MACHINE LEARNING
MACHINE LEARNING
Machine learning can learn from the data that is processed and analyzed. In other words, the more data that is processed, the more it can learn.Machine learning has similarities to the human reasoning process. Unlike traditional analysis, the generated model cannot evolve as data is accumulated.
R, as a dialect of GNU-S, is a powerful statistical language that can be used to manipulate and analyze data. Additionally, R provides many machine learning packages and visualization functions, which enable users to analyze data on the fly. Most importantly, R is open source and free
This course has been designed keeping the current requirements in the world of Data Analysis. This knowledge will open up huge opportunities for students who would like to pursue careers in the field of DATA SCIENCE.

Pedagogy

The Course will be taught using a combination of Lecture and hands on with standard sample datasets. Participants will bring their own Laptops.

Prerequisite

The participants should have undergone a course on basic and inferential statistics. Knowledge of computer basics

Course Outline :

  
Introduction to R
  • Downloading and installing R
  • Downloading and installing R Studio
  • Installing and loading packages
  • Reading and writing data
  • Using R to manipulate data
  • Applying basic statistics
  • Visualizing data
Regression with R
  • Linear
  • Multiple
  • Logistic Regression
  • Regression Diagnostics
Classification
  • Logistic
  • Decision Trees
  • KNN
Unsupervised Learning
  • Cluster
  • KMeans
Model evaluation
  • Confusion Matrix
  • Kappa Statistics
  • AIC Criteria
  • ROC curve

Machine Learning and deep Learning

Course Outline :

  
Introduction to R – (16 hrs)
  • Downloading and installing R
  • Downloading and installing R Studio
  • Installing and loading packages
  • Reading and writing data
  • Using R to manipulate data
  • Applying basic statistics
  • Visualizing data

Introduction to Analytics

  
Introduction – Basics of Statistics –
Descriptive statistics,
Hypothesis testing,
Estimation – (16 hrs)
  • Data exploration
  • Data Preparation
  • Model Building
  • Model Evaluation
Predictive Analytics–(12 hrs)
  • Linear and Multilinear prediction with case study
  • Regression Trees
    1. Understanding Regression Trees – why?
    2. SDR
    3. Model Evaluation
  • Time series - Understanding of time series and suitability with case study
Classification/Regression Analytics with Algorithms and case study – (16- hrs)
  • Logistic Regression
  • KNN
  • K Means
  • Random Forest
  • SVM
NLP – (6 hrs)
  • Introduction to text mining and NLP
  • Concept of document and Corpus
  • Document Matrix
  • Plotting word cloud
  • Bayesian rule
  • Model building
  • Model accuracy
  • Model improvement
Neural Network – (8hrs)
  • Introduction to deep learning
  • Concept of Artificial neural
  • Building Feed Forward NeuralNetwork
  • Impact of Hidden Layers
  • Gradient Descent
  • Weights Update
  • Model building
  • Model evaluation and improvement