ML ecosystem and will be well-versed with the best practices of applying ML techniques to make the most out of new opportunities. 

Working in a hands-on learning environment, led by our Python Machine Learning instructor, students will learn about and explore: 

  • Exploit the power of Python to explore the world of data mining and data analytics 
  • Discover machine learning algorithms to solve complex challenges faced by data scientists today 
  • Use Python libraries such as TensorFlow and Keras to create smart cognitive actions for your projects 

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

  • Understand the important concepts in machine learning and data science 
  • Use Python to explore the world of data mining and analytics 
  • Scale up model training using varied data complexities with Apache Spark 
  • Delve deep into text and NLP using Python libraries such NLTK and gensim 
  • Select and build an ML model and evaluate and optimize its performance 
  • Implement ML algorithms from scratch in Python, TensorFlow, and scikit-learn 

Audience & Pre-Requisites 

This course is geared for attendees with Apache knowledge who wish to Grasp machine learning concepts, techniques, and algorithms with the help of real-world examples using Python libraries such as TensorFlow and scikit-learn. 

Pre-Requisites:  Students should have  

  • Basic to python Skills. 
  • Good foundational mathematics or logic skills 
  • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su 

Course Agenda / Topics 

  1. Getting Started with Machine Learning and Python 
  • Getting Started with Machine Learning and Python 
  • Defining machine learning and why we need it 
  • A very high-level overview of machine learning technology 
  • Core of machine learning – generalizing with data 
  • Preprocessing, exploration, and feature engineering 
  • Combining models 
  • Installing software and setting up 
  1. Exploring the 20 Newsgroups Dataset with Text Analysis Techniques 
  • Exploring the 20 Newsgroups Dataset with Text Analysis Techniques 
  • How computers understand language – NLP 
  • Picking up NLP basics while touring popular NLP libraries 
  • Getting the newsgroups data 
  • Exploring the newsgroups data 
  • Thinking about features for text data 
  • Visualizing the newsgroups data with t-SNE 
  1. Mining the 20 Newsgroups Dataset with Clustering and Topic Modeling Algorithms 
  • Mining the 20 Newsgroups Dataset with Clustering and Topic Modeling Algorithms 
  • Learning without guidance – unsupervised learning 
  • Clustering newsgroups data using k-means 
  • Discovering underlying topics in newsgroups 
  • Topic modeling using NMF 
  • Topic modeling using LDA 
  1. Detecting Spam Email with Naive Bayes 
  • Detecting Spam Email with Naive Bayes 
  • Getting started with classification 
  • Exploring Naïve Bayes 
  • Classification performance evaluation 
  • Model tuning and cross-validation 
  1. Classifying Newsgroup Topics with Support Vector Machines 
  • Classifying Newsgroup Topics with Support Vector Machines 
  • Finding separating boundary with support vector machines 
  • Classifying newsgroup topics with SVMs 
  • More example – fetal state classification on cardiotocography 
  • A further example – breast cancer classification using SVM with TensorFlow 
  1. Predicting Online Ad Click-Through with Tree-Based Algorithms 
  • Predicting Online Ad Click-Through with Tree-Based Algorithms 
  • Brief overview of advertising click-through prediction 
  • Getting started with two types of data – numerical and categorical 
  • Exploring decision tree from root to leaves 
  • Implementing a decision tree from scratch 
  • Predicting ad click-through with decision tree 
  • Ensembling decision trees – random forest 
  1. Predicting Online Ad Click-Through with Logistic Regression 
  • Predicting Online Ad Click-Through with Logistic Regression 
  • Converting categorical features to numerical – one-hot encoding and ordinal encoding 
  • Classifying data with logistic regression 
  • Training a logistic regression model 
  • Training on large datasets with online learning 
  • Handling multiclass classification 
  • Implementing logistic regression using TensorFlow 
  • Feature selection using random forest 
  1. Scaling Up Prediction to Terabyte Click Logs 
  • Scaling Up Prediction to Terabyte Click Logs 
  • Learning the essentials of Apache Spark 
  • Programming in PySpark 
  • Learning on massive click logs with Spark 
  • Feature engineering on categorical variables with Spark 
  1. Stock Price Prediction with Regression Algorithms 
  • Stock Price Prediction with Regression Algorithms 
  • Brief overview of the stock market and stock prices 
  • What is regression? 
  • Mining stock price data 
  • Estimating with linear regression 
  • Estimating with decision tree regression 
  • Estimating with support vector regression 
  • Estimating with neural networks 
  • Evaluating regression performance 
  • Predicting stock price with four regression algorithms 
  1. Machine Learning Best Practices 
  • Machine Learning Best Practices 
  • Machine learning solution workflow 
  • Best practices in the data preparation stage 
  • Best practices in the training sets generation stage 
  • Best practices in the model training, evaluation, and selection stage 
  • Best practices in the deployment and monitoring stage 
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