BTech / BCA Track8 Weeks CohortTopic-Linked Attendance Enabled
Machine Learning — Basic
Learn foundational machine learning workflows. Covers data preprocessing with Scikit-Learn, linear/logistic regression, decision trees, KNN, evaluation metrics (ROC-AUC, F1), and K-Means clustering.
Key Skills You Will Master:
Scikit-LearnFeature EngineeringRegressionClassificationModel EvaluationK-Means
Lead Instructor
Vikramaditya Iyer
Ex-Tech Lead & STEM Mentor
Total Program Fee (Full Cohort + Materials)
₹3,499₹4,999Save 30%
Choose Your CohortLive Interactive
Next cohort starting next weekend • 5:00 PM IST
10 Live Hybrid Interactive Sessions
10 Granular Tracked Topics
Downloadable datasets, cheatsheets & Jupyter notebooks
Verifiable Certificate upon 85%+ topic completion
Syllabus Breakdown
Course Structure & Tracked Topics
Click on any module to view live sessions and the exact list of topics tested & tracked for attendance.
Session 1
Session 1: ML Landscape & Workflow
90 mins
Attendance-Tracked Topics (1):
1Supervised/Unsupervised Learning & ML Project Lifecycle
Session 2
Session 2: Data Preprocessing & Scaling
90 mins
Attendance-Tracked Topics (1):
1Imputation, OneHotEncoder, StandardScaler, MinMaxScaler
Session 3
Session 3: Train-Test Split & Overfitting
90 mins
Attendance-Tracked Topics (1):
1train_test_split, Bias vs Variance, Baseline Models
Beginner Milestone: Cleaned & Scaled ML Data Pipeline
Construct a reusable Python preprocessing pipeline that returns scaled matrices ready for training.
Deliverable: Python Scikit-Learn preprocessing pipeline.
Session 4
Session 4: Linear Regression (Simple & Multiple)
90 mins
Attendance-Tracked Topics (1):
1Cost Function, Gradient Descent Concept, R2 Score
Session 5
Session 5: Logistic Regression for Classification
90 mins
Attendance-Tracked Topics (1):
1Sigmoid Function, Log-Loss & Binary Decision Boundaries
Session 6
Session 6: Classification Metrics & Evaluation
90 mins
Attendance-Tracked Topics (1):
1Confusion Matrix, Precision, Recall, F1-Score, ROC-AUC
Session 7
Session 7: KNN & Decision Trees
90 mins
Attendance-Tracked Topics (1):
1Distance Metrics, Gini Impurity, Tree Pruning
Intermediate Milestone: Automated Bank Loan Risk Classifier
Benchmark Logistic Regression, KNN, and Decision Trees on a 20,000-row lending dataset.
Deliverable: Model comparison notebook with Precision-Recall and ROC-AUC curves.
Session 8
Session 8: K-Means Clustering
90 mins
Attendance-Tracked Topics (1):
1Centroid Initialization, Inertia & Elbow Method
Session 9
Session 9: Cross-Validation & Model Tuning
90 mins
Attendance-Tracked Topics (1):
1K-Fold Cross-Validation, GridSearchCV & Feature Importance
Session 10
Session 10: Capstone Presentation
90 mins
Attendance-Tracked Topics (1):
1CLV & Churn Pipeline Presentation
Graduation Capstone
Customer Lifetime Value (CLV) & Churn Prediction Platform
Build an ML pipeline predicting customer churn and lifetime value with automated feature engineering, cross-validation, and segmentations.
Deliverable:
Jupyter Notebook Pipeline + Streamlit Model Prediction UI
What You Will Be Able To Do
Preprocess messy data with standard scaling, one-hot encoding, and train-test splits
Train and evaluate regression and classification algorithms in Scikit-Learn
Diagnose model overfitting/underfitting and tune hyperparameters
Segment customer datasets using unsupervised K-Means clustering
Prerequisites
Python fundamentals.
Unsure if this fits your current grade?
Schedule a 10-minute discovery call with our academic advisor.