Master Simplified Supervised Machine Learning™

Goals

• Introduction to Machine Learning: The basics and core concepts of machine learning.
  Machine Learning - Reinforcement Learning: How agents learn to act by interacting with their environment.
  Introduction to Supervised Learning: How models get trained on labelled data to make prediction.
  Machine Learning Model Training and Evaluation: Learn techniques for training models and how to evaluate them
  Machine Learning Linear Regression: Master how to predict continuous outcomes with linear regression.
  Machine Learning - Model Fit Evaluation Learn how to determine whether models for regression tasks are correct and a good fit.
  Application of Machine Learning - Supervised Learning Apply supervised learning to realworld problems.
  Multiple Linear Regression - Introduction Understand what happens when it is possible that more than one predictor affects the output of regression models
  Multiple Linear Regression - Model Performance Evaluation Learn to evaluate and improve multiple linear regression models.
  Application of Machine Learning - Multiple Linear Regression: Multivariate linear regression on real-world datasets.
  Machine Learning Logistic Regression: How logistic regression could be used to classify such tasks.
  Machine Learning Feature Engineering - Logistic Regression: How feature engineering techniques improve logistic regression
  Application of Machine Learning - Logistic Regression: Logistic regression for practical classification problems
  Machine Learning Decision Trees: How decision trees are implemented such that data can be split and a prediction decision can be made.
  Machine Learning-Classification: How to Evaluate the Performance of Decision Trees. Learn how to check whether the decision tree is accurate and robust or not.
  Machine Learning-Application of Decision Trees. Implementation of decision tree algorithms on practical datasets.
  Machine Learning-Random Forests. How random forests combine multiple decision trees to form an even stronger predictor
  Mastering Machine Learning-Hyperparameter Tuning. Advanced techniques for improving model performance with hyperparameter tuning.
  Machine Learning Decision Trees Random Forest Discover how random forests improve the performance of decision trees.
  Master Machine Learning-Support Vector Machines (SVM): Learn how SVMs can be applied to classification by maximizing the margin between classes
  Master Machine Learning-Kernel Functions in Support Vector Machines (SVM): Learn how kernel functions make SVM classifiable even for nonlinear datasets
  Machine Learning Application - Support Vector Machines (SVM): Apply SVM algorithms to classify complex data sets.
  Machine Learning K-Nearest Neighbor Algorithm: Learn how the neighbors classify data points
  Machine Learning Data Preprocessing for KNN Algorithm: Master how to preprocess your data to get the best out of the KNN algorithm
  Application of Machine Learning: KNN Algorithm Apply the KNN Algorithm to solve classification problems
  Machine Learning Gradient Boosting Algorithm Learn gradient boosting algorithm that work by boosting its predictive ability with iterative training
  Mastering Hyperparameter Tuning in Machine Learning. Learn how to fine-tune model hyperparameters for the best performance.
  Practice Applying Gradient Boosting in Machine Learning. How to Improve model accuracy in real-world applications.
  Metrics of Machine Learning Model Evaluation. How to measure a machine learning model using the most important metric-like accuracy and F1-score.
  Machine Learning ROC Curve and AUC Learn the interpretation of the ROC curve and AUC for the evaluation of a classification model.

Prerequisites

Anyone can learn this class it is very simple.

Anyone who wants to learn future skills and become Data Scientist, Ai Scientist, Ai Engineer, Ai Researcher & Ai Expert.