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Python Machine Learning

  • Course Code: Artificial Intelligence - Python Machine Learning.
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 5 Days Audience: This course is geared for Python experienced developers, analysts or others who are intending to Apply machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning.

Course Snapshot 

  • Duration: 5 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 Python experienced developers, analysts or others who are intending to Apply machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning.  
  • 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. 

Python Machine Learning, This Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you’ll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and working examples, the course covers all the essential machine learning techniques in depth. While some course teach you only to follow instructions, with this machine learning course, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself. Updated for TensorFlow 2.0, this new edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It’s also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this course also explores a subfield of natural language processing (NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents. This course is your companion to machine learning with Python, whether you’re a Python developer new to machine learning or want to deepen your knowledge of the latest developments. 

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

  • Widely acclaimed Python machine learning course 
  • Clear and intuitive explanations take you deep into the theory and practice of Python machine learning 
  • Fully updated and expanded to cover TensorFlow 2, Generative Adversarial Network models, reinforcement learning, and best practices 

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

  • Master the frameworks, models, and techniques that enable machines to ‘learn’ from data 
  • Use scikit-learn for machine learning and TensorFlow for deep learning 
  • Apply machine learning to image classification, sentiment analysis, intelligent web applications, and more 
  • Build and train neural networks, GANs, and other models 
  • Discover best practices for evaluating and tuning models 
  • Predict continuous target outcomes using regression analysis 
  • Dig deeper into textual and social media data using sentiment analysis 

Audience & Pre-Requisites 

This course is geared for attendees with Python skills who wish to Apply machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning. 

Pre-Requisites:  Students should have  

  • Basic to Intermediate IT 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. Giving Computers the Ability to Learn from Data 
  • Giving Computers the Ability to Learn from Data 
  • Building intelligent machines to transform data into knowledge 
  • The three different types of machine learning 
  • Introduction to the basic terminology and notations 
  • A roadmap for building machine learning systems 
  • Using Python for machine learning 
  1. Training Simple Machine Learning Algorithms for Classification 
  • Training Simple Machine Learning Algorithms for Classification 
  • Artificial neurons – a brief glimpse into the early history of machine learning 
  • Implementing a perceptron learning algorithm in Python 
  • Adaptive linear neurons and the convergence of learning 
  1. A Tour of Machine Learning Classifiers Using scikit-learn 
  • A Tour of Machine Learning Classifiers Using scikit-learn 
  • Choosing a classification algorithm 
  • First steps with scikit-learn – training a perceptron 
  • Modeling class probabilities via logistic regression 
  • Maximum margin classification with support vector machines 
  • Solving nonlinear problems using a kernel SVM 
  • Decision tree learning 
  • K-nearest neighbors – a lazy learning algorithm 
  1. Building Good Training Datasets – Data Preprocessing 
  • Building Good Training Datasets – Data Preprocessing 
  • Dealing with missing data 
  • Handling categorical data 
  • Partitioning a dataset into separate training and test datasets 
  • Bringing features onto the same scale 
  • Selecting meaningful features 
  • Assessing feature importance with random forests 
  1. Compressing Data via Dimensionality Reduction 
  • Compressing Data via Dimensionality Reduction 
  • Unsupervised dimensionality reduction via principal component analysis 
  • Supervised data compression via linear discriminant analysis 
  • Using kernel principal component analysis for nonlinear mappings 
  1. Learning Best Practices for Model Evaluation and Hyperparameter Tuning 
  • Learning Best Practices for Model Evaluation and Hyperparameter Tuning 
  • Streamlining workflows with pipelines 
  • Using k-fold cross-validation to assess model performance 
  • Debugging algorithms with learning and validation curves 
  • Fine-tuning machine learning models via grid search 
  • Looking at different performance evaluation metrics 
  1. Combining Different Models for Ensemble Learning 
  • Combining Different Models for Ensemble Learning 
  • Learning with ensembles 
  • Combining classifiers via majority vote 
  • Bagging – building an ensemble of classifiers from bootstrap samples 
  • Leveraging weak learners via adaptive boosting 
  1. Applying Machine Learning to Sentiment Analysis 
  • Applying Machine Learning to Sentiment Analysis 
  • Preparing the IMDb movie review data for text processing 
  • Introducing the bag-of-words model 
  • Training a logistic regression model for document classification 
  • Working with bigger data – online algorithms and out-of-core learning 
  • Topic modeling with Latent Dirichlet Allocation 
  1. Embedding a Machine Learning Model into a Web Application 
  • Embedding a Machine Learning Model into a Web Application 
  • Serializing fitted scikit-learn estimators 
  • Setting up an SQLite database for data storage 
  • Developing a web application with Flask 
  • Turning the movie review classifier into a web application 
  • Deploying the web application to a public server 
  1. Predicting Continuous Target Variables with Regression Analysis 
  • Predicting Continuous Target Variables with Regression Analysis 
  • Introducing linear regression 
  • Exploring the Housing dataset 
  • Implementing an ordinary least squares linear regression model 
  • Fitting a robust regression model using RANSAC 
  • Evaluating the performance of linear regression models 
  • Using regularized methods for regression 
  • Turning a linear regression model into a curve – polynomial regression 
  • Dealing with nonlinear relationships using random forests 
  1. Working with Unlabeled Data – Clustering Analysis 
  • Working with Unlabeled Data – Clustering Analysis 
  • Grouping objects by similarity using k-means 
  • Organizing clusters as a hierarchical tree 
  • Locating regions of high density via DBSCAN 
  1. Implementing a Multilayer Artificial Neural Network from Scratch 
  • Implementing a Multilayer Artificial Neural Network from Scratch 
  • Modeling complex functions with artificial neural networks 
  • Classifying handwritten digits 
  • Training an artificial neural network 
  • About the convergence in neural networks 
  • A few last words about the neural network implementation 
  1. Parallelizing Neural Network Training with TensorFlow 
  • Parallelizing Neural Network Training with TensorFlow 
  • TensorFlow and training performance 
  • First steps with TensorFlow 
  • Building input pipelines using tf.data – the TensorFlow Dataset API 
  • Building an NN model in TensorFlow 
  • Choosing activation functions for multilayer neural networks 
  1. Going Deeper – The Mechanics of TensorFlow 
  • Going Deeper – The Mechanics of TensorFlow 
  • The key features of TensorFlow 
  • TensorFlow’s computation graphs: migrating to TensorFlow v2 
  • TensorFlow Variable objects for storing and updating model parameters 
  • Computing gradients via automatic differentiation and GradientTape 
  • Simplifying implementations of common architectures via the Keras API 
  • TensorFlow Estimators 
  1. Classifying Images with Deep Convolutional Neural Networks 
  • Classifying Images with Deep Convolutional Neural Networks 
  • The building blocks of CNNs 
  • Putting everything together – implementing a CNN 
  • Implementing a deep CNN using TensorFlow 
  • Gender classification from face images using a CNN 
  1. Modeling Sequential Data Using Recurrent Neural Networks 
  • Modeling Sequential Data Using Recurrent Neural Networks 
  • Introducing sequential data 
  • RNNs for modeling sequences 
  • Implementing RNNs for sequence modeling in TensorFlow 
  • Understanding language with the Transformer model 
  1. Generative Adversarial Networks for Synthesizing New Data 
  • Generative Adversarial Networks for Synthesizing New Data 
  • Introducing generative adversarial networks 
  • Implementing a GAN from scratch 
  • Improving the quality of synthesized images using a convolutional and Wasserstein GAN 
  • Other GAN applications 
  1. Reinforcement Learning for Decision Making in Complex Environments 
  • Reinforcement Learning for Decision Making in Complex Environments 
  • Introduction – learning from experience 
  • The theoretical foundations of RL 
  • Reinforcement learning algorithms 
  • Implementing our first RL algorithm 
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