Machine Learning With Python

( Duration: 5 Days )

This Machine Learning With Python training course explores core skills and concepts in machine learning practices. You’ll learn machine learning concepts and algorithms from scratch. This includes the foundations, applicability and limitations, and an exploration of implementation and use.

By attending Machine Learning With Python workshop, delegates will learn:

  • Popular machine learning algorithms, their applicability and limitations
  • Practical application of these methods in a machine learning environment
  • Practical algorithm use cases and limitations
  • Machine Learning with Python

  • Basic Python skills
  • Good foundational mathematics in linear algebra and probability
  • Basic Linux skills
  • Familiarity with command line options
  • Experienced Developers, Data Analysts, Data Scientists, and others interested in learning about machine learning algorithms and core concepts leveraging Python.



Machine Learning (ML) Overview

  • Machine Learning landscape
  • Machine Learning applications
  • Understanding ML algorithms and models (supervised and unsupervised)

Machine Learning Environment

  • Introduction to Jupyter notebooks

Machine Learning Concepts

  • Statistics Primer
  • Covariance, Correlation, and Covariance Matrix
  • Errors, Residuals
  • Overfitting/Underfitting
  • Cross validation and bootstrapping
  • Confusion Matrix
  • ROC curve and Area Under Curve (AUC)

Feature Engineering (FE)

  • Preparing data for ML
  • Extracting features and enhancing data
  • Data cleanup
  • Visualizing Data
  • Exercise: data cleanup
  • Exercise: visualizing data
  • Linear regression
  • Simple Linear Regression
  • Multiple Linear Regression
  • Running LR
  • Evaluating LR model performance

Logistic Regression

  • Understanding Logistic Regression
  • Calculating Logistic Regression
  • Evaluating model performance

Classification: SVM (Supervised Vector Machines)

  • SVM concepts and theory
  • SVM with kernel

Classification: Decision Trees and Random Forests

  • Theory behind trees
  • Classification and Regression Trees (CART)
  • Random Forest concepts

Classification: Naive Bayes

  • Theory behind Naive Bayes
  • Running NB algorithm
  • Evaluating NB model

Clustering (K-Means)

  • Theory behind K-Means
  • Running K-Means algorithm
  • Estimating the performance

Principal Component Analysis (PCA)

  • Understanding PCA concepts
  • PCA applications
  • Running a PCA algorithm
  • Evaluating results

Recommendation (collaborative filtering)

  • Recommender systems overview
  • Collaborative Filtering concepts

Encarta Labs Advantage

  • One Stop Corporate Training Solution Providers for over 4,000 Modules on a variety of subjects
  • All courses are delivered by Industry Veterans
  • Get jumpstarted from newbie to production ready in a matter of few days
  • Trained more than 50,000 Corporate executives across the Globe
  • All our trainings are conducted in workshop mode with more focus on hands-on sessions

View our other course offerings by visiting http://encartalabs.com/course-catalogue-all.php

Contact us for delivering this course as a public/open-house workshop/online training for a group of 10+ candidates.