What you'll learn

All fundamentals of Julia programming, Julia syntax for coding, DataTypes, Data-Structures in Julia.
Defining and working with Functions, Methods, Constructors, Macros in Julia programming environment.
Linear Algebra in Julia. Working with DataFrames, TimeSeries for Data Manipulation in Julia.
Date and Time objects, manipulating Period objects in Julia.
Usage of Julia packages for solving Machine Learning problems.
Usage of Data Visualization tools in Julia.

Course content

9 sections • 118 lectures • 20h 11m total length

Welcome and Getting Started1:27
Installation of Julia and Development Environment3:03
Setting up VS Code Julia Extension OR Juno as Julia programming development environment. Although, you can use any other editor/tool you prefer to write and run Julia code.
Writing First Julia Program5:46
Resources in this Course here...0:19

Integer, Float and Rational Numbers18:56
Arithmetic Operations on Real Numbers7:44
Complex Numbers4:02
Comparison and Logical Operators8:46
Strings and Characters29:28
Symbols and Expressions3:29
Basic I/O9:14
Basic I/O Exercise Solution5:26
Tuples9:56
Usage of Dot Syntax for Broadcasting Operations4:15
Arrays - Part I26:47
Arrays - Part II22:49
Arrays - Part III19:36
Arrays Exercise Solution3:35
Dictionaries13:19
Dictionaries Exercise Solution2:52
File I/O7:13
Section 2 Exercise - Two Homework Tasks1:17
Section 2 Exercise - Task I Solution8:32
Section 2 Exercise - Task II Solution1:48

Compound Expressions5:29
Conditional Evaluation: if-elseif-else syntax9:03
Conditional Evaluation Exercise Solution4:56
Repeated Evaluation: for loop8:07
Usage of break and continue4:59
Repeated Evaluation: while loop3:18
Repeated Evaluation Exercise Solution2:41
Exception Handling10:48
Section 3 Exercise - Two Homework Challenges4:12
Section 3 Exercise - Challenge I Workout6:38
Section 3 Exercise - Challenge II Workout8:05

Anonymous Functions4:51
map(), filter(), reduce()14:35
Defining Functions with Arguments7:18
Optional Arguments and Keyword Arguments5:33
Type Assertion for Function Arguments5:22
Varargs Functions5:20
Multiple Dispatch10:42
Constructors - Part I8:43
Constructors - Part II16:59
Section 4 Exercise - Two Homework Tasks12:34
Section 4 Exercise - Task I Solution1:40
Section 4 Exercise - Task II Solution0:39

Working with Matrices25:27
Matrix and Vector Operations - Part I15:11
Basic matrix operations
UniformScaling objects and Identity matrix
Matrix and Vector Operations - Part II9:06
Division of a matrix by another matrix
Inverse of a matrix
Matrix and Vector Operations - Part III23:06
Pseudoinverse
Condition number
Right division and left division
Matrix and Vector Operations - Part IV31:39
Left inverse and right inverse
Matrix and Vector Operations - Part V21:43
Power of a matrix and matrix as power
Concatenation
Matrix and Vector Operations - Part VI15:20
Slicing matrices into row and column vectors
Diagonal elements in a matrix
Matrix and Vector Operations - Part VII10:12
Adjoint and transpose
Dot product and cross product
Matrix and Vector Operations - Part VIII14:14
Norm
Transformation of vectors
Matrices with Special Structures21:48
Eigen values and eigen vectors of a Matrix26:46
Factorizations of Matrices - Part I18:13
LU decomposition
Cholesky decomposition
Factorizations of Matrices - Part II19:39
LDLt decomposition
Bunch-Kaufman decomposition
Factorizations of Matrices - Part III21:20
QR decomposition
LQ decomposition
Hessenberg decomposition
Schur decomposition
Singular value decomposition
Sparse Matrices and their Factorizations38:51
Rank and Null space of Matrix15:27

Date and Time Objects in Julia13:18
Query on Date Objects14:44
Query on Dates Exercise Solution0:26
Date Time Arithmetic20:15
Working with TimeZones12:49
TimeSeries in Julia15:55
Accessing Data in TimeArray12:48
Applying Conditions for Accessing TimeArray10:21
Applying and Combining Methods for TimeArray15:36
Applying and Combining Methods Exercise Solution2:46
[Project 1] Data Analysis - Problem Statement4:53
E-Commerce Data Analysis - Part I21:35
E-Commerce Data Analysis - Part II5:16
E-Commerce Data Analysis - Part III18:14
E-Commerce Data Analysis - Part IV7:58
Section 7 Exercise - Two Tasks2:19
Section 7 Exercise - Task I Workout1:45
Section 7 Exercise - Task II Workout2:36
Date, Time and E-Commerce Data Analysis

Machine Learning with Julia0:41
Introduction to Linear Regression7:30
[Project 2] Simple Linear Regression - Problem Statement2:02
Simple Linear Regression in Julia - Part I10:55
Simple Linear Regression in Julia - Part II10:56
Simple Linear Regression in Julia - Part III14:03
[Project 3] Multiple Linear Regression - Problem Statement2:32
Label Encoding, One-hot Encoding, Dummy Coding4:55
Multiple Linear Regression in Julia - Part I16:20
Multiple Linear Regression in Julia - Part II19:40
Polynomial Regression4:03
[Project 4] Polynomial Regression - Problem Statement1:36
Polynomial Regression in Julia - Part I18:16
Polynomial Regression in Julia - Part II9:27

Requirements

Recapitulation of some high school mathematics and statistics.
Basic proficiency in working with computer.

Description

Welcome to this online course on Julia! This course is for anyone who wants to learn Julia programming for problem solving. Machine learning and data science are the well applied domains of Julia programming. Above all, Julia is a fast and highly efficient programming language for scientific computation. Master Julia syntax for coding through arranged topics and exercises in this course.

Full-fledged segment in this course is dedicated to know about core concept of data manipulation in Julia which is an essential part of data analysis.

This course includes 4 projects on “data analysis” and for building “machine learning models based on regression analysis”, to learn the usage of Julia packages for data analysis and machine learning.

With data manipulation and building machine learning models, we will see the usage of Julia package StatsPlots for data visualization.

By the end of this course, you will know how to work with Julia syntax for

writing Julia program.
working with several datatypes and data-structures.
creating and manipulating arrays.
working with raw text.
defining functions and macros.
metaprogramming.
creating objects from new datatype that can be defined in Julia.
Linear Algebra.
data manipulation in DataFrame and TimeArray objects.
building machine learning models for numeric prediction.
setting up data visualization tools.

See you inside the course!

Who this course is for:

Anyone from any professional or academic background, familiar with basic high-school mathematics.
You can learn everything from scratch as a beginner programmer in this course.
If you have coding experience in any programming language (e.g., Python, R, C, C++, Fortran, COBOL, Pascal etc.), this course is for you to enhance knowledge.

What you'll learn

Explore related topics

Course content

Introduction4 lectures • 11min

Data Types and Data Structures20 lectures • 3hr 29min

Control Flow11 lectures • 1hr 8min

Functions, Methods and Constructors12 lectures • 1hr 34min

Metaprogramming12 lectures • 1hr 7min

Linear Algebra16 lectures • 5hr 28min

Working with DataFrames11 lectures • 2hr 8min

Working with Dates, Times and TimeSeries18 lectures • 3hr 4min

Machine Learning Projects on Regression Analysis14 lectures • 2hr 3min

Requirements

Description

Who this course is for: