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Machine Learning with scikit-learn, 4 Days

  • Course Code: Artificial Intelligence - Machine Learning with scikit-learn
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 4 Days Audience: This course is geared for Python experienced developers, analysts or others who are intending to Use scikit-learn to apply machine learning to real-world problems

Course Snapshot 

  • Duration: 4 days 
  • Skill-level: Foundation-level Machine Learning with scikit-learn 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 Use scikit-learn to apply machine learning to real-world problems 
  • 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, or remote instructor led delivery, or CBT/WBT (by request). 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. Using powerful algorithms and techniques offered by machine learning you can automate any analytical model. This course examines a variety of machine learning models including popular machine learning algorithms such as k-nearest neighbors, logistic regression, naive Bayes, k-means, decision trees, and artificial neural networks. It discusses data preprocessing, hyperparameter optimization, and ensemble methods. You will build systems that classify documents, recognize images, detect ads, and more. You will learn to use scikit-learn’s API to extract features from categorical variables, text and images; evaluate model performance, and develop an intuition for how to improve your model’s performance. By the end of this course, you will master all required concepts of scikit-learn to build efficient models at work to carry out advanced tasks with the practical approach. 

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

  • Master popular machine learning models including k-nearest neighbors, random forests, logistic regression, k-means, naive Bayes, and artificial neural networks 
  • Learn how to build and evaluate performance of efficient models using scikit-learn 
  • Practical guide to master your basics and learn from real life applications of machine learning 

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

  • Review fundamental concepts such as bias and variance 
  • Extract features from categorical variables, text, and images 
  • Predict the values of continuous variables using linear regression and K Nearest Neighbors 
  • Classify documents and images using logistic regression and support vector machines 
  • Create ensembles of estimators using bagging and boosting techniques 
  • Discover hidden structures in data using K-Means clustering 
  • Evaluate the performance of machine learning systems in common tasks 

Audience & Pre-Requisites 

This course is geared for attendees with Python skills who wish to Use scikit-learn to apply machine learning to real-world problems 

Pre-Requisites:  Students should have  

  • Basic to Intermediate IT Skills. Attendees without a programming background like Python may view labs as follow along exercises or team with others to complete them. 
  • 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. The Fundamentals of Machine Learning 
  • The Fundamentals of Machine Learning 
  • Defining machine learning 
  • Learning from experience 
  • Machine learning tasks 
  • Training data, testing data, and validation data 
  • Bias and variance 
  • An introduction to scikit-learn 
  • Installing scikit-learn 
  • Installing pandas, Pillow, NLTK, and matplotlib 
  1. Simple Linear Regression 
  • Simple Linear Regression 
  • Simple linear regression 
  • Evaluating the model 
  1. Classification and Regression with k-Nearest Neighbors 
  • Classification and Regression with k-Nearest Neighbors 
  • K-Nearest Neighbors 
  • Lazy learning and non-parametric models 
  • Classification with KNN 
  • Regression with KNN 
  1. Feature Extraction 
  • Feature Extraction 
  • Extracting features from categorical variables 
  • Standardizing features 
  • Extracting features from text 
  • Extracting features from images 
  1. From Simple Linear Regression to Multiple Linear Regression 
  • From Simple Linear Regression to Multiple Linear Regression 
  • Multiple linear regression 
  • Polynomial regression 
  • Regularization 
  • Applying linear regression 
  • Gradient descent 
  1. From Linear Regression to Logistic Regression 
  • From Linear Regression to Logistic Regression 
  • Binary classification with logistic regression 
  • Spam filtering 
  • Tuning models with grid search 
  • Multi-class classification 
  • Multi-label classification and problem transformation 
  1. Naive Bayes 
  • Naive Bayes 
  • Bayes’ theorem 
  • Generative and discriminative models 
  • Naive Bayes 
  • Naive Bayes with scikit-learn 
  1. Nonlinear Classification and Regression with Decision Trees 
  • Nonlinear Classification and Regression with Decision Trees 
  • Decision trees 
  • Training decision trees 
  • Decision trees with scikit-learn 
  1. From Decision Trees to Random Forests and Other Ensemble Methods 
  • From Decision Trees to Random Forests and Other Ensemble Methods 
  • Bagging 
  • Boosting 
  • Stacking 
  1. The Perceptron 
  • The Perceptron 
  • The perceptron 
  • Limitations of the perceptron 
  1. From the Perceptron to Support Vector Machines 
  • From the Perceptron to Support Vector Machines 
  • Kernels and the kernel trick 
  • Maximum margin classification and support vectors 
  • Classifying characters in scikit-learn 
  1. From the Perceptron to Artificial Neural Networks 
  • From the Perceptron to Artificial Neural Networks 
  • Nonlinear decision boundaries 
  • Feed-forward and feedback ANNs 
  • Multi-layer perceptrons 
  • Training multi-layer perceptrons 
  1. K-means 
  • K-means 
  • Clustering 
  • K-means 
  • Evaluating clusters 
  • Image quantization 
  • Clustering to learn features 
  1. Dimensionality Reduction with Principal Component Analysis 
  • Dimensionality Reduction with Principal Component Analysis 
  • Principal component analysis 
  • Visualizing high-dimensional data with PCA 
  • Face recognition with PCA 
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