Julia Programming for Machine Learning

Course Curriculum

Chapter 1: Introduction

Lecture 1: Welcome and Getting Started

Lecture 2: Installation of Julia and Development Environment

Lecture 3: Writing First Julia Program

Lecture 4: Resources in this Course here…

Chapter 2: Data Types and Data Structures

Lecture 1: Integer, Float and Rational Numbers

Lecture 2: Arithmetic Operations on Real Numbers

Lecture 3: Complex Numbers

Lecture 4: Comparison and Logical Operators

Lecture 5: Strings and Characters

Lecture 6: Symbols and Expressions

Lecture 7: Basic I/O

Lecture 8: Basic I/O Exercise Solution

Lecture 9: Tuples

Lecture 10: Usage of Dot Syntax for Broadcasting Operations

Lecture 11: Arrays – Part I

Lecture 12: Arrays – Part II

Lecture 13: Arrays – Part III

Lecture 14: Arrays Exercise Solution

Lecture 15: Dictionaries

Lecture 16: Dictionaries Exercise Solution

Lecture 17: File I/O

Lecture 18: Section 2 Exercise – Two Homework Tasks

Lecture 19: Section 2 Exercise – Task I Solution

Lecture 20: Section 2 Exercise – Task II Solution

Chapter 3: Control Flow

Lecture 1: Compound Expressions

Lecture 2: Conditional Evaluation: if-elseif-else syntax

Lecture 3: Conditional Evaluation Exercise Solution

Lecture 4: Repeated Evaluation: for loop

Lecture 5: Usage of break and continue

Lecture 6: Repeated Evaluation: while loop

Lecture 7: Repeated Evaluation Exercise Solution

Lecture 8: Exception Handling

Lecture 9: Section 3 Exercise – Two Homework Challenges

Lecture 10: Section 3 Exercise – Challenge I Workout

Lecture 11: Section 3 Exercise – Challenge II Workout

Chapter 4: Functions, Methods and Constructors

Lecture 1: Anonymous Functions

Lecture 2: map(), filter(), reduce()

Lecture 3: Defining Functions with Arguments

Lecture 4: Optional Arguments and Keyword Arguments

Lecture 5: Type Assertion for Function Arguments

Lecture 6: Varargs Functions

Lecture 7: Multiple Dispatch

Lecture 8: Constructors – Part I

Lecture 9: Constructors – Part II

Lecture 10: Section 4 Exercise – Two Homework Tasks

Lecture 11: Section 4 Exercise – Task I Solution

Lecture 12: Section 4 Exercise – Task II Solution

Chapter 5: Metaprogramming

Lecture 1: Meta.parse() Function

Lecture 2: Expr() Constructor

Lecture 3: Construct few more Expressions

Lecture 4: Construct an Expression for Compound Expressions

Lecture 5: Construct Expression Exercise Solution

Lecture 6: Construct an Expression using quote block

Lecture 7: Macros

Lecture 8: @enum Macro

Lecture 9: Defining Macros – Part I

Lecture 10: Defining Macros – Part II

Lecture 11: Section 5 Exercise – Create a Macro

Lecture 12: Section 5 Exercise – Solution

Chapter 6: Working with DataFrames

Lecture 1: Working with DataFrames in Julia

Lecture 2: Creating DataFrame Object

Lecture 3: Import and Read Data as DataFrame

Lecture 4: Filtering and Sorting Data

Lecture 5: Updating and Reshaping DataFrame

Lecture 6: Replacing and Changing Entries

Lecture 7: Replacing and Changing Entries Exercise Solution

Lecture 8: Usage of Split-Apply-Combine Strategy

Lecture 9: Treating Missing Entries in Data

Lecture 10: Section 6 Exercise

Lecture 11: Section 6 Exercise – Solution

Chapter 7: Working with Dates, Times and TimeSeries

Lecture 1: Date and Time Objects in Julia

Lecture 2: Query on Date Objects

Lecture 3: Query on Dates Exercise Solution

Lecture 4: Date Time Arithmetic

Lecture 5: Working with TimeZones

Lecture 6: TimeSeries in Julia

Lecture 7: Accessing Data in TimeArray

Lecture 8: Applying Conditions for Accessing TimeArray

Lecture 9: Applying and Combining Methods for TimeArray

Lecture 10: Applying and Combining Methods Exercise Solution

Lecture 11: [Project 1] Data Analysis – Problem Statement

Lecture 12: E-Commerce Data Analysis – Part I

Lecture 13: E-Commerce Data Analysis – Part II

Lecture 14: E-Commerce Data Analysis – Part III

Lecture 15: E-Commerce Data Analysis – Part IV

Lecture 16: Section 7 Exercise – Two Tasks

Lecture 17: Section 7 Exercise – Task I Workout

Lecture 18: Section 7 Exercise – Task II Workout

Chapter 8: Machine Learning Projects on Regression Analysis

Lecture 1: Machine Learning with Julia

Lecture 2: Introduction to Linear Regression

Lecture 3: [Project 2] Simple Linear Regression – Problem Statement