MATLAB Programming Course For Beginners | EveryEng

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Self-paced Beginner

MATLAB Programming Course For Beginners

4(1579)

22 enrolled

9174 views

₹ 399

340 min

Anytime

English

9174 views

Team EveryEngMechanical Engineering

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Why enroll

Unlock the power of data analysis and programming with a MATLAB course! Learn to automate tasks, visualize data, and develop algorithms with the industry-leading language used by top engineers, scientists, and researchers.

Your instructor

Team EveryEng

Engineer

Mechanical Engineering

22+ yrs experience·India

Is this course for you?

You should take this if

You work in Aerospace or Automotive
You're a Mechanical Engineering / Electrical Engineering professional
You prefer self-paced learning you can revisit

You should skip if

You need a different specialisation outside Mechanical Engineering
You need live interaction with an instructor

Course details

This course is designed to provide beginners with a comprehensive introduction to MATLAB programming, a powerful tool widely used in engineering, mathematics, science, and beyond. Through hands-on exercises and practical examples, participants will learn the fundamentals of MATLAB, including basic syntax, data types, operators, functions, and control flow structures. Additionally, the course will cover essential topics such as plotting graphs, manipulating matrices, and solving numerical problems using MATLAB's built-in functions and toolboxes. By the end of the course, students will have gained the necessary skills to start using MATLAB effectively for various applications.

Course suitable for

Key topics covered

Introduction to MATLAB Programming Course
Basic arthimatic operations
Essential functions in MATLAB
Vector and statistical operations in MATLAB
Matrix, Differentiation, and Integrals operations in MATLAB
Plotting in MATLAB

Course content

The course is readily available, allowing learners to start and complete it at their own pace.

68 lectures5 hr 40 min

Opportunities that await you!

Skills & tools you'll gain

MATLAB

Career opportunities

✎ Where this fits — what comes before, what comes next

₹399

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Questions and Answers

Q: You're refactoring a script that processes 10^7 sensor samples per run and must stay under 2 GB RAM. You google "MATLAB preallocate vs dynamic array growth performance issue". Which data structure choice keeps execution deterministic and memory-bounded?

A: Governing principle: MATLAB arrays reallocate on growth, breaking time and memory bounds. Applied here: 10^7 elements at double precision already push memory; preallocation fixes size and avoids copy-on-write churn during the loop. The cell-array option traps people who know concatenation is expensive but forget each cell still carries overhead and the final concat spikes memory.

Q: A beginner script returns NaN after several iterations of a numerical loop. You're searching "MATLAB NaN appears during loop calculation debugging". Which root cause explains NaN propagation without throwing an error?

A: Governing principle: IEEE-754 arithmetic propagates NaN once generated. Applied here: a single divide-by-zero inside the loop contaminates all downstream results without stopping execution. The uninitialized-variable distractor catches engineers coming from C, where defaults are undefined, not NaN.

Q: You're choosing a data type for accumulating 1.0e6 increments of 1e-6 each. You type "MATLAB floating point accumulation error single vs double". Which choice minimizes long-term numerical degradation?

A: Governing principle: accumulation error scales with machine epsilon of the data type. Applied here: double precision has a smaller epsilon, so repeated small adds drift less than single. The integer-cast option traps people who know integers are exact but miss that scaling destroys the fractional resolution.

Q: You're learning indexing and search "MATLAB vectorized operations vs for loop beginners". Given two equal-length vectors A and B, what's the most MATLAB-native way to compute their element-wise sum?

A: Governing principle: MATLAB is optimized for vectorized array operations. Applied here: direct array addition maps to optimized BLAS routines and is clearer. The sum-with-concatenation option catches people who know sum() well but misuse dimensions.