Admissions Open for JANUARY Batch
Understand the math behind regression, regularization, and model evaluation in ML.
Days : Tue & Thu
Duration : 10 Hours
Timings: 8 - 10 PM IST
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1 Months
5 Hours
Tue & Thu
Classical ML Foundations
Here, you’ll learn the mathematical engine that powers machine learning models. You’ll see how
models find patterns in data, how they measure errors, and how they avoid mistakes like overfitting. It covers everything from fitting a straight line through data to using probabilities for yes/no predictions and reducing complex data into simpler forms.
Online Live Instructor-Led Learning
10 Hours
8 - 10 PM IST
Tue & Thu
By end of this course
Get stronger in
Linear and logistic regression formulas
Dimensionality reduction using PCA
Get familier with
Loss functions and error measurement
Regularization methods to prevent overfitting
New Batch Starts : jan 2026
Limited seats only 15 students per batch
Who Should Enroll?
This course is for learners advancing to applied machine learning, with emphasis on regression math, dimensionality reduction, loss functions, and regularization for building ML models.
Prerequisites
Knowledge of calculus, linear algebra, and basic statistics.
Experience our course risk-free
We offer a 15-day money back guarantee
Prerequisite
Knowledge of calculus, linear algebra, and basic statistics.
Who Should Enroll?
This course is for learners advancing to applied machine learning, with emphasis on regression math, dimensionality reduction, loss functions, and regularization for building ML models.
By end of this course
Get Stronger in
- Linear and logistic regression formulas
- Dimensionality reduction using PCA
Get Familiar in
- Loss functions and error measurement
- Reguarization methods to prevent overfitting
Course Contents
What is covered: Predicting a value using a straight line (y = mx + c).
Application: Forecasting, trend analysis, basic ML models.
Example:
1.House Price Prediction: Using area and location to predict price with a linear equation.
2.Trend Analysis: Predicting sales growth over time.
What is covered: Predicting categories (yes/no) using probabilities.
Application: Classification tasks (spam/not spam, disease/no disease).
Example:
1. Email Spam Filter: Predicting if an email is spam (1) or not (0) using logistic regression.
2. Medical Diagnosis: Predicting if a patient has a disease based on symptoms.
What is covered: Reducing the number of features while keeping important information.
Application: Data compression, visualization, speeding up ML models.
Example:
1. Face Recognition: PCA reduces image data to key features for faster matching.
2. Visualizing High-Dimensional Data: Showing complex data in 2D plots.
What is covered: Techniques to prevent overfitting (model memorizing data instead of learning patterns).
Application: Improving model generalization, stability.
Example:
1.Ridge/Lasso Regression: Adding penalties to model complexity so it doesn’t overfit.
2.Dropout in Neural Nets: Randomly removing connections during training to improve robustness.
What is covered: Measuring how wrong a model’s predictions are.
Application: Training models, choosing best parameters.
Example:
1.Mean Squared Error (MSE): Used in regression to measure average error.
2.Cross-Entropy Loss: Used in classification to measure prediction accuracy.
What is covered: Predicting a value using a straight line (y = mx + c).
Application: Forecasting, trend analysis, basic ML models.
Example:
1.House Price Prediction: Using area and location to predict price with a linear equation.
2.Trend Analysis: Predicting sales growth over time.
What is covered: Predicting categories (yes/no) using probabilities.
Application: Classification tasks (spam/not spam, disease/no disease).
Example:
1. Email Spam Filter: Predicting if an email is spam (1) or not (0) using logistic regression.
2. Medical Diagnosis: Predicting if a patient has a disease based on symptoms.
What is covered: Reducing the number of features while keeping important information.
Application: Data compression, visualization, speeding up ML models.
Example:
1. Face Recognition: PCA reduces image data to key features for faster matching.
2. Visualizing High-Dimensional Data: Showing complex data in 2D plots.
What is covered: Techniques to prevent overfitting (model memorizing data instead of learning patterns).
Application: Improving model generalization, stability.
Example:
1.Ridge/Lasso Regression: Adding penalties to model complexity so it doesn’t overfit.
2.Dropout in Neural Nets: Randomly removing connections during training to improve robustness.
What is covered: Measuring how wrong a model’s predictions are.
Application: Training models, choosing best parameters.
Example:
1.Mean Squared Error (MSE): Used in regression to measure average error.
2.Cross-Entropy Loss: Used in classification to measure prediction accuracy.
Phase 1: From Coders to Creators
You’ll set up your professional coding environment by installing VS Code and Jupyter, introduce ChatGPT as a coding co-pilot, and learn to build effective prompts to generate code, establishing a productivity mindset for modern development.
Learn to reframe coding as building blocks for real applications by working with CSV, JSON, and image datasets from relatable domains like YouTube, food, and books, developing a system-level thinking approach.
Master abstraction, reusability, and clarity in logic by breaking down real-world use cases like meal planners and birthday reminders into modular code components using functions, loops, and conditions.
Build a functional CLI project such as a task tracker or GPA calculator, solving real-world problems like smart schedulers or basic calculators while developing ownership and confidence in your coding abilities