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MATLAB Master Class: Go from Beginner to Expert in MATLAB (updated0 - aretr - 09-12-2019

[Image: MATLAB-Master-Class-Go-from-Beginner-to-...750x42.jpg]

MATLAB Master Class: Go from Beginner to Expert in MATLAB (updated)
Video: .MP4, 1280x720 | Audio: AAC, 44 kHz, 2ch | Duration: 25h
Genre: eLearning | Language: English | Size: 10 GB
MATLAB from beginner to advance level with Advanced Data Types and Applications from Data Science and Data preprocessing

MATLAB (matrix laboratory) is one of the fundamental and leading programming language and is a must learn skill for anyone who want to develop a career in engineering, science or related fields. Excellent MATLAB programming skills is therefore a crucial factor in making or breaking your career.

What you'll learn
Develop beginer to advance level skills of Programming with MATLAB. This is the only course which enables you to learn intermediate and advance programming data structures such as structures, tables, times tables, cells and map container.
Gain Hands-On experience with MATLAB for visualizing, analyzing and formulating intermediate and some advanced level problems using MATLAB programming skills
Experience some real world applications of MATLAB in solving Data Science problems.

Part 1: MATLAB from Beginer to Advance

Segment 1.1: Handling variables and Creating Scripts

Segment 1.2: Doing Basic Maths in MATLAB

Segment 1.3: Operations on Matrices

Segment 1.4: Advance Math Functions with Symbolic Data Type

Segment 1.5: Interacting with MATLAB and Graphics

Segment 1.6: Importing Data into MATLAB

Segment 1.7: File Handling and Text Processing

Segment 1.8: MATLAB Programming

Segment 1.9: Sharing Your MATLAB Results

Part 2: Advance MATLAB Data Types

Segment 2.1: Cell Data Type

Segment 2.2: Tables and Time Tables

Segment 2.3: Working with Structures and Map Container Data Type

Segment 2.4: Converting between Different Data Types

Part 3: Machine Learning for Data Science Using MATLAB

Segment 3.1 Data Preprocessing

Segment 3.2. Classification

Segment 3.2.1 K-Nearest Neighbor

Segment 3.2.2 Naive Bayes

Segment 3.2.3 Decision Trees

Segment 3.2.4 Support Vector Machine

Segment 3.2.5 Discriminant Analysis

Segment 3.2.6 Ensembles

Segment 3.2.7 Performance Evaluation

Segment 3.3 Clustering

Segment 3.3.1 K-Means

Segment 3.3.2 Hierarchical Clu stering

Segment 3.4 Dimensionality Reduction

Segment 3.5 Project

Part 4: Data Preprocessing for Machine Learning using MATLAB

Segment 4.1 Handing Missing Values

Segment 4.2 Dealing with Categorical Variables

Segment 4.3 Outlier Detection

Segment 4.4 Feature Scaling and Data Discretization

Segment 4.5 Selecting the Right Method for your Data
DOWNLOAD

Code:
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Code:
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