Duration: Hours

Julia is a high-level and primarily developed to perform scientific computation, machine learning, and statistical tasks. The language is designed to keep all the needs of scientific researchers and data scientists to optimize the experimentation and design implementation.

Training Mode: Online


Objectives :

a). Update your resume with Julia Skill
b). Learn Julia programming constructs
c). Julia installation with Jupyter notebook
d). Julia basics variable numbers and string
e). Managing Third Party Package in Julia
f). Learn different Julia collection array, dictionary and tuples & Operations
g). Apply Julia Function for vector and matrix Operations
h). Analyse Data with Julia Dataframes package equivalent to pandas in Python
i). Draw plot with plots module in julia
j). Sale Prediction using Linear Regression on Sales Data with GLM Package
k). Predict Salary using Multiple Linear Regression on Salary Data
l). Logistic Regression on camera data with Julia GLM
m). ClusterData with K-Means clustering algorithm (Clustering)
n). Reduce Dimension of iris Dataset with PCA (Multivariate Stats package)

a). Introduction 

  1. Installation Instruction
  2. Install Julia (Windows)
  3. Install Julia (Linux)
  4. Installation issue
  5. Getting started Julia with Jupyter Notebook

b). Julia code

  1. Download Code

c). Julia Basics

  1. Numbers & Variables, Comment
  2. String

d). Data structure

  1. Arrays
  2. Tuples
  3. Dictionary
  4. Sets

e). Control Flow

  1. Decision making
  2. Looping in Julia

f). More on Julia

  1. Function
  2. Packages

g). Vector and Matrix Processing

  1. Random package
  2. Algebra Operations

h). Data Analysis with Dataframes

  1. Getting started with DataFrames
  2. More on Dataframes
  3. Reading and Writing Dataframe
  4. Row and Column operation on DataFrames

i). Data Visualization

  1. Getting started with Plots
  2. Some more Plots with Plots

j). Regression

  1. Linear Regression
  2. Multiple Linear Regression

k). Classification

  1. Logistic Regression

l). Clustering Unsupervised Learning

  1. K-means Clustering

m). Dimensionality Reduction

  1. Princial Component analysis


For more inputs on Julia Programming for Data Science and Machine Learning you can connect here.
Contact the L&D Specialist at Locus IT.



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