Machine Learning Basics
Category: IT & Software
Course Info
Overview
Develop a Machine Learning Basics arsenal of skills including critical strategic, managerial, and leadership abilities with our expertly developed Machine Learning Basics.
To stay ahead of the curve, companies are now embracing eLearning in the most inventive methods to develop an excellent workforce and produce optimal outcomes to get the most out of their investments. To align with your goals and your workplace objectives, Learning Paths has been working on creating the most effective and dynamic courses.
Through an immersive online experience, Learning Paths offers market-driven courses that empower you or any working professional with the competence required for the workplace of the future. We assess future skill demands using a data-driven methodology and ensure that all of our courses satisfy this need. This Machine Learning Basics is no exception.
This platform provides you with exclusive resources to help you along your professional path, as well as the tools you’ll need to further your career.
To enhance your learning outcomes, this career-focused Machine Learning Basics curriculum employs a variety of interactive modules to provide you with abilities that are relevant to your career. This Machine Learning Basics was also created with professionals in mind and is tailored to fit with your busy schedule.
Sign up for the Machine Learning Basics today and build core Machine Learning Basics skills for tackling any complex challenges.
Description
This Machine Learning Basics will teach you how to think critically and strategically about Machine Learning Basics, as well as how to build and implement strategy. You’ll also discover fundamental Machine Learning Basics ideas that will help you build the foundation you need to flourish in the workplace.This Learning Paths online Machine Learning Basics is for working people who want to improve their abilities and advance in their careers. The Machine Learning Basics is provided in an interactive virtual learning environment where you can study at your own comfort and convenience.
Who is this course for?
This certificate Machine Learning Basics is curated for individuals who want to improve their hard and soft skills. The interactive, guided approach to learning and the potential to build their worldwide network online will help you thrive. Moreover, working professionals in managerial and leadership roles in a variety of industries will benefit from the emphasis on the dynamics of leadership, influence, and strategy, as well as Machine Learning Basics abilities. Those pursuing career advancement in the future will benefit from the skill to apply Machine Learning Basics-derived abilities to current and future employment.
Requirements
This Learning Paths Machine Learning Basics will prepare you to make data-driven decisions that will give you a competitive edge. There are no formal requirements for this course. However smart gadgets and stable internet connection is required for a smooth learning journey.
Career Path
Earn a certificate of competence from the Learning Paths platform by learning all about Machine Learning Basics. This Machine Learning Basics curriculum will benefit you at all stages of your career.
Certification
After successfully completing the Machine Learning Basics, you will get your PDF certificate for FREE! The hardcopy certificate will cost only £11.99 with free shipping inside the UK. For delivery outside the UK an additional shipping charge will be applied.
Course Curriculum
| Section 01: Introduction | |||
| Introduction to Supervised Machine Learning | 00:06:00 | ||
| Section 02: Regression | |||
| Introduction to Regression | 00:13:00 | ||
| Evaluating Regression Models | 00:11:00 | ||
| Conditions for Using Regression Models in ML versus in Classical Statistics | 00:21:00 | ||
| Statistically Significant Predictors | 00:09:00 | ||
| Regression Models Including Categorical Predictors. Additive Effects | 00:20:00 | ||
| Regression Models Including Categorical Predictors. Interaction Effects | 00:18:00 | ||
| Section 03: Predictors | |||
| Multicollinearity among Predictors and its Consequences | 00:21:00 | ||
| Prediction for New Observation. Confidence Interval and Prediction Interval | 00:06:00 | ||
| Model Building. What if the Regression Equation Contains “Wrong” Predictors? | 00:13:00 | ||
| Section 04: Minitab | |||
| Stepwise Regression and its Use for Finding the Optimal Model in Minitab | 00:13:00 | ||
| Regression with Minitab. Example. Auto-mpg: Part 1 | 00:17:00 | ||
| Regression with Minitab. Example. Auto-mpg: Part 2 | 00:18:00 | ||
| Section 05: Regression Trees | |||
| The Basic idea of Regression Trees | 00:18:00 | ||
| Regression Trees with Minitab. Example. Bike Sharing: Part 1 | 00:15:00 | ||
| Regression Trees with Minitab. Example. Bike Sharing: Part 2 | 00:10:00 | ||
| Section 06: Binary Logistics Regression | |||
| Introduction to Binary Logistics Regression | 00:23:00 | ||
| Evaluating Binary Classification Models. Goodness of Fit Metrics. ROC Curve. AUC | 00:20:00 | ||
| Binary Logistic Regression with Minitab. Example. Heart Failure: Part 1 | 00:16:00 | ||
| Binary Logistic Regression with Minitab. Example. Heart Failure: Part 2 | 00:18:00 | ||
| Section 07: Classification Trees | |||
| Introduction to Classification Trees | 00:12:00 | ||
| Node Splitting Methods 1. Splitting by Misclassification Rate | 00:20:00 | ||
| Node Splitting Methods 2. Splitting by Gini Impurity or Entropy | 00:11:00 | ||
| Predicted Class for a Node | 00:06:00 | ||
| The Goodness of the Model – 1. Model Misclassification Cost | 00:11:00 | ||
| The Goodness of the Model – 2 ROC. Gain. Lit Binary Classification | 00:15:00 | ||
| The Goodness of the Model – 3. ROC. Gain. Lit. Multinomial Classification | 00:08:00 | ||
| Predefined Prior Probabilities and Input Misclassification Costs | 00:11:00 | ||
| Building the Tree | 00:08:00 | ||
| Classification Trees with Minitab. Example. Maintenance of Machines: Part 1 | 00:17:00 | ||
| Classification Trees with Miitab. Example. Maintenance of Machines: Part 2 | 00:10:00 | ||
| Section 08: Data Cleaning | |||
| Data Cleaning: Part 1 | 00:16:00 | ||
| Data Cleaning: Part 2 | 00:17:00 | ||
| Creating New Features | 00:12:00 | ||
| Section 09: Data Models | |||
| Polynomial Regression Models for Quantitative Predictor Variables | 00:20:00 | ||
| Interactions Regression Models for Quantitative Predictor Variables | 00:15:00 | ||
| Qualitative and Quantitative Predictors: Interaction Models | 00:28:00 | ||
| Final Models for Duration and TotalCharge: Without Validation | 00:18:00 | ||
| Underfitting or Overfitting: The “Just Right Model” | 00:18:00 | ||
| The “Just Right” Model for Duration | 00:16:00 | ||
| The “Just Right” Model for Duration: A More Detailed Error Analysis | 00:12:00 | ||
| The “Just Right” Model for TotalCharge | 00:14:00 | ||
| The “Just Right” Model for ToralCharge: A More Detailed Error Analysis | 00:06:00 | ||
| Section 10: Learning Success | |||
| Regression Trees for Duration and TotalCharge | 00:18:00 | ||
| Predicting Learning Success: The Problem Statement | 00:07:00 | ||
| Predicting Learning Success: Binary Logistic Regression Models | 00:16:00 | ||
| Predicting Learning Success: Classification Tree Models | 00:09:00 | ||
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