Udemy – Master Simplified Supervised Machine Learning™ 2024-10

What you will learn

• Introduction to Machine Learning: Understanding the basics and main concepts of Machine Learning.
• Reinforcement learning: learning how agents make decisions by interacting with the environment.
• An Introduction to Supervised Learning: Exploring How to Train Models Using Labeled Data for Prediction.
• Training and evaluation of machine learning models: learning techniques for training models and evaluating their performance.
• Linear Regression: Mastering how to predict continuous outcomes using linear regression.
• Assessing Model Fit: Learn how to assess accuracy and model fit for regression tasks.
• Application of supervised learning: application of supervised learning techniques to solve practical problems.
• An introduction to multiple linear regression: understanding how multiple predictors affect outcomes in regression models.
• Evaluating Multiple Linear Regression Model Performance: Learning how to evaluate and optimize multiple linear regression models.
• Application of Multiple Linear Regression: Application of multiple linear regression to real world data sets.
• Logistic Regression: Learning how to perform classification tasks using logistic regression.
• Feature Engineering for Logistic Regression: Mastering techniques to improve logistic regression with feature engineering.
• Application of logistic regression: application of logistic regression in practical classification problems.
• Decision Trees: Learn how to use decision trees to partition data for predictive decision making.
• Performance evaluation of decision trees: Discover how to evaluate the accuracy and reliability of decision trees.
• Application of Decision Trees: Application of Decision Tree Algorithms to Real World Data Sets.
• Random forests: Understanding how random forests combine multiple decision trees for robust prediction.
• Mastering Hyperparameter Tuning: Learn advanced techniques to optimize model performance through hyperparameter tuning.
• Decision Trees and Random Forests: Investigating How Random Forests Increase the Performance of Decision Trees.
• Support Vector Machines (SVM): Learning how to use SVM for classification by maximizing edge separation.
• Kernel Functions in Support Vector Machines (SVM): Understanding How to Improve SVM Classification of Nonlinear Data Using Kernel Functions.
• Application of Support Vector Machines (SVM): Application of SVM algorithms to classify complex datasets.
• K-Nearest Neighbor (KNN) Algorithm: Learning how KNN uses neighbors to classify data points.
• Preprocessing for KNN Algorithm: Master data preprocessing techniques to improve KNN performance.
• Application of KNN algorithm: Application of KNN algorithm to solve classification problems.
• Gradient boosting algorithm: learning how to improve prediction accuracy through gradient boosting iterative training.
• Mastering hyperparameter tuning in machine learning: learning to fine-tune model hyperparameters for maximum performance.
• Application of gradient boosting: Application of gradient boosting to increase model accuracy in real-world scenarios.
• Model evaluation criteria: Understanding key criteria such as accuracy and F1-score for evaluating machine learning models.
• ROC and AUC curves explained: learning to interpret ROC and AUC curves to evaluate classification models.

This course is suitable for people who:

• Anyone who wants to learn future skills and become a Data Scientist, AI Scientist, AI Engineer, AI Researcher and AI Specialist.

Installation guide

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