Machine Learning AI Foundations, R Statistics Essential, Python Essential

Data Science

5 Days

  • This Machine Learning online course will provide you with insights into the vital roles played by machine learning engineers and data scientists.
  • You will learn how to uncover the hidden value in the data using Python programming for futuristic inference.
  • You will work with real-time data across multiple domains including e-commerce, automotive, social media and more. 
  • You will learn how to develop machine learning algorithms using concepts of regression, classification, time series modelling and much more.

  • Anyone who wants to learn about Machine Learning.
  • Data Engineers, Software Engineers, Technical managers, Analysts, Architects, IT operations etc.
  • Data scientists, Researchers and Students.
  • This course can be taken by anyone. It starts from scratch and has taken care of all concepts required.
  • Any students in college who want to start a career in Data Science.

  • Basic familiarity with Windows Environment
  • Knowledge of RDBMS is helpful

Day 1

Module: Machine Learning and Value Prediction

  • What is machine learning?
  • Supervised machine learning for value prediction
  • Build a simple home value estimator
  • Find the best weights automatically
  • Cool uses of value prediction


Module: R Statistics Essential

  • Installing R on your computer
  • Using RStudio
  • Taking a first look at the interface
  • Installing and managing packages
  • Using built-in datasets in R
  • Entering data manually
  • Importing data
  • Converting tabular data to row data
  • Working with color in R
  • Exploring color with Colorbrewer
  • Challenge: Creating color palettes in R


Module: Charts for One Variable

  • Creating bar charts for categorical variables
  • Creating pie charts for categorical variables
  • Creating histograms for quantitative variables
  • Creating box plots for quantitative variables
  • Overlaying plots
  • Saving images
  • Challenge: Layering plots


Module: Statistics for One Variable

  • Calculating frequencies
  • Calculating descriptives
  • Using a single proportion: Hypothesis test and confidence interval
  • Using a single mean: Hypothesis test and confidence interval
  • Using a single categorical variable: One sample chi-square test
  • Examining robust statistics for univariate analyses
  • Challenge: Calculating descriptive statistics


Day 2

Module: Modifying Data

  • Examining outliers
  • Transforming variables
  • Computing composite variables
  • Coding missing data
  • Challenge: Transforming skewed data to pull in outliers


Module: Working with the Data File

  • Selecting cases
  • Analyzing by subgroup
  • Merging files
  • Challenge: Analyzing guinea pig data subgroups


Module: Charts for Associations

  • Creating bar charts of group means
  • Creating grouped box plots
  • Creating scatter plots
  • Challenge: Creating your own grouped box plots


Day 3

Module: Statistics for Associations

  • Calculating correlation
  • Computing a bivariate regression
  • Comparing means with the t-test
  • Comparing paired means: Paired t-test
  • Comparing means with a one-factor analysis of variance (ANOVA)
  • Comparing proportions
  • Creating cross tabs for categorical variables
  • Computing robust statistics for bivariate associations
  • Challenge: Comparing proportions across several different groups


Module: Charts for Three or More Variables

  • Creating clustered bar charts for means
  • Creating scatter plots for grouped data
  • Creating scatter plot matrices
  • Creating 3D scatter plots
  • Challenge: Creating your own scatter plot matrix


Module: Statistics for Three or More Variables

  • Computing a multiple regression
  • Comparing means with a two-factor ANOVA
  • Conducting a cluster analysis
  • Conducting a principal components/factor analysis
  • Challenge: Creating a cluster analysis of states in the US


Day 4

Module: Getting Started

  • Installing Python and Komodo
  • Taking a first look at the interface


Module: Language Overview

  • About the overview
  • Hello world
  • Python anatomy
  • Expressions and statements
  • Whitespace and comments
  • Using print()
  • Blocks and scope
  • Conditionals
  • Loops
  • Functions
  • Objects


Module: Types and Values

  • Overview
  • The string type
  • Numeric types
  • The bool type
  • Sequence types
  • type() and id()


Module: Conditionals

  • Conditional syntax
  • Conditional operators
  • Conditional assignment


Module: Operators

  • Arithmetic operators
  • Bitwise operators
  • Comparison operators
  • Boolean operators
  • Operator precedence


Module: Loops

  • Python loops
  • The while loop
  • The for loop
  • Additional controls


Module: Functions

  • Defining a function
  • Function arguments
  • Argument lists
  • Keyword arguments
  • Return values
  • Generators
  • Decorators


Day 5

Module: Structured Data

  • Basic data structures
  • Lists and tuples
  • Dictionaries
  • Sets
  • List comprehension
  • Mixed structures


Module: Classes

  • Creating a class
  • Constructing an object
  • Class methods
  • Object data
  • Inheritance
  • Iterator objects


Module: Exceptions

  • Handling exceptions
  • Reporting errors


Module: String Objects

  • Overview of string objects
  • Common string methods
  • Formatting strings
  • Splitting and joining


Module: File I/O

  • Opening files
  • Text vs. binary mode
  • Text files
  • Binary files


Module: Built-in Functions

  • Numeric functions
  • String functions
  • Container functions
  • Object and class functions


Module: Modules

Using standard modules

Creating a module