Manikanda - Electrical engineering tutor - Chennai

1^st lesson belanja

Manikanda

5 (1 review)

Rate RM63
Response 1h

5 (1 review)

RM63/h

1^st lesson belanja

Electrical Engineering
Electronics

IITian Silver Medalist and ML/CV researcher teaching signal and image processing, control and machine learning with relevant mathematical and programming foundations.

Name: Manikanda
Address: Chennai, MY
Telephone: undefined
Price range: RM63

Electrical Engineering
Electronics

Lesson location

- online

About Manikanda

I hold an M.Tech in Data Science from IIT Hyderabad, where I graduated as a Silver Medalist, and a B.Tech in Electronics Engineering from IIT (BHU) Varanasi.

I have over 10 years of industry experience in Machine Learning and Computer Vision, working on real-world engineering problems and deploying AI solutions. My teaching combines strong theoretical foundations with practical insights from industry, helping students understand not just how algorithms work, but why they are used in practice.

I have also qualified GATE in Computer Science (CS), Electrical Engineering (EE), and Electronics & Communication Engineering (EC), scoring 98+ percentile in each discipline.

About the lesson

Primary Subjects: Signal Processing, Image Processing and Computer Vision, Machine and Deep Learning, Linear Algebra, Probability. Control Systems

Modes of teaching(student choice):
1. Practical with a focus on helping students code and solve problems. Help them create a demonstrable GitHub portfolio. Every module in ML, IP and CV will mandatorily have these.
2. Theoretical: with a focus on mathematics, derivations etc for students who want to learn the subject at a deeper level.

Topics Covered:

1. Linear Algebra: Vectors and vector spaces, Matrix operations, Linear independence, basis, dimension. Rank and null space, Systems of linear equations, QR & LU decompositions, Orthogonality and projections, Eigenvalues and eigenvectors. Singular Value Decomposition (SVD), Positive definite matrices, Matrix calculus (essential for deep learning)

2. Probability and Statistics: Probability axioms, Conditional probability & Bayes' theorem, Random variables, Common distributions (Gaussian, Bernoulli, Binomial, Poisson, Exponential), Expectation and variance, Covariance and correlation, Joint distributions, Central Limit Theorem, Maximum Likelihood Estimation, MAP estimation, Hypothesis testing
Confidence intervals, Information theory (Entropy, KL Divergence, Cross Entropy)

3. Signal Processing: Continuous vs discrete signals, sampling theorem, Basics of LTI systems, convolution, Fourier series, discrete time and continuous time Fourier transform, discrete Fourier transform, FFT, frequency domain analysis, filtering, IIR and FIR filters, Z transform.

4. Image Processing and Computer vision: Representation and color spaces, intensity transformations, spatial filters, frequency domain filters, edge detection, morphological operations, image restoration, optical flow, image registration, homography, stereo vision, structure from motion, convolutional neural networks, image classification, object detection, image segmentation, keypoint matching, pose estimation, image stitching, image generation, video and 3D image processing (on demand)

5. Machine Learning and Deep Learning: ML pipeline, feature engineering, cross validation, bias variance tradeoff, regression, density estimation, mixture models, trees, ensemble methods, clustering, density estimation, dimensionality reduction, neural networks, gradient descent and optimizers, activation functions, regularization, CNN and transformer building blocks, network debugging,

Students can customize topics from the above and even propose new ones.

Recommendations

Recommendations come from relatives, friends and acquaintances of the teacher

U

Uma

He taught me linear regression. His grasp on the subject is phenomenal. He is a very kind teacher who is committed to ensuring the student has learned the subject. He also spends time to give you a perspective on the concepts.
Read more

U
Uma
He taught me linear regression. His grasp on the subject is phenomenal. He is a very kind teacher who is committed to ensuring the student has learned the subject. He also spends time to give you a perspective on the concepts.
View more recommendations

Rates

Rate

RM63

Pack rates

5h: RM254
10h: RM466

online

RM63/h

free lessons

This first lesson is free to allow you to get to know your teacher so that they can best meet your needs.

IITian Silver Medalist and ML/CV researcher teaching signal and image processing, control and machine learning with relevant mathematical and programming foundations.

Lesson location

About Manikanda

About the lesson

Recommendations

Rates

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Pack rates

online

free lessons

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IITian Silver Medalist and ML/CV researcher teaching signal and image processing, control and machine learning with relevant mathematical and programming foundations.

Lesson location

About Manikanda

About the lesson

Recommendations

Rates

Rate

Pack rates

online

free lessons

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