Machine Learning with Python - IBM (training)

Colleagues, the “Machine Learning with Python” training from IBM will equip you to utilizeScikit-learn to build, test, and evaluate models, apply data preparation techniques and manage bias-variance trade-offs to optimize model performance, implement core machine learning algorithms, including linear regression, decision trees, and SVM, for classification and regression tasks, and evaluate model performance using metrics, cross-validation, and hyperparameter tuning to ensure accuracy and reliability. Gain highly marketable skills involving Machine Learning, Clustering, regression, classification, SciPy and scikit-learn. You’ll explore supervised learning techniques with libraries such as TensorFlow and Pandas, as well as classification methods like decision trees, KNN, and SVM, covering key concepts like the bias-variance tradeoff. The course also covers unsupervised learning, including clustering and dimensionality reduction. With guidance on model evaluation, tuning techniques, and practical projects in Jupyter Notebooks, you’ll gain the Python skills that power your ML journey. Skill-based training modules include: 1) Introduction to Machine Learning, 2) Linear and Logistic Regression, 3) Building Supervised Learning Models, 4) Building Unsupervised Learning Models, 5) Evaluating and Validating Machine Learning Models, and 6) Final Project and Exam. 

Enroll today (teams & execs welcome): https://imp.i384100.net/VxZMZ6 

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