Machine Learning & Data Analytics Topics

  • Descriptive Statistics

  • Measure of Central Tendency

  • Measure of Dispersion

  • Skewness and Kurtosis

  • Covariance and Correlation

  • Events

  • Sets in Probability

  • Random Variables

  • Conditional Probability

  • Expectation and Variance

  • Discrete Distribution

    • Uniform

    • Bernoulli

    • Binomial

    • Poisson

  • Continuous Distribution

    • Uniform

    • Normal

  • Hypothesis formulation

  • Null Hypothesis

  • Procedure for testing a hypothesis

  • Small Sample test

  • Large Sample test

  • t-test

  • chi-square test

  • z-score

  • p-value

  • One-way ANOVA

  • Data Import and Export

  • Data Cleaning and Transformation

  • Combining Datasets

  • Regression Analysis

  • Clustering and Classification

  • Case Study

  • Installation of R-Studio

  • Introduction to Various panes of R-Studio

  • Data Types, Operators

  • Creation of Vectors

  • Functions

  • Data handling and cleaning

  • Dataframes, Merging Dataframes

  • Plotting

  • Case Study

  • Python Built-in types

  • Python operators

  • Python Control flow

  • Python functions

  • User defined and Lamda functions

  • Python classes

  • Python modules and packages

  • Numpy

  • Pandas

  • Dataframe manipulation

  • DDL Statements

  • DML Statements

  • Aggregate functions

  • Date functions

  • Union, Union All

  • Joins

  • View, Indexes

  • Rollup and Cubes, Group by

  1. Introduction to Weka:

    1. Overview of Weka

    2. Installation and setup

  2. Exploring the Weka Interface:

    1. Overview of the graphical user interface

    2. Loading datasets

    3. Basic navigation and functionality

  3. Data Preprocessing:

    1. Handling missing values

    2. Data cleaning and filtering

    3. Transforming and normalizing data

  4. Exploratory Data Analysis (EDA):

    1. Descriptive statistics

    2. Visualization using Weka tools

  5. Classification Algorithms:

    1. Introduction to classification

    2. Decision trees

    3. k-Nearest Neighbors (k-NN)

    4. Support Vector Machines (SVM)

    5. Naive Bayes

  6. Clustering Algorithms:

    1. Introduction to clustering

    2. k-Means clustering

    3. Hierarchical clustering

    4. EM (Expectation-Maximization) clustering

  7. Association Rule Mining:

    1. Market basket analysis

    2. Apriori algorithm

  8. Feature Selection and Dimensionality Reduction:

    1. Importance of feature selection

    2. Principal Component Analysis (PCA)

  9. Model Evaluation and Validation:

    1. Cross-validation

    2. Confusion matrix and performance metrics

    3. ROC curves

  10. Ensemble Learning:

    1. Introduction to ensemble methods

    2. Random Forest

    3. Bagging and boosting

  11. Introduction to Scripting in Weka:

    1. Using Weka through the command line

    2. Writing scripts for automation

  12. Resources and Further Learning:

    1. Weka documentation and resources

    2. Additional readings and tutorials

  13. Assessment:

    1. Assignments and quizzes

    2. Final project or exam