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Course Skill Level:


Course Duration:

4 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:


Who should attend & recommended skills:

Those with Python experience and basic IT and Linux skills

Who should attend & recommended skills

  • Python experienced developers, analysts or others with Python skills intending to use scikit-learn to apply machine learning to real-world problems.
  • Skill-level: Foundation-level Machine Learning with scikit-learn skills for Intermediate skilled team members. This is not a basic class.
  • IT Skills: Basic to Intermediate (2+ years’ experience)
  • Linux: Basic (1-2 years’ experience), including familiarity with command-line options such as ls, cd, cp, and su
  • Attendees without a programming background like Python may view labs as follow along exercises or team with others to complete them

About this course

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 API to extract features from categorical variables, text and images; evaluate model performance, and develop an intuition for how to improve your models 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.

Skills acquired & topics covered

  • Mastering popular machine learning models including k-nearest neighbors, random forests, logistic regression, k-means, naive Bayes, and artificial neural networks
  • How to build and evaluate performance of efficient models using scikit-learn
  • A practical guide to master your basics and learn from real life applications of machine learning
  • Reviewing fundamental concepts such as bias and variance
  • Extracting features from categorical variables, text, and images
  • Predicting the values of continuous variables using linear regression and K Nearest Neighbors
  • Classifying documents and images using logistic regression and support vector machines
  • Creating ensembles of estimators using bagging and boosting techniques
  • Discovering hidden structures in data using K-Means clustering
  • Evaluating the performance of machine learning systems in common tasks

Course breakdown / modules

  • 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

  • Evaluating the model

  • K-Nearest Neighbors
  • Lazy learning and non-parametric models
  • Classification with KNN
  • Regression with KNN

  • Extracting features from categorical variables
  • Standardizing features
  • Extracting features from text
  • Extracting features from images

  • Multiple linear regression
  • Polynomial regression
  • Regularization
  • Applying linear regression
  • Gradient descent

  • Binary classification with logistic regression
  • Spam filtering
  • Tuning models with grid search
  • Multi-class classification
  • Multi-label classification and problem transformation

  • Bayes’ theorem
  • Generative and discriminative models
  • Naive Bayes
  • Naive Bayes with scikit-learn

  • Decision trees
  • Training decision trees
  • Decision trees with scikit-learn

  • Bagging
  • Boosting
  • Stacking

  • Limitations of the perceptron

  • Kernels and the kernel trick
  • Maximum margin classification and support vectors
  • Classifying characters in scikit-learn

  • Nonlinear decision boundaries
  • Feed-forward and feedback ANNs
  • Multi-layer perceptrons
  • Training multi-layer perceptrons

  • Clustering
  • K-means
  • Evaluating clusters
  • Image quantization
  • Clustering to learn features

  • Principal component analysis
  • Visualizing high-dimensional data with PCA
  • Face recognition with PCA