Industry Skills Self Paced

NumPy

Structured to take you from raw arrays to real-world data manipulation, this course builds your skills step-by-step so you can confidently work with data in any AI, Analytics, or Data Science project. Every concept is paired with hands-on coding practice, ensuring you don't just understand NumPy — you can actually use it. By the end, you'll have the practical, job-ready foundation needed to move into Machine Learning, Data Science, and Analytics roles with confidence.

5.0 (1 ratings)
1 enrolled 8 Hours Advanced Tamil

What You'll Learn

  • Think in Arrays
  • Handle Real Data
  • Write Clean Code
  • Solve Data Problems
  • Process Images Fast
  • Build ML Foundation

Requirements

  • Python Basics Knowledge Must

Tools & Technologies

Python Python
NumPy NumPy
Matplotlib Matplotlib

Course Description

Every AI model, every dashboard, every dataset — it all comes down to arrays, and NumPy is the language they speak. This course takes you from zero to confidently manipulating data at the array level, the same skill powering Machine Learning, Data Science, and Analytics workflows today. 100% practical, hands-on coding that builds real intuition. Learn in Tamil, at your own pace, and build the foundation every AI & Analytics career is built on

Right for You?

Who This Course is For

  • Students & Freshers
  • Aspiring Data Analysts
  • Machine Learning Beginners
  • Career Switchers
  • Python Enthusiasts

Course Curriculum

10 modules • 82 lessons
Module 1: Fundamentals 6 lessons • 1h
  • What is NumPy & why it matters in AI/Analytics 10m
  • Installing & importing NumPy 10m
  • NumPy vs Python lists u2014 speed & memory comparison 10m
  • Creating ndarrays u2014 array, zeros, ones, full, eye, arange, linspace 10m
  • Data types (dtype) u2014 int, float, complex, bool 10m
  • Array attributes u2014 shape, size, ndim, nbytes 10m
Module 2: Array Operations 11 lessons • 1h 50m
  • Indexing & slicing u2014 1D, 2D, 3D 10m
  • Fancy indexing 10m
  • Boolean masking / conditional filtering 10m
  • Reshaping & flattening u2014 reshape, ravel, flatten 10m
  • Stacking & splitting u2014 hstack, vstack, concatenate, split 10m
  • Copy vs View u2014 memory gotcha (interview favourite) 10m
  • Axis parameter mastery u2014 axis=0 vs axis=1 vs axis=None 10m
  • Element & Pattern Repetition 10m
  • Combination of Points with Meshgrid 10m
  • Modify dimensions 10m
  • Filtering Array Elements 10m
Module 3: Math & Statistics 14 lessons • 2h 20m
  • Element-wise arithmetic operations 10m
  • Broadcasting rules & examples 10m
  • Aggregations u2014 sum, mean, min, max, std, var 10m
  • np.percentile & np.quantile u2014 analytics daily use 10m
  • np.unique & np.bincount u2014 EDA staple 10m
  • np.clip u2014 data preprocessing must 10m
  • np.where u2014 conditional logic, used everywhere in ML 10m
  • Handling NaN u2014 np.nan, np.nanmean, np.nansum 10m
  • np.histogram & np.histogram2d u2014 distribution analysis & EDA 10m
  • np.cumsum & np.cumprod u2014 cumulative ops for time-series 10m
  • np.diff u2014 differences between elements, time-series features 10m
  • np.corrcoef & np.cov u2014 correlation & covariance matrix (feature selection) 10m
  • np.digitize & np.searchsorted u2014 binning & efficient lookup 10m
  • np.random u2014 seed, rand, randn, randint, choice, shuffle, Generator API 10m
Module 4: Array Manipulation & Functions 9 lessons • 1h 30m
  • Vectorization u2014 replace loops with array ops 10m
  • Universal functions (ufuncs) u2014 sqrt, exp, log, abs 10m
  • Sorting & searching u2014 sort, argsort, argmax, argmin 10m
  • Structured arrays u2014 named fields 10m
  • Memory layout u2014 C order vs Fortran order 10m
  • np.gradient u2014 numerical differentiation for optimization 10m
  • np.convolve u2014 signal & image processing basics 10m
  • np.vectorize u2014 wrapping Python functions (still a Python loop, NOT fast) 10m
  • dtype precision u2014 float16 vs float32 vs float64 (GPU memory efficiency) 10m
Module 5: Linear Algebra (Core for AI/ML) 12 lessons • 2h
  • Matrix operations u2014 dot, matmul, transpose 10m
  • np.linalg.inv u2014 matrix inverse 10m
  • np.linalg.det u2014 determinant 10m
  • np.linalg.norm u2014 distance metrics in ML 10m
  • Eigenvalues & eigenvectors u2014 np.linalg.eig 10m
  • np.linalg.eigh u2014 eigenvalues for symmetric matrices (PCA, stable) 10m
  • SVD u2014 Singular Value Decomposition 10m
  • Solving linear equations u2014 np.linalg.solve 10m
  • np.linalg.matrix_rank u2014 data quality & feature redundancy 10m
  • np.linalg.lstsq u2014 least squares solution (regression core) 10m
  • np.linalg.pinv u2014 pseudoinverse (when matrix is not invertible) 10m
  • np.linalg.cholesky u2014 Cholesky decomp (Gaussian processes) 10m
Module 6: Advanced 10 lessons • 1h 40m
  • np.einsum u2014 efficient tensor operations 10m
  • np.fft u2014 Fast Fourier Transform for time-series analysis 10m
  • Strided tricks & views u2014 memory-efficient manipulation 10m
  • np.memmap u2014 memory-mapped files for large datasets 10m
  • Performance optimization u2014 profiling, in-place ops 10m
  • Batch processing arrays u2014 chunking large data 10m
  • np.ma (Masked Arrays) u2014 proper missing data handling 10m
  • File I/O u2014 np.save, np.load, np.savez, np.savez_compressed, np.loadtxt 10m
  • np.testing u2014 assert_array_almost_equal, assert_allclose for ML unit tests 10m
  • np.errstate u2014 managing float point exceptions (overflow, underflow) 10m
Module 7: DateTime Operations 9 lessons • 1h 30m
  • np.datetime64 u2014 native date/time dtype in NumPy 10m
  • np.timedelta64 u2014 representing time durations & differences 10m
  • DateTime units u2014 D, M, Y, h, m, s, ms, us, ns 10m
  • Date arithmetic u2014 adding & subtracting timedeltas 10m
  • Creating date ranges with np.arange on datetime64 10m
  • Business day ops u2014 np.busday_count, np.busday_offset, np.is_busday 10m
  • Converting np.datetime64 u2194 Python datetime u2194 Pandas Timestamp 10m
  • Extracting date components (year, month, day) via .astype 10m
  • Real-world u2014 time-series feature engineering with datetime arrays 10m
Module 8: NumPy in the AI/Analytics Ecosystem 5 lessons • 50m
  • NumPy u2194 Pandas u2014 array to DataFrame and back 10m
  • NumPy u2194 Matplotlib u2014 plotting arrays 10m
  • NumPy u2194 Scikit-learn u2014 array handoff for ML models 10m
  • NumPy u2194 TensorFlow/PyTorch u2014 tensor conversion 10m
  • NumPy in ML pipelines u2014 feature engineering, normalization, batching 10m
Module 9: Conceptual & Interview Prep 5 lessons • 50m
  • When to use NumPy vs Pandas vs PyTorch 10m
  • Why vectorization matters u2014 explain with benchmarks 10m
  • Common interview traps u2014 copy vs view, broadcasting, dtype mismatch 10m
  • np.vectorize misconception u2014 why it doesn't speed up code 10m
  • Real-world use cases u2014 image as array, tabular data, embeddings 10m
Module 10: Image Processing with NumPy 1 lesson • 10m
  • Padding Elements 10m
Our Advantage

Why Choose This Course

  • 00% Free Course
  • Taught in Tamil
  • Beginner to Advanced
  • Hands-On Projects

Your Instructor

Parthiban Kannan

Parthiban Kannan

Co-founder & Manager

21 Courses

14+ years of experience in digital transformation, leading teams of 15+ members and delivering 160+ projects for organizations like Lakshmi Machine Works, Milacron, Schneider Electric, Aatomz Research, and Variablz Technologies. AI innovator with a patent approved by the Government of India, with strong expertise in Data Science, Data Analysis, Business Intelligence, and Software Development.

FAQ

Frequently Asked Questions

Is this course really 100% free?

Yes, the entire course is completely free and available on YouTube.

Do I need prior programming experience?

Yes, you should know the fundamentals of Python before starting this course.

What language is this course taught in?

The course is taught in Tamil, making it easy to learn and understand every concept clearly.

Is this course suitable for absolute beginners?

Yes, the course is structured to take you from fundamentals to advanced topics step-by-step.

Will this help me get into Data Analytics or Machine Learning roles?

Yes, NumPy is a core skill used across Data Analytics, Data Science, and Machine Learning workflows.

Does this course include hands-on practice?

Yes, every concept is paired with practical coding exercises and real examples.

Is Image Processing covered in this course?

Yes, there's a dedicated section on Image Processing using pure NumPy, without OpenCV.

How long will it take to complete the course?

The course is self-paced, so you can learn according to your own schedule and speed.

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