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Machine Learning Course – Master Core ML Skills for 2025

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

Build end-to-end ML pipelines with scikit-learn, cross-validation, Optuna, and PCA. Train and tune Linear/Logistic, Tree/Ensemble, and XGBoost models; handle imbalanced datasets; and deploy with FastAPI and Azure ML.

Designed for developers focused on core ML (not deep learning). Comes with certification, capstones, and job-readiness support.

  • Imbalanced & noisy data (SMOTE, class weights)
  • Feature engineering, scaling & selection
  • Model tuning with GridSearch & Optuna
  • FastAPI services & Azure ML deployment
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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.

Ideal for developers, analysts, and engineers who want to build production-ready ML pipelines—feature engineering, model selection, evaluation, and deployment with FastAPI & Azure ML.

Python and Pandas for data analysis

Python & Pandas

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

Feature engineering and preprocessing for ML

Feature Engineering

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

Supervised and unsupervised learning algorithms

Supervised & Unsupervised

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

Model evaluation metrics and validation

Model Evaluation

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

Deploy ML with FastAPI and Azure Machine Learning

FastAPI & Azure ML

Serve models, containerize, and ship to Azure ML.

Enquire Now : +91 96914 40998

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

Placement Support & Next Steps

We keep things transparent and skills-first. You’ll get structured interview prep, mentor feedback, and capstone review. If you’re aiming beyond core ML, explore our specialized Data Science tracks below.

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 Syllabus

Download Brochure

Complete the Machine Learning Course and earn a verified credential from the School of Core AI. This certificate attests to your ability to design scikit-learn pipelines, craft robust feature engineering steps, compare supervised and unsupervised models, address class imbalance, apply PCA for dimensionality reduction, and tune models with Optuna using rigorous cross-validation and metric-driven evaluation (AUC, F1, PR-AUC).

ADVANCEDCERTIFIED
SCHOOLOFCOREAI
OF ACHIEVEMENT
This certificate is presented to
Shweta Sharma

Has completed the Machine Learning Course, demonstrating practical mastery of data preprocessing, feature design, model selection, dimensionality reduction, and hyperparameter optimization for production-ready ML workflows.

Aishwarya Pandey
Founder & CEO
DD/MM/YY
SCAI-MLCORE-000123

Machine Learning Course vs Free Bootcamps

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.

Machine Learning Course – Fees

Hands-on Machine Learning course with supervised & unsupervised learning, feature engineering, model evaluation (AUC/F1), Optuna tuning, PCA, imbalanced data, and scikit-learn pipelines. Certification and interview prep included.

One-time Payment
₹20,000
₹20,000 flat — project-based ML training, certification, lifetime access, and placement assistance.

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.
Enroll Now

Machine Learning Salaries & Career Paths

This course builds core ML skills—from regression/classification and feature engineering to PCA, Optuna tuning, cross-validation and imbalanced data handling—so you can step into ML roles with a portfolio of practical, end-to-end projects.

India — Indicative Salary Bands

₹5–9 LPA (Entry)₹10–16 LPA (2–4 yrs)₹18–25 LPA+ (Experienced)

Ranges vary by city, domain, and portfolio depth. Strong pipelines (feature engineering, CV/metrics, Optuna tuning) help you stand out in interviews.

Global Trends (Typical)

$90K–$140K (US)€55K–€95K (EU)

Depends on stack (Python, pandas, scikit-learn), model evaluation rigor, and domain knowledge. A solid GitHub portfolio is a strong signal.

Roles You Can Target

  • • Machine Learning Engineer (Entry to Junior)
  • • Data Scientist (ML focus)
  • • Applied ML Engineer
  • • ML Analyst / Data Analyst (ML-ready)
  • • MLOps (foundational exposure)

Tip: showcase projects that cover EDA → feature engineering → modeling → metrics (AUC/F1/PR-AUC) → tuning (Optuna) → simple API demo.

Note: Salary figures are indicative and compiled from public listings and typical outcomes. Actual offers depend on skills, interview performance, location, and sector.

What Our Learners Say

Honest feedback from learners who mastered Machine Learning step by step

“Coming from a reporting background, I struggled to understand model metrics and overfitting. The module on bias-variance and cross-validation finally made things clear. Now I can build and explain regression models confidently to my team.”
Priya Menon
Data Analyst, Chennai
“The course moved systematically — from EDA to feature engineering, and then tuning with Optuna. The live projects helped me connect each concept to real data. My final project on loan default prediction actually impressed my manager.”
Arjun Singh
Software Engineer, Bangalore
“I was new to ML, but the clarity in teaching made even PCA and clustering easy to grasp. The visual explanations during unsupervised learning sessions were excellent. Now I’ve added two ML case studies to my resume.”
Ritika Shah
Graduate, Pune
“I liked how deployment wasn’t an afterthought. We learned FastAPI and Docker, then pushed our trained models live on Azure ML. It gave me the confidence to handle real-time inference at work.”
Nikhil Verma
Backend Developer, Delhi NCR
“Handling imbalanced data was something I always ignored before. After learning SMOTE, undersampling, and threshold tuning, my classification accuracy and recall both improved. The project feedback was detailed and personal.”
Ananya Rao
Business Analyst, Hyderabad
“Mock interviews were incredibly helpful — they focused on reasoning, not rote answers. I learned to explain model choice, metrics, and SHAP values in simple words. It helped me crack my first ML engineer interview.”
Rahul Mehta
ML Interview Prep Track, Gurgaon

Advanced Tracks After the Machine Learning Course

MLOps (Production ML)

CI/CD for models, experiment tracking, model registry, monitoring & drift handling. Ship reliable ML to production.

Explore track

AIOps (Ops with AI)

Automate incident detection, alert triage & RCA using ML + rule engines. Observability pipelines tuned with AI.

Explore track

LLMOps (LLM in Production)

Eval frameworks, prompt/version control, safety/guardrails, latency & cost optimization for LLM apps at scale.

Explore track

These are advanced specializations you can pursue after mastering core ML. Choose the ops track that matches your role and roadmap.

Your Questions Answered –MAchine Learning Course

Got More Questions?

Talk to Our Team Directly

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Enquire Now +91 96914 40998 info@schoolofcoreai.com
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