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Course Skill Level:


Course Duration:

3 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:


Who should attend & recommended skills:

Those with Apache knowledge and basic Python and Linux skills

Who should attend & recommended skills

  • Those 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
  • Skill-level: Foundation-level Python Machine Learning skills for Intermediate skilled team members. This is not a basic class.
  • Python skills: Basic (1-2 years’ experience)
  • Linux: Basic (1-2 years’ experience) including familiarity with command-line options such as ls, cd, cp, and su

About this course

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 are 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 will 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 will 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 will 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.

Skills acquired & topics covered

  • Exploiting the power of Python to explore the world of data mining and data analytics
  • Discovering machine learning algorithms to solve complex challenges faced by data scientists today
  • Using Python libraries such as TensorFlow and Keras to create smart cognitive actions for your projects
  • Understanding the important concepts in machine learning and data science
  • Using Python to explore the world of data mining and analytics
  • Scaling up model training using varied data complexities with Apache Spark
  • Delving deep into text and NLP using Python libraries such NLTK and gensim
  • Selecting and building an ML model and evaluate and optimize its performance
  • Implementing ML algorithms from scratch in Python, TensorFlow, and scikit-learn

Course breakdown / modules

  • 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

  • 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

  • Learning without guidance – unsupervised learning
  • Clustering newsgroups data using k-means
  • Discovering underlying topics in newsgroups
  • Topic modeling using NMF
  • Topic modeling using LDA

  • Getting started with classification
  • Exploring Naïve Bayes
  • Classification performance evaluation
  • Model tuning and cross-validation

  • 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

  • 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

  • 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

  • Learning the essentials of Apache Spark
  • Programming in PySpark
  • Learning on massive click logs with Spark
  • Feature engineering on categorical variables with Spark

  • 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

  • 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