Master Simplified Supervised Machine Learning™
Master Simplified Supervised Machine Learning™
Published 10/2024
Created by Dr. F.A.K. Noble
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 30 Lectures ( 14h 22m ) | Size: 7.6 GB
A Beginner-to-Advanced Deep MasterClass with Real Life Project Application
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.
Requirements
Anyone can learn this class it is very simple.
Description
Supervised Machine Learning: Mastering Predictive ModelsThis 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 OptimizationLinear 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 AlgorithmsDecision 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 TuningSupport 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 MetricsModel 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.
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