Prisoner classification interviewer education requirements

1. Supervised Machine Learning: Classification

This course introduces you to one of the main types of modeling families of supervised Machine Learning: Classification. You will learn how to train predictive models to classify categorical outcomes and how to use error metrics to compare across different models. The hands-on section of this course focuses on using best practices for classification, including train and test splits, and handling data sets with unbalanced classes. By the end of this course you should be able to: -Differentiate...

2. Basic Image Classification with TensorFlow

In this 2-hour long project-based course, you will learn the basics of using Keras with TensorFlow as its backend and use it to solve a basic image classification problem. By the end of this project, you will have created, trained, and evaluated a Neural Network model that will be able to predict digits from hand-written images with a high degree of accuracy. You also will have learned the fundamentals of neural networks, TensorFlow, and Keras. Note: This course works best for learners who are...

3. Machine Learning for Data Analysis: Classification Modeling

4.7

(439)

Machine Learning made simple with Excel! Classification for advanced data analysis & business intelligence (no coding!)...

4. Job Interview Skills Training Course Successful Interviews

4.4

(376)

Job Interview Skills Training to hit the ground running Interview like a PRO and achieve success in your interviews...

5. Regression and Classification

Introduction to Statistical Learning will explore concepts in statistical modeling, such as when to use certain models, how to tune those models, and if other options will provide certain trade-offs. We will cover Regression, Classification, Trees, Resampling, Unsupervised techniques, and much more! This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that...

6. Language Classification with Naive Bayes in Python

In this 1-hour long project, you will learn how to clean and preprocess data for language classification. You will learn some theory behind Naive Bayes Modeling, and the impact that class imbalance of training data has on classification performance. You will learn how to use subword units to further mitigate the negative effects of class imbalance, and build an even better model...

7. Land use Land cover classification GIS, ERDAS, ArcGIS, ML

4.6

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Machine Learning, Land Use Scratch to Advance, All Softwares of Remote Sensing and GIS, GIS Tasks in Easy way learning...

8. Machine Learning Classification Bootcamp in Python

4.5

(934)

Build 10 Practical Projects and Advance Your Skills in Machine Learning Using Python and Scikit Learn...

9. Image Classification with Amazon Sagemaker

Please note: You will need an AWS account to complete this course. Your AWS account will be charged as per your usage. Please make sure that you are able to access Sagemaker within your AWS account. If your AWS account is new, you may need to ask AWS support for access to certain resources. You should be familiar with python programming, and AWS before starting this hands on project. We use a Sagemaker P type instance in this project, and if you don't have access to this instance type, please...

10. Support Vector Machine Classification in Python

In this 1-hour long guided project-based course, you will learn how to use Python to implement a Support Vector Machine algorithm for classification. This type of algorithm classifies output data and makes predictions. The output of this model is a set of visualized scattered plots separated with a straight line. You will learn the fundamental theory and practical illustrations behind Support Vector Machines and learn to fit, examine, and utilize supervised Classification models using SVM to...

11. Electric Vehicle Battery Charging Motor Components - Basics

4.3

(425)

ELECTRIC VEHICLE BASICS LITHIUM ION BATTERY MANAGEMENT SYSTEM CHARGING TECHNOLOGY MOTOR EV COMPONENT BMS JOB...

12. Job Interviewing: Complete Job Interview Success Course 6HR

4.8

(747)

Job Interviewing is critical. Learn all key Job Interview skills + demo on answering specific job interview questions...

13. Scikit-Learn For Machine Learning Classification Problems

Hello everyone and welcome to this new hands-on project on Scikit-Learn Library for solving machine learning classification problems. In this project, we will learn how to build and train classifier models using Scikit-Learn library. Scikit-learn is a free machine learning library developed for python. Scikit-learn offers several algorithms for classification, regression, and clustering. Several famous machine learning models are included such as support vector machines, random forests,...

14. Decision Tree and Random Forest Classification using Julia

This guided project is about glass classification using decision tree classification and random forest classification in Julia. It is ideal for beginners who do not know what decision trees or random forests are because this project explains these concepts in simple terms. While you are watching me code, you will get a cloud desktop with all the required software pre-installed. This will allow you to code along with me. After all, we learn best with active, hands-on learning. Special features:...

15. Interviewing Skills for Jobs: Ace the Job Interview

4.1

(700)

Interviewing Skills - How to ace any job interview-Excel in all Job Interviewing skills...

16. Classification and valuation - What you must know!

This is the second course in Tax Academy's Customs Certification. This certification would provide you with relevant skills to apply Customs knowledge in less than two months. In this course, you will build upon your knowledge on legal principles of classification and valuation of goods. Once you are able to correctly classify and declare the value of goods, you will be able to take accurate decisions about Customs duty payment and ascertain other compliance requirements. Correct classification...

17. Handling Imbalanced Data Classification Problems

In this 2-hour long project-based course on handling imbalanced data classification problems, you will learn to understand the business problem related we are trying to solve and and understand the dataset. You will also learn how to select best evaluation metric for imbalanced datasets and data resampling techniques like undersampling, oversampling and SMOTE before we use them for model building process. At the end of the course you will understand and learn how to implement ROC curve and...

18. Simple Nearest Neighbors Regression and Classification

In this 2-hour long project-based course, we will explore the basic principles behind the K-Nearest Neighbors algorithm, as well as learn how to implement KNN for decision making in Python. A simple, easy-to-implement supervised machine learning algorithm that can be used to solve both classification and regression problems is the k-nearest neighbors (KNN) algorithm. The fundamental principle is that you enter a known data set, add an unknown data point, and the algorithm will tell you which...

19. Logistic Regression for Classification using Julia

This guided project is about book genre classification using logistic regression in Julia. It is ideal for beginners who do not know what logistic regression is because this project explains these concepts in simple terms. While you are watching me code, you will get a cloud desktop with all the required software pre-installed. This will allow you to code along with me. After all, we learn best with active, hands-on learning. Special features: 1) Simple explanations of important concepts. 2)...