EncartaLabs

Scikit Learn

Scikit Learn is the de facto Machine Learning package for Python. It consists of classification, regression, clustering, dimension reduction, model selection, and many data preprocessing functionalities. You can do many supervised and unsupervised machine learning with Scikit Learn.

This Machine Learning with Scikit-Learn training course aims to equip you with fundamental machine learning knowledge using such as classification algorithms and classification metrics, ensemble methods, regression and regularization, K-Means and Hierarchical Clustering and, feature reduction with PCA.

In Scikit Learn - Advanced training course delegates will learn many useful utility functions such as model selection, hyper parameter tuning, feature extraction, data preprocessing, etc.

By attending Machine Learning with Scikit Learn workshop, delegates will learn:

  • Supervised Learning vs Unsupervised Learning
  • Analysing Classification Models with F1 Score and AUC
  • Multivariate Linear regression
  • Ridge and Lasso Regularization to reduce overfitting
  • Silhouette Analysis and Dendrogram for Clustering
  • Dimension Reduction with PCA

By attending Scikit Learn - Advanced workshop, delegates will learn:

  • Feature Extraction
  • Pipelines
  • Discussing Ensemble Methods in Detail
  • Hyper Parameter Tunning
  • Model Metrics

For Machine Learning with Scikit Learn

  • Knowledge of Basic Python

For Scikit Learn - Advanced Knowledge of:

  • Basic Python
  • Basic Scikit-Learn
  • Basic Machine Learning

  • Data Analysts
  • Data Scientists
  • Financial Analytics
  • Engineers
  • Digital Marketers

COURSE AGENDA

Machine Learning with Scikit Learn
(Duration : 4 Days)

1

Overview of Machine Learning and Scikit Learn

  • Introduction to Machine Learning
  • Supervised vs Unsupervised Learnings
  • Machine Learning Applications and Case Studies
  • What is Scikit Learn
  • Installing Scikit-Learn
2

Classification

  • What is Classification
  • Classification Algorithms
  • Classification Workflow
  • Confusion Matrix
  • Binary Classification Metrics
  • ROC and AUC
3

Regression

  • What is Regression?
  • Regression Algorithms
  • Regression Workflow
  • Regression Metrics
  • Overfitting and Regularizations
4

Clustering

  • What is Clustering
  • K-Means Clustering
  • Silhouette Analysis
  • Dendrogram and Hierarchical Clustering
5

Principal Component Analysis

  • Curse of Dimensionality Issue
  • What is Principal Component Analysis (PCA)
  • Feature Reduction with PCA
Scikit Learn - Advanced
(Duration : 3 Days)

1

Text Feature Vectorization

  • Bag of Words Vectorization
  • TF-IDF Vectorization
  • Hash Vectorization
2

Model Evaluation

  • Confusion Matrix
  • ROC & AUC
  • K-Fold Cross Validation
3

Pipelines

  • Pipelining Estimators
  • Chain Multiple Pipelines
  • Applications of Pipelines
4

Hyper Parameter Tuning

  • Bias Variance Tradeoff
  • Exhaustive Grid Search
  • Randomized Search
  • Search on Pipeline Estimator
5

Ensemble Estimators

  • Ensemble Methods
  • Bagging Meta Estimators
  • RandomForest
  • AdaBoost
  • Gradient Boosting

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.

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