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COHORTLAB / INTERMEDIATE / LIVE LEARNING

Machine LearningLearn it. Build it. Understand it.

Understand the math. Write the code. Evaluate the model.

1 month (4 weeks)8 live sessions20 planned seats

01 / THE IDEA

Connect the intuition
with the implementation.

A four-week introduction to practical machine learning for students who already know basic Python. Across 8 live weekend sessions, implement regression models, work with datasets, and learn how to evaluate results without data leakage. No prior DSA cohort is required.

RamanandLearn with RamanandMeet your instructor ↗

02 / YOUR STARTING POINT

Is this your next step?

  • Students comfortable with Python functions, loops and lists
  • CS and non-CS students with basic algebra
  • Learners ready to work with notebooks and datasets

LET’S TALK

What would you like to know?

No account needed. Ask about a batch, the course, or a college-club session.

Your details and question will be saved so our team can respond to this inquiry.

03 / WHAT YOU’LL TAKE AWAY

Skills you can put into practice.

  1. 01

    Understand ML mathematically and intuitively

  2. 02

    Implement algorithms from scratch

  3. 03

    Work with real datasets

  4. 04

    Understand model evaluation and tuning

  5. 05

    Explain the choices and limitations in an end-to-end ML notebook

04 / THE LEARNING PATH

8 sessions. Connected ideas.

Open a session to explore what we’ll cover.

01SESSION 1Math for ML (Practical)
  • Linear algebra basics
  • Probability intuition
  • Gradients (conceptual)
02SESSION 2Linear Regression (From Scratch)
  • Cost function
  • Gradient descent
  • Implementation without libraries
03SESSION 3Logistic Regression
  • Classification concepts
  • Sigmoid function
  • Decision boundaries
04SESSION 4Feature Engineering + Evaluation
  • Train/test splits and avoiding data leakage
  • Data preprocessing
  • Overfitting/underfitting
  • Metrics: accuracy, precision, recall
05SESSION 5Decision Trees + Random Forest
  • Tree splitting logic
  • Entropy and Gini
  • Ensemble learning
06SESSION 6Unsupervised Learning
  • K-Means clustering
  • PCA
  • Clustering intuition
07SESSION 7SVM + Model Selection
  • Margin concept
  • Kernel trick (intuitive)
  • Model comparison
08SESSION 8ML in Real World + DL Intro
  • End-to-end ML pipeline
  • Case study
  • Intro to deep learning (bridge to next cohort)

LET’S FIGURE IT OUT TOGETHER

Still have a question?

Ask about the course, prerequisites, or the next batch.

LET’S TALK

What would you like to know?

No account needed. Ask about a batch, the course, or a college-club session.

Your details and question will be saved so our team can respond to this inquiry.