Learn how to build intelligent systems that learn from data and make accurate predictions using Machine Learning.
The Machine Learning course focuses on teaching computers to learn from data and improve performance without being explicitly programmed.
You will learn how to prepare data, train machine learning models, evaluate performance, and deploy predictive solutions using Python.
This course is hands-on and project-driven, designed to prepare you for machine learning roles and AI-focused careers.
Practical, industry-oriented learning path covering core Machine Learning concepts and projects.
What is Machine Learning, types of ML (Supervised, Unsupervised, Reinforcement), ML workflow & use cases.
Python fundamentals review, NumPy & Pandas, data preprocessing.
Handling missing values, encoding categorical data, feature scaling & selection.
Linear & Logistic Regression, Decision Trees, Random Forest, KNN.
K-Means clustering, Hierarchical clustering, Principal Component Analysis (PCA).
Train-test split, cross-validation, accuracy, precision & recall metrics.
Bias–variance tradeoff, hyperparameter tuning, overfitting & underfitting.
Neural network basics, difference between ML & DL, real-world use cases overview.
Model saving & loading, integrating ML models into applications.
Predictive analysis project, classification model project, end-to-end ML case study.
To build a career in machine learning by developing intelligent models that analyze data and make accurate predictions for real-world applications.
Learn how intelligent systems are built and become job-ready for AI and ML roles.
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