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Machine Learning Practical Course with scikit-learn

Build, evaluate and tune real ML models in Python with scikit-learn, from regression to clustering

Ghosla Academy · AI Team

Machine learning interviews in India test whether you can take a dataset, prepare it properly, train a model and honestly report how good it is. This course teaches exactly that workflow using scikit-learn, the standard Python library for classical ML. You will work through regression, classification, model selection and clustering with small, realistic datasets such as used-car listings, loan applications and shopper data, and finish with two portfolio projects and a saved model you can reuse.

What you will be able to do

  • Explain supervised and unsupervised learning with clear examples
  • Prepare data with train/test splits, imputers, scalers and encoders
  • Train and evaluate regression models using MAE, RMSE and R²
  • Train classifiers and judge them with precision, recall and F1
  • Tune models with cross-validation and GridSearchCV
  • Cluster data with k-means and save models with joblib

What you build

  • Used-car price predictor with regression
  • Loan approval classifier with tuned models

What you need to start

  • Comfort with basic Python (variables, lists, functions)
  • Some familiarity with pandas is helpful but not required
  • A laptop that can run Jupyter Notebook

Curriculum · 6 sections, 24 lectures

Machine learning foundations4 lectures
  1. What machine learning really isFree preview10 min
  2. Setting up Jupyter and scikit-learnFree preview12 min
  3. The fit and predict pattern12 min
  4. Quiz: ML foundations6 min
Preparing data4 lectures
  1. Train/test split and why it matters12 min
  2. Scaling and encoding with pipelines16 min
  3. Handling missing values with SimpleImputer10 min
  4. Quiz: preparing data6 min
Regression4 lectures
  1. Linear regression and its metrics14 min
  2. Regularisation with Ridge and Lasso12 min
  3. Project: used-car price predictor20 min
  4. Quiz: regression6 min
Classification4 lectures
  1. Logistic regression and k-nearest neighbours14 min
  2. Confusion matrix, precision, recall and F114 min
  3. Decision trees and random forests14 min
  4. Quiz: classification6 min
Model selection4 lectures
  1. Cross-validation with cross_val_score12 min
  2. Hyperparameter tuning with GridSearchCV14 min
  3. Project: loan approval classifier22 min
  4. Quiz: model selection6 min
Clustering and next steps4 lectures
  1. K-means clustering for customer segments14 min
  2. Saving and loading models with joblib10 min
  3. Responsible ML: bias and fairness basics10 min
  4. Quiz: clustering and deployment6 min

Who this is for

  • Engineering and science students starting machine learning
  • Data analysts who want to move into predictive modelling
  • Freshers preparing for ML or data science interviews