Machine Learning Course – Master Core ML Skills for 2025

1 Month Specialization

Machine Learning Course with projects, feature engineering, imbalanced data handling, model tuning, and deployment. Includes certificate and interview prep.

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Program fee

₹20,000

Why Join Our Machine Learning Course?

Master ML from Scratch Understand core algorithms like Linear Regression, Decision Trees, SVM, and Ensemble Methods with implementation focus. End-to-End ML Workflow Get hands-on with real projects — from data cleaning and EDA to model tuning, evaluation, and deployment. Imbalanced Data Handling Master techniques like SMOTE, undersampling, class-weighting, and ensemble balancing for skewed datasets. Feature Engineering Expertise Learn domain-driven feature construction, encoding tricks, interaction terms, binning, and PCA for dimensionality reduction. Hyperparameter Tuning Explore GridSearchCV, RandomizedSearch, and Optuna for tuning ML models in a reproducible way. ML Interview Preparation Sharpen your ML understanding with interview-style Q&A, mock sessions, and practical problem-solving skills. Model Evaluation Mastery Dive deep into confusion matrices, ROC-AUC, precision-recall curves, F1 scores, and threshold tuning. Real-World Use Cases Work on applied ML tasks in domains like finance, HR, sales forecasting, fraud detection, and churn prediction. Capstone Projects & Mentorship Build resume-grade ML pipelines with expert mentorship and GitHub-ready projects to crack job interviews.

Python & Pandas

Brush up Python, NumPy, Pandas for ML-ready datasets.

Feature Engineering

Encoding, scaling, PCA, leakage avoidance, data quality.

Supervised & Unsupervised

Linear/Logistic, SVM, Trees, KMeans, PCA—end to end.

Model Evaluation

CV, stratified splits, AUC/F1/PR, calibration, fairness.

FastAPI & Azure ML

Serve models, containerize, and ship to Azure ML.

Skills to Master in ML Training

Python for Machine Learning Mathematics for ML (Linear Algebra, Probability) EDA & Feature Engineering Techniques Handling Imbalanced Datasets Supervised ML Algorithms (Regression, Classification) Unsupervised ML (Clustering, Dimensionality Reduction) Model Evaluation Metrics & Cross-Validation Hyperparameter Tuning with GridSearch & Optuna Principal Component Analysis (PCA) Ensemble Methods (Bagging, Boosting, Random Forest) Model Interpretability (SHAP, LIME) Real-World ML Projects (End-to-End) ML Tools: Scikit-learn, Pandas, NumPy, Matplotlib ML Interview Preparation & Case Studies ML Deployment Basics with Streamlit & FastAPI

Interview Prep Sprints

Problem sets, case studies, and mock interviews focused on ML fundamentals & scenario questions.

Mentor Guidance

1:1 feedback on projects, resume pointers, and role-aligned guidance for transitions.

Transparent Placement Help

We don’t oversell. You get referrals where fit exists, plus portfolio polish and outreach strategy.

Explore Related Data Science Tracks

Data Science — ML End-to-end ML for analytics & product roles Data Science — Deep Learning CNNs, LSTMs, Transformers, computer vision & NLP Data Science — Gen AI RAG, prompt patterns, evaluation & governance Full-Stack Gen AI Agents, LangChain, FastAPI, deployment workflows Talk to a Mentor

MACHINE LEARNING FOUNDATIONS

Module 1: Python Refresher Module 2: Foundational Mathematics for Machine Learning Module 3: Data Preprocessing and Feature Engineering Module 4: Basic Machine Learning Models Module 5: Model Validation and Optimization Module 6: Time Series Analysis Module 7: Machine Learning Pipelines and Deployment

End-to-End ML Pipeline

✔ Build complete ML workflows: data preprocessing, feature engineering, training, tuning, and evaluation. ✘ Covers only basic model fitting with no structured pipeline approach.

Model Optimization (Optuna, GridSearchCV)

✔ Tune models efficiently using Optuna and cross-validation to achieve production-level accuracy. ✘ Focuses only on default parameters without tuning or validation strategies.

Imbalanced Data & Feature Engineering

✔ Apply SMOTE, scaling, encoding, and feature selection to handle real-world imbalance issues. ✘ Limited to clean toy datasets with no class imbalance handling.

Cloud Accessibility (Azure ML, AWS SageMaker)

✔ Train, track, and deploy models using Azure ML Studio and AWS SageMaker with free-tier guidance. ✘ No exposure to cloud tools or enterprise-level ML environments.

Explainability & Model Interpretation

✔ Understand SHAP, LIME, and error analysis to explain model predictions for stakeholders. ✘ Skips interpretability; focuses only on model accuracy numbers.

Deployment & CI/CD

✔ Deploy ML models via FastAPI and Docker with GitHub Actions for continuous delivery. ✘ Notebook-only learning; no API deployment or CI/CD exposure.

Certification & Placement

✔ Industry-recognized ML certification, resume projects, interview prep, and placement assistance. ✘ No certification, mentorship, or professional portfolio support.

What You’ll Get:

End-to-end ML pipeline: EDA, feature engineering, cross-validation, model comparison. Supervised & unsupervised learning, PCA , ensembles (RF/Boosting). Imbalanced data handling (SMOTE, thresholds, PR-AUC) and Optuna tuning. Clean scikit-learn code, experiment tracking, and mini deployment demo. Certification, interview prep sprints, resume/portfolio review & referral support. Lifetime access to recordings and ongoing updates.

Course Curriculum

Core algorithms

Module 1: Supervised methods

Regression and Classification
Ensembles
Imbalanced data

Module 2: Unsupervised methods

Clustering and Anomaly Detection
PCA
Feature Engineering

Tuning and trust

Module 3: Optimization

Hyperparameter Tuning
GridSearch and Optuna
Performance Optimization

Module 4: Interpretability

Model Interpretability
Error analysis
Mini deployment demo

Frequently asked questions

Why Join Our Machine Learning Course?

What You’ll Get:

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