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best machine learning course in jalandhar

TechCadd is the best IT training institute in Jalandhar, offering expert-led courses in AI, Data Science, MEAN, MERN, and more.Enhance your skills and career prospects.<br>visit now:https://techcadd.com/best-machine-learning-course-in-jalandhar.php

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best machine learning course in jalandhar

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  1. CONTACT NO:9888122255 VISIT NOW:HTTPS://TECHCADD.COM/ MACHINE LEARNING COURSE

  2. CONTENTS: • WHAT IS MACHINE LEATRNING? • HISTORY AND EVOLUTION • TYPES OF MACHINE LEARNING • SUPERVISED LEARNING • UNSUPERVISED LEARNING • REINFORCEMENT LEARNING • KEY ML ALGORITHMS • MODEL EVOLUTION METRICES • THE ML PIPELINE • TOOLS AND LIBRARIES • REAL WORLD APPLICATIONS • CONCLUSION

  3. WHAT IS MACHINE 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. • Definition: A field of AI that enables systems to learn from data • Difference between ML, AI, and Deep Learning • Categories: Supervised, Unsupervised, Reinforcement Learning

  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. Supervised Learning Unsupervised Learning Reinforcement Learning TYPES OF MACHINE LEARNING?

  6. No labeled data Examples: Customer segmentation, Market basket analysis • Algorithms: K-Means, PCA, Hierarchical Clustering UNSUPERVISED LEARNING:

  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: • Linear Regression • Logistic Regression • Support Vector Machines (SVM) • Neural Networks • Decision Tres e& Random • Forests • K-Nearest Neighbors (KNN)

  10. MODEL EVOLUTION METRICS: • Accuracy, Precision, Recall, F1 Score • Confusion Matrix • ROC Curve & AUC

  11. THE ML PIPELINE • Data Collection • Data Cleaning • Feature Engineering • Model Training • Evaluation • Deployment

  12. TOOLS AND LIBRARIES: • Languages: Python, R • Libraries: Scikit-learn, TensorFlow, Keras, PyTorch • Platforms: Google Colab, Jupyter Notebooks, AWS Sagemaker

  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 • Resources for further study (books, courses, websites) • Recommended learning path (hands-on projects, deeper math)

  15. THANKYOU....... visit now:https://techcadd.com/

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