Machine Learning with scikit-learn Training primarily focuses on learning and leading real-world problems occuring in the ML Applications. Training includes the implementation of k-nearest neighbors, random forest, logistic regression and artificial neural networks ML Models.


  • Understanding the core concept like bias and variance
  • Feature extraction from categorical variables, images and text
  • Understanding Documents and images using logistic regression methods


  • Discovering hidden data structure in data using K-means clustering
  • Evaluation of performance of ML systems in common tasks
  • Integration of Computer Science and statistics for building smart and efficient models
Duration: 2 Days

Course Content:
  1. Basic Overview of Machine Learning

    • Introduction to Machine Learning
    • Understanding Machine Learning Tasks
    • Concept of Training Data, Testing Data and Data Validation
    • Understanding Bias and variance
    • Basic Overview of scikit-learn
  2. Installation of scikit-learn

    • Installation using pip
    • Installation on Windows Machine
    • Installation on Ubuntu
    • Installation on MacOS
    • Installation of Anaconda
    • Installation Verification Process
  3. Basic Installation of Pandas, Pillow, NLTK and matplotlib
  4. Basic Overview of Simple Linear Regression
  5. Classification Scenario with k-Nearest neighbors
  6. Regression Scenario with k-Nearest Neighbors

    • Lazy Learning and Non-parametric models
    • KNN Classification methods
    • KNN Regression Methods
  7. Features Extraction Concepts

    • Features extraction from categorical variables
    • More on standardized features
    • Features extraction from text
    • Features extraction from images
  1. Migration from Simple to Multiple Linear Regression

    • Understanding Multiple linear regression
    • Understanding Polynomial Regression
    • Concept of Regularization
    • Apply for Linear Regression
    • Introducing Gradient Descent
  2. Migration from Linear to Logistic Regression

    • Using Binary Classification with Logistic Regression
    • Concept of Spam Filtering
    • Using Tuning Models with Grid Search
    • Understanding Multi-class Classification
    • Understanding Multi-label Classification
  3. Basic overview of Naiye Bayes
  4. Understanding Nonlinear Classification and Regression with Decision Trees

    • Understanding Decision Trees
    • Concept on Training Decision Trees
    • Using Decision Trees with scikit-learn
  5. Migration from Decision Trees to Random Forest
  6. Understanding the concept of perceptron
  7. Migration from Perception to Support Vector Machines

    • Using Kernels and kernel trick
    • Understanding Maximum Margin Classification and Support Vectors
    • Characters Classification in scikit-learn
  8. Migration from Perception to Artificial Neural Networks

    • Overview of Nonlinear decision boundaries
    • Using Feed-forward and feedback ANNs
    • Understanding Multi-layer perceptrons
    • Concept of Training multi-layer perceptrons
  9. Understanding the concept of K-means