Master Simplified Supervised Machine Learning™
Published 10/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 6.19 GB | Duration: 14h 22m
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.
