Python for Machine Learning and Advanced Analytics

Who Should Attend?

IT PROFESSIONALS, DATA ANALYST and PROFESSIONALS with basic knowledge of programming

Duration: 4 Days

This course will introduce the learner to applied data analytics with Python, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and the introduction to the scikit learn toolkit.

  • Identify difference between supervised (classification) and unsupervised (clustering) technique
  • Identify which technique they need to apply for a particular dataset and need
  • Engineer features to meet the machine learning needs
  • Write python code to carry out an analysis

Module 1: Introduction

  • Introduction to Machine Learning
  • Introduction to Scikit Learn Package
  • Regression vs Classification

Module 2: Supervised Learning

  • K Nearest Neighbour (kNN)
  • Naïve Bayes
  • Logistic Regression
  • Support Vector Machine (SVM)
  • Decision Tree & Random Forest
  • Hyperparameter Model Tuning,
    Regularization Ridge and Lasso

Module 3: Unsupervised Learning

  • Clustering

Module 4: Advanced Analytics

  • Cross Validation
  • Model Evaluation and Selection
  • Select, Manipulate and Analyze Data
  • Introduction to Ensemble Models

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Drop us your entry if you are interested to join this course.