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Advanced Certificate Program in Machine Learning

Program Highlights:
Praxis Certification, Placement Support

Advanced Certificate Program in Machine Learning

This Advanced Program on Data Science has a perfect blend of Technology, Data Science and Business cases and insights; it stands out to be among the best in the world. This uniquely blended Program is brought to you by Praxis, a Top-ranked Analytics B-School in India.

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INR 29,800

Program Summary

  • 10 credits
    Credits

    With this course, you are 5 credits short of an assured placement.

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  • Duration 6 months
  • 40 Hours of projects/Assignments
  • 81 Hours of online sessions

Course Topics

  • 1

    Statistics 101

    • Introduction to Statistics

    • Introduction to Statistics – II

    • Measures of Central Tendency, Spread and Shape – I

    • Measures of Central Tendency, Spread and Shape – II

    • Measures of Central Tendency, Spread and Shape – III

  • 2

    R Programming

    • R Programming

    • Introduction to R – I

    • Introduction to R – II

    • Common Data Structures in R

    • Conditional Operation and Loops

    • Looping in R using Apply Family Functions

    • Creating User Defined Functions in R

    • Graphics with R

    • Advanced Graphics with R

  • 3

    Python

    • Understanding Basics of Python

    • Control Structures and for loop

    • Playing with while loop | break and continue

    • Strings and files

    • List

    • Dictionary and Tuples

  • 4

    Data Mining 1 - Machine Learning with R & Python

    • Introduction to NumPy

    • Introduction to Pandas

    • Slicing Data

    • Exploratory Data Analysis

    • Exploratory Data Analysis (Continue)

    • Missing Value Imputation and Outlier Analysis

    • Linear Regression Motivation

    • Linear Regression optimization objective

    • Linear Regression in Python

    • Introduction to Regression Tree

    • Introduction to Classification Tree

    • Measures of Selecting the best Split

    • Cluster Analysis – Hierarchical Clustering & k-Means Clustering

    • Customer segmentation in Telecom Industry using Cluster Analysis

    • k-Means clustering

    • Association Rules mining

    • Market Basket Analysis

  • 5

    Data Mining 2 - Advanced Machine Learning with R & Python

    • Sources of Error (Irreducible error, bias and variance)

    • Formally defining the 3 Sources of Error

    • Linear Regression – Multicollinearity (VIF)

    • Qualitative Predictors – Use of Dummy Variables

    • Observing overfitting in Polynomial Regression

    • Regularized Regression (L2 – Regularization) – To avoid overfitting

    • Regularized Regression (L1 – Regularization) – Feature selection using regularization

    • Regularized Regression – How does regularized regression handles multicollinearity?

    • Decision Tree – Pruning

    • Bagging Models

    • Designing your own Bagged Model

    • Random Forest

    • Boosting (Ada Boost)

    • K Nearest Neighbour – Concept. kNN algorithm for k=1 and k>1

    • Writing a K Nearest Neighbour algorithm from scratch

    • Comparison of kNN with Linear Regression; Difference between kNN and kMeans.

    • Revision of basics of Linear Algebra

    • The Theory of dimension reduction

    • Practical – Compressing an image file [Practical using R Software]

    • Practical – Compressing an image file [Practical using R Software] (Continue)

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