NeuralMinds
Welcome to NeuralMinds
This channel is dedicated to simplifying the world of Machine Learning and Deep Learning from the ground up.
Our goal is to help learners understand not just how ML and DL work, but why they work, focusing on the core logic, math, and intuition behind every algorithm.
What you’ll find here:
• In-depth explanations of ML and DL concepts
• Intuitive Math for Data Science
• Regular videos on trending AI topics
• Concept-first and clarity-driven teaching
Whether you are a beginner or looking to strengthen your foundation, AS NeuralMinds is your destination to learn Machine Learning and Artificial Intelligence the right way — with depth, understanding, and purpose.
Subscribe now and start your journey toward mastering Data Science, Machine Learning, and AI.
Python Problem Solving Part 2 | Loops, Lists, Tuples & Sets Explained Step-by-Step
Python Problem Solving for Beginners | Practice Questions with Step-by-Step Explanation
Functions in Python in Detail | Definition, Syntax, Arguments, Return Values & Examples
🎉 Thank You for Joining the Maths Series for Data Science! 🎉
Chi-Square Test, ANOVA, Types of ANOVA, p-Value Explained | Inferential Statistics Part 2
For Loop and While Loop in Python | Iteration, Range, Nested Loops & Examples
Python Conditional Statements & Operators | Python for Beginners Tutorial
Hypothesis Testing in Statistics | p-Value, Type I & II Errors, Z-Test, t-Test, Confidence Interval
Continuous Probability Distributions Part 2 | Exponential, Gamma, Beta, Weibull, Cauchy
Python Sets & Dictionaries Explained | Learn with Examples
Python Tuples Explained | Learn Tuple Basics with Examples
Python Lists Tutorial | Learn List in Python with Examples
Python Strings Tutorial | Learn String Basics in Python with Examples
Continuous Probability Distributions: Gaussian ,Standard Normal, Z-Score, Log Normal , Pareto
Python Variables & Data Types Explained | Python for Beginners
Distribution in Discrete Random Variables | Bernoulli, Binomial,Poisson & Zipf’s Law
Object-Oriented Principles in Python | Python Program Life Cycle | Python OOP Basics
Introduction to Probability & Distributions | Statistics for Data Science
5 Number Summary, Skewness & Correlation Explained | Statistics for Data Science – Part 2
Descriptive Statistics for Data Science | Mean, Median, Mode, Dispersion, Percentiles – Part 1
Types of Data & Scales of Measurement (Part 2) | Statistics for Machine Learning
Statistics For Machine Learning And Data Science | Types, Sampling and Applications – Part 1
Lines, Planes, and Hyperplanes in ML | Visual Intuition for Beginners
Eigen Values and Eigen Vectors Explained | Linear Algebra for Machine Learning
Functions, Vectors & Linear Transformations 🔥 | Math Behind Machine Learning
Linear Algebra for Data Science – Part 2 | Vectors: Scalar & Element-wise Multiplication + Matrices
Linear Algebra for Data Science – Part 1 | Vectors, Scalars & Their Use in ML🔥
📘 Maths for Data Science 🔢 | Complete Syllabus Explained for Beginners 🚀 #datascience#pwskills
INTRO VIDEO🔥 | DATA SCIENCE 👨💻 | ARTIFICIAL INTELLIGENCE🤖 | MACHINE LEARNING #data #pwskills