Data Structure

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COURSE FEATURES

10 quizzes

20 assignments

.

50+ hours of videos

24x7 doubt support

COURSE SYLLABUS

  • Linear Algebra and Probability
  • Linear Algebra Review and Reference
  • Matrices and Vectors
  • Addition and Scalar Multiplication
  • Matrix Vector Multiplication
  • Matrix Matrix Multiplication
  • Matrix Multiplication Properties
  • Inverse and Transpose
  • Weighted Least Squares. Logistic Regression. Netwon’s Method Perceptron. Exponential Family. Generalized Linear Models
  • How to get the dataset
  • Missing Data
  • Categorical Data
  • Splitting the Dataset into the Training set and Test set
  • Feature Scaling
  • Simple Linear Regression in Python
  • Multiple Linear Regression
  • What is the P-Value?
  • Multiple Linear Regression Intuition
  • Multiple Linear Regression in Python
  • Polynomial Regression Intuition
  • Polynomial Regression in Python
  • Python Regression Template
  • Support Vector Regression (SVR)
  • Decision Tree Regression
  • Random Forest Regression
  • Evaluating Regression Models Performance
  • Logistic Regression
  • K-Nearest Neighbors (K-NN)
  • Support Vector Machine (SVM)
  • Kernel SVM
  • Naive Bayes
  • Decision Tree Classification
  • Random Forest Classification
  • Evaluating Classification Models Performance
  • K-Means Clustering
  • Hierarchical Clustering​
  • Multilayered Perceptron
  • Classifier Evaluation
  • Ensemble Learning, Boosting
  • Unsupervised Learning, Clustering
  • Dimensionality Reduction
  • Reinforcement Learning
  • Introduction to Learning Theory
  • Anomaly Detection
  • Problem Motivation
  • Gaussian Distribution
  • Recommender System
  • Need to list assignments on each algorithm.

course SAMPLES

Sidharth
Neeraj
Harleen Kaur

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