Machine Learning with Python

Live Online (VILT) & Classroom Corporate Training Course

Python for Machine Learning is a programming language that helps build algorithms for smart and intelligent machines that work without human intervention and continuously learn, evolve, and improve by taking in new data.
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Overview

In this course, participants will learn all the concepts of Python and ML along with Supervised and unsupervised learning, understand how Statistical Modeling relates to Machine Learning, and learn to build algorithms with practical hands-on exercises.

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Objectives

At the end of Machine Learning with Python training course, participants will

  • Learn about the various libraries offered by Python to manipulate, preprocess, and visualize data.
  • Learn the basics of Machine Learning including an introduction to Supervised and Unsupervised Learning.
  • Learn to use optimization techniques to find the minimum error in your Machine Learning model.
  • Learn about Linear and Logistic Regression, KNN Classification and Bayesian Classifiers.
  • Learn about K-means Clustering and Hierarchical Clustering to understand Unsupervised Learning.
  • Learn to use multiple learning algorithms to obtain better predictive performance.
  • Understand Neural Networks and apply them to classify image and perform sentiment analysis.
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Prerequisites

  • Elementary programming knowledge
  • Familiarity with statistics
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Course Outline

  • Statistical analysis concepts
  • Descriptive statistics
  • Introduction to probability and Bayes theorem
  • Probability distributions
  • Hypothesis testing & scores

  • Python Overview
  • Pandas for Pre-Processing and Exploratory Data Analysis
  • Numpy for Statistical Analysis
  • Matplotlib & Seaborn for Data Visualization
  • Scikit Learn

  • Machine Learning Modelling Flow
  • How to treat Data in ML
  • Types of Machine Learning
  • Performance Measures
  • Bias-Variance Trade-Off
  • Overfitting & Underfitting

  • Maxima and Minima
  • Cost Function
  • Learning Rate
  • Optimization Techniques

  • Linear Regression
  • Case Study
  • Logistic Regression
  • Case Study
  • K-NN Classification
  • Case Study
  • Naive Bayesian classifiers
  • Case Study
  • SVM – Support Vector Machines
  • Case Study

  • Clustering approaches
  • K Means clustering
  • Hierarchical clustering
  • Case Study

  • Decision Trees
  • Case Study
  • Introduction to Ensemble Learning
  • Different Ensemble Learning Techniques
  • Bagging
  • Boosting
  • Random Forests
  • Case Study
  • PCA (Principal Component Analysis) and Its Applications
  • Case Study

  • Introduction to Recommendation Systems
  • Types of Recommendation Techniques
  • Collaborative Filtering
  • Content based Filtering
  • Hybrid RS
  • Performance measurement
  • Case Study
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