Essential Mathematics for Machine Learning



This course is mostly a non-coding course, which is a MUST for ALL people desirous of learning Machine Learning from the mathematical and algorithmic point of view. We will focus more on the theoretical aspects of the algorithms, parameters, and hand-calculations will be done on dummy data step-by-step. In some cases, to automate the calculations, we will be using MS Excel. Some Basic Implementation will be taught using Scipy and statsmodels packages of Python, but majorly it's a non-coding course. Remaining algorithms (esp. the unsupervised ones) I'll cover during my follow-up Machine Learning in Python course (another 40 hours)

Course Highlights:
  • Clear algorithm explanations that help you to understand the principles that underlie each technique.
  • The step-by-step algorithm workout on black-board to show you exactly how each model learns.
  •  Real hand-worked numeric examples. 
Course Contents:

The Following algorithms are tentatively planned to be discussed and detailed tutorials/examples will be worked out in the class.

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General Introduction:
  •  Parametric and Non-parametric Machine Learning Algorithms
  •  The Supervised, Unsupervised and semi-supervised Learning
  •  The Bias-Variance Trade-off
  •  Overfitting and Underfitting
Essential Mathematics for Machine Learning - Part – A: Basic Probability
  •  Basic Definitions
  •  Even & odds of an event
  •  Bayes Theorem & applications
  •  Probability Distribution Functions
Essential Mathematics for Machine Learning - Part – B: Basic Statistics
  •  Mean, Mode, Median
  •  Standard Deviation, Variance
  •  Correlation and Correlation-coefficient
  •  Standard Statistical Distributions
  • Inferential and Descriptive Statistics
  • Hypothesis Testing
  • Chi-square Tests
Essential Mathematics for Machine Learning - Part – C: Linear Algebra
  • Identity Matrix and Diagonal Matrices
  • Transpose, Inverse, Trace, Norms and Determinant of Matrices
  • Symmetric & Orthogonal Matrices
  • Linear Independence and Rank
  • Eigenvalues and Eigenvectors of Symmetric Matrices
  • Matrix Multiplication
  • Operations and Properties
Essential Mathematics for Machine Learning - Part – D: Matrix Calculus
  • Gradients and Hessians of Quadratic and Linear Functions
  • Least Squares
  • Gradients of the Determinant
  • Eigenvalues as Optimization
Essential Mathematics for Machine Learning - Part – E: Machine Learning Algorithms
  • Mathematical Formulation for Loss functions in ML,
  • Ordinary Least Square Solution to Linear Regression Problem of one variable,
  • OLS in vector form
  • Algorithms like Gradient Descent (and its variants)
  • Maths behind the ML algorithms like Linear & Logistic Regressions,
  • Regularization algorithms (Ridge, LASSO, ElasticNet)
  • Naive Bayes and Gaussian Naive Bayes algorithms,
  • Decision Trees and Random Forests (with Gini Index and Information Gain calculations),
  • K-Nearest Neighbors (calculation of diff distance-metrics),
  • Support Vector Machines (slack variables, soft & hard margin classifiers, Kernel Trick, and parameters optimization),
  • Linear Discriminant Analysis (LDA) & Dimensionality reduction using Principal Component Analysis (PCA).
Training Schedule: Saturday - Sunday Weekend Batch (5 Weekends, 40 Hours)
Dates Starting August 17th onwards (Confirmed).
Saturday Time :  10:00 AM - 2:00 PM IST
Sunday Time :  09:00 AM - 1:00 PM IST

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WhatApp: 8169 543 099.