Machine Learning with Python

Machine Learning with Python

About the course
  • Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves
  • Prerequisite : Basic Knowledge on Descriptive & Inferential Statistics
  • Eligibility : MS/M.Tech/B.E/B.Tech/M.C.A/M.B.A/M.sc in Science/M.com
  • Duration : Duration : 3 Months(60 Hours) : Only Saturday & Sunday
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Course Details


Python for Data Science
Introduction to Machine Learning & Examples
Introduction to Cluster Analysis
About Agglomerative Hierarchical Clustering 
K-Means Procedure & Medoid Cluster Analysis
Introduction to Dimensionality Reduction
About Principal Component Analysis & Examples
Introduction to Association Rules
About Market Basket Analysis
Apriori/Support/Confidence/Lift & Examples
Introduction to Bayes Law
About Naïve Bayes Algorithm & Examples
About Variance-Bias Tradeoff
About Gradient Decent/Ascent Procedures
About Maximum Likelihood Method
About Logistic Regression & Examples
About K-NN Classifier (Nearest-Neighbor Methods) & Examples
Introduction to Tree based Methods
Introduction to Decision Trees & Examples
About Ensemble Methods
Introduction to Random Forest
About Bagging & Bootstrap & Examples
Introduction to Support Vector Machines
About Maximum Marginal Classifier & Support Vector Classifier
Kernel Trick  & SVM with more than Two Classes & Examples
Introduction to Neural Networks
About Single & Multi-Layer Perceptron
About Forward Feed & Backward Propagation & Examples
About Ridge Regression
About Lasso Regression & Examples
Introduction to Simple Linear Regression
Introduction to Multiple Linear Regression
Estimation of Model Parameters
Hypothesis Testing in Multiple Linear Regression
Model Adequacy Checking
Variable Selection Methods & Examples
Polynomial Regression



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