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Keep shoppingMaster Simplified Supervised Machine Learning™
1 students
Master Simplified Supervised Machine Learning™
A Beginner-to-Advanced Deep MasterClass with Real Life Project Application
Last updated 10/2024
English
What you'll learn
- Introduction to Machine Learning: Understand the basics and core concepts of machine learning.
- Machine Learning - Reinforcement Learning: Learn how agents make decisions by interacting with their environment.
- Introduction to Supervised Learning: Explore how models are trained on labeled data to make predictions.
- Machine Learning Model Training and Evaluation: Learn techniques for training models and evaluating their performance.
- Machine Learning Linear Regression: Master how to predict continuous outcomes using linear regression.
- Machine Learning - Evaluating Model Fit: Learn how to assess model accuracy and fit for regression tasks.
- Application of Machine Learning - Supervised Learning: Apply supervised learning techniques to solve practical problems.
- Introduction to Multiple Linear Regression: Understand how multiple predictors influence outcomes in regression models.
- Multiple Linear Regression - Evaluating Model Performance: Learn how to assess and optimize multiple linear regression models.
- Machine Learning Application - Multiple Linear Regression: Apply multiple linear regression to real-world datasets.
- Machine Learning Logistic Regression: Learn how to perform classification tasks using logistic regression.
- Machine Learning Feature Engineering - Logistic Regression: Master techniques to improve logistic regression with feature engineering.
- Machine Learning Application - Logistic Regression: Apply logistic regression to practical classification problems.
- Machine Learning Decision Trees: Learn how decision trees split data to make predictive decisions.
- Machine Learning - Evaluating Decision Trees Performance: Discover how to assess the accuracy and reliability of decision trees.
- Machine Learning Application - Decision Trees: Apply decision tree algorithms to real-world datasets.
- Machine Learning Random Forests: Understand how random forests combine multiple decision trees for robust predictions.
- Master Machine Learning Hyperparameter Tuning: Learn advanced techniques for optimizing model performance through hyperparameter tuning.
- Machine Learning Decision Trees Random Forest: Explore how random forests enhance decision tree performance.
- Master Machine Learning - Support Vector Machines (SVM): Learn how SVMs are used for classification by maximizing margin separation.
- Master Machine Learning - Kernel Functions in Support Vector Machines (SVM): Understand how kernel functions improve SVM classification of non-linear data.
- Machine Learning Application - Support Vector Machines (SVM): Apply SVM algorithms to classify complex datasets.
- Machine Learning K-Nearest Neighbor (KNN) Algorithm: Learn how KNN uses neighbors to classify data points.
- Machine Learning Preprocessing for KNN Algorithm: Master data preprocessing techniques to improve KNN performance.
- Machine Learning Application - KNN Algorithm: Apply the KNN algorithm to solve classification problems.
- Machine Learning Gradient Boosting Algorithm: Learn how gradient boosting improves prediction accuracy through iterative training.
- Master Hyperparameter Tuning in Machine Learning: Learn to fine-tune model hyperparameters for maximum performance.
- Machine Learning Application of Gradient Boosting: Apply gradient boosting to enhance model accuracy in real-world scenarios.
- Machine Learning Model Evaluation Metrics: Understand key metrics like accuracy and F1-score for evaluating machine learning models.
- Machine Learning ROC Curve and AUC Explained: Learn to interpret ROC curves and AUC for assessing classification models.
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Course content
30 sections • 30 lectures • 14h 22m total length
Requirements
- Anyone can learn this class it is very simple.
Description
Supervised Machine Learning: Mastering Predictive Models
This course provides a deep dive into the fundamental concepts and techniques of supervised machine learning. You will learn how to build, train, and evaluate predictive models to solve real-world problems.
Introduction to Machine Learning: Explore the principles of machine learning and its applications.
Reinforcement Learning: Understand the role of reinforcement learning and its distinction from supervised learning.
Introduction to Supervised Learning: Gain insights into how models are trained using labeled data.
Model Training and Evaluation: Learn the process of model training, including performance evaluation techniques.
Regression Models and Performance Optimization
Linear Regression: Discover how linear regression is used to model continuous outcomes.
Evaluating Model Fit: Master techniques to evaluate and refine regression models for better performance.
Multiple Linear Regression: Dive into modeling with multiple variables, extending linear regression capabilities.
Logistic Regression: Understand classification tasks using logistic regression, with a focus on feature engineering and model interpretation.
Advanced Decision-Making Algorithms
Decision Trees: Learn how decision trees create intuitive, tree-like structures for classification and regression tasks.
Evaluating Decision Tree Performance: Explore methods to evaluate decision trees for accuracy and generalization.
Random Forests: Understand ensemble learning through random forests and how they improve model robustness.
Advanced Techniques and Hyperparameter Tuning
Support Vector Machines (SVM): Learn how SVMs optimize classification tasks, including the use of kernel functions for non-linear data.
K-Nearest Neighbor (KNN) Algorithm: Explore the KNN algorithm and its preprocessing requirements for optimal performance.
Gradient Boosting: Master this powerful ensemble technique that iteratively improves model accuracy.
Hyperparameter Tuning: Discover advanced strategies to tune hyperparameters for improved model performance.
Model Evaluation and Metrics
Model Evaluation Metrics: Grasp key metrics such as accuracy, precision, recall, and F1-score for model evaluation.
ROC Curve and AUC Explained: Learn how to use ROC curves and AUC scores to evaluate classification model performance.
Who this course is for:
- Anyone who wants to learn future skills and become Data Scientist, Ai Scientist, Ai Engineer, Ai Researcher & Ai Expert.