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A Beginner-to-Advanced Deep MasterClass with Real Life Project Application
Updated on Sep, 2026
IT and Software , Other IT and Software, Python
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Supervised Machine Learning: Deep Learning of Predictive Models This course focuses on giving you a detailed understanding of the basic principles and techniques in supervised machine learning. Learn how to build, train, and evaluate predictive models for real-world problems. Introduction to Machine Learning Explore the core principles and applications of machine learning. Reinforcement Learning Understand how reinforcement learning works and what sets it apart from supervised learning. Introduction to Supervised Learning Know how models learn with labelled data. Model Training and Evaluation: Know how you can train your model, including the numerous ways of measuring performance evaluation. Regression Models and Performance Optimization Linear Regression: Learn about the way which allows continuous outcomes in a linear regression fashion. Model Fit Evaluation: Learn to assess and tune regression models into better performing ones. Multiple Linear Regression: Improve your skill in modeling with multiple variables by learning from extension from the linear regression. Logistic Regression: Classification with Logistic Regression Learn logistic regression to solve classification tasks, from feature engineering to model interpretation. Advanced Decision-Making Algorithms Decision Trees Learn how decision trees return intuitive tree-like structures that are used for both classification and forecasting. Evaluating Decision Trees Learn how to evaluate a decision tree in terms of its accuracy and generalization. Random Forests Learn the concept of ensemble learning in random forests as well as how models increase robustness with respect to this technique. Advanced Techniques and Hyperparameter Tuning Support Vector Machines SVM: Understand how SVMs solve the classification problem using kernel functions to handle nonlinear data. K-Nearest Neighbor (KNN) Algorithm Learn about the KNN algorithm and what needs to be done before running it in order to get the best performance. Gradient Boosting Master the powerful ensemble technique that iteratively builds up to improve the precision of the model. Hyperparameter Tuning Learn advanced techniques for hyperparameter tuning so that models will perform at their best. Model Evaluation and Metrics Discuss the Metrics for the Performance of the Model: Learn the key metrics such as accuracy, precision, recall, and F1-score which form the criteria for model evaluation. Regarding the ROC Curve and AUC: Learn to use ROC curves and AUC scores in the evaluation of efficiency of the classification models. likecopy 326 words Use our AI Humanizer and Humanize AI Text absolutely for free.
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Anyone who wants to learn future skills and become Data Scientist, Ai Scientist, Ai Engineer, Ai Researcher & Ai Expert.
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