Machine Learning With Python Training


Machine Learning is a global phenomenon, it is the application of artificial giving systems the ability to automatically learn and improve the experience without having to program aspects explicitly. Python considered the preferred language for Machine Learning is used extensively in this course to cover various concepts and drive participants through the depths and various use cases of Machine Learning.


At the end of this training program, participants will have a depth of understanding of

  • Python for Machine Learning
  • Various Machine Learning concepts & algorithms
  • Working with Data for Machine Learning

3 Days

Basic understanding of Python Programming is good to have.

  • Basic syntax
  • Data structures in Python
  • Functions
  • Indexing, Data Processing using Numpy Arrays and Pandas
  • Mathematical computing basics
  • Basic statistics
  • File Input and Output
  • Getting Started with Dataframes
  • Data Acquisition (Import & Export)
  • Selection and Filtering
  • Combining and Merging Data Frames
  • Removing Duplicates & String Manipulation
  • Regression Problem Analysis
  • Mathematical modelling of Regression Model
  • Gradient Descent Algorithm
  • Use cases
  • Model Specification
  • L1 & L2 Regularization
  • Building simple Univariate Linear Regression Model
  • Multivariate Regression Model
  • KNN Theory
  • KNN with Python
  • KNN Exercise with real data
  • Forming a Decision Tree
  • Components of Decision Tree
  • Mathematics of Decision Tree
  • Decision Tree Evaluation for use cases
  • Random Forest Mathematics
  • Examples & use cases using Random Forests
  • Bias Variance Tradeoffs
  • Bagging
  • Boosting
  • Bootstrapping
  • Ensemble models with real world data
  • Concept and Working Principle
  • Mathematical Modelling
  • Optimization Function Formation
  • The Kernel Method and Nonlinear Hyperplanes
  • Use Cases & Programming SVM using Python
  • Hierarchical Clustering
  • K Means Clustering
  • Use Cases for K Means Clustering
  • Programming for K Means using Python
  • Cluster Size Optimization vs Definition Optimization
  • Assumptions
  • Reason for the Logit Transform
  • Logit Transformation
  • Hypothesis
  • Variable and Model Significance
  • Maximum Likelihood Concept
  • Log Odds and Interpretation