best machine learning course in jalandhar
- 2. CONTENTS: 1.WHAT IS MACHINE LEATRNING? 2.HISTORY AND EVOLUTION 3.TYPES OF MACHINE LEARNING 4.SUPERVISED LEARNING 5.UNSUPERVISED LEARNING 6.REINFORCEMENT LEARNING 7. KEY ML ALGORITHMS 8.MODEL EVOLUTION METRICES 9.THE ML PIPELINE
- 3. WHAT IS MACHINE LEARNING? • Definition: A field of AI that enables systems to learn from data • Difference between ML, AI, and Deep Learning • Categories: Supervised, Unsupervised, Reinforcement Learning MACHINE LEARNING IS A BRANCH OF ARTIFICIAL INTELLIGENCE (AI) THAT ENABLES COMPUTERS TO LEARN PATTERNS FROM DATA AND MAKE DECISIONS OR PREDICTIONS WITHOUT BEING EXPLICITLY PROGRAMMED.
- 4. HISTORY AND EVOLUTION: • Early AI systems (1950s– 1980s) • Rise of data-driven models (1990s–2000s) • Modern era: Deep Learning, Big Data, and real-time ML
- 5. TYPES OF MACHINE LEARNING? Supervised Learning Unsupervised Learning Reinforcement Learning
- 6. UNSUPERVISED LEARNING: No labeled data Examples: Customer segmentation, Market basket analysis • Algorithms: K-Means, PCA, Hierarchical Clustering
- 7. SUPERVISED LEARNING: • Input-output mapping • Examples: Spam detection, House price prediction • Algorithms: Linear Regression, Decision Trees, SVM
- 8. Learning through trial and error • Applications: Robotics, Game playing (e.g., AlphaGo) Key terms: Agent, Environment, Reward REINFORCEMENT LEARNING
- 9. KEY ML ALGORITHMS: • Decision Tres e& Random Forests • K-Nearest Neighbors (KNN) • Support Vector Machines (SVM) • Neural Networks • Linear Regression • Logistic Regression
- 10. MODEL EVOLUTION METRICS: • Accuracy, Precision, Recall, F1 Score • Confusion Matrix • ROC Curve & AUC
- 11. • Data Collection • Data Cleaning • Feature Engineering • Model Training • Evaluation • Deployment THE ML PIPELINE
- 12. • Languages: Python, R • Libraries: Scikit-learn, TensorFlow, Keras, PyTorch • Platforms: Google Colab, Jupyter Notebooks, AWS Sagemaker TOOLS AND LIBRARIES:
- 13. REAL WORLD APPLICATIONS: • Healthcare: Disease prediction • Finance: Fraud detection • Retail: Recommendation systems • Self-driving cars, NLP, Image recognition
- 14. CONCLUSION: • Recap of ML types and use cases • Recommended learning path (hands-on projects, deeper math) • Resources for further study (books, courses, websites)