Open Source/Machine Learning with Python

Machine Learning with Python Certification Training Course Overview

The Machine Learning with Python for Beginner training course will give you a detailed overview on developing machine learning using python covering the topics like regression, Naive Bayes, Clustering, tensor flow etc.

Who should do Machine Learning with Python ( Beginner ) training?

  • Anyone interested in Machine learning
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Machine Learning with Python (40 Hours) Download Course Contents

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Group Training 3050 Per Participant
07 - 11 Feb 09:00 AM - 05:00 PM CST
(8 Hours/Day)
07 - 11 Mar 09:00 AM - 05:00 PM CST
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1-on-1 Training 6100 3500+
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Course Modules

Module 1: Introduction
  • Why Machine Learning?
  • Why Python?
  • Scikit-learn
  • Essential Libraries and Tools
  • Python 2 Versus Python 3
  • Versions Used in this Book
  • A First Application: Classifying Iris Species
  • Summary and Outlook
Module 2: Supervised Learning
  • Classification and Regression
  • Generalization, Overfitting, and Underfitting
  • Supervised Machine Learning Algorithms
  • Uncertainty Estimates from Classifiers
  • Summary and Outlook
Module 3: Unsupervised Learning and Preprocessing
  • Types of Unsupervised Learning
  • Challenges in Unsupervised Learning
  • Preprocessing and Scaling
  • Dimensionality Reduction, Feature Extraction, and Manifold Learning
  • Clustering
  • Summary and Outlook
Module 4: Representing Data and Engineering Features
  • Categorical Variables
  • Binning, Discretization, Linear Models, and Trees
  • Interactions and Polynomials
  • Univariate Nonlinear Transformations
  • Automatic Feature Selection
  • Utilizing Expert Knowledge
  • Summary and Outlook
Module 5: Model Evaluation and Improvement
  • Cross-Validation
  • Grid Search
  • Evaluation Metrics and Scoring
  • Summary and Outlook
Module 6: Algorithm Chains and Pipelines
  • Parameter Selection with Preprocessing
  • Building Pipelines
  • Using Pipelines in Grid Searches
  • The General Pipeline Interface
  • Grid-Searching Preprocessing Steps and Model Parameters
  • Grid-Searching Which Model To Use
  • Summary and Outlook
Module 7: Working with Text Data
  • Types of Data Represented as Strings
  • Example Application: Sentiment Analysis of Movie Reviews
  • Representing Text Data as a Bag of Words
  • Stopwords
  • Rescaling the Data with tf–idf
  • Investigating Model Coefficients
  • Bag-of-Words with More Than One Word (n-Grams)
  • Advanced Tokenization, Stemming, and Lemmatization
  • Topic Modeling and Document Clustering
  • Summary and Outlook
Module 8: Working with Text Data
  • Approaching a Machine Learning Problem
  • From Prototype to Production
  • Testing Production Systems
  • Building Your Own Estimator
  • Where to Go from Here
  • Conclusion
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Course Prerequisites
  • Basic Computer Knowledge.

On completion of this training, you will know:

  • Overview of Python Programming Language
  • Regression
  • K-Nearest Neighbors
  • Naive Bayes
  • Neural Networks
  • Clustering
  • Network Analysis
  • Classification
  • Deep Learning using Tensor Flow

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