Machine Learning Literacy | Path
Год выпуска: 2021
Производитель: Pluralsight
Сайт производителя://app.pluralsight.com/paths/skill/machine-learning-literacy
Автор: Mohammed Osman / Janani Ravi
Продолжительность: 15h 45m
Тип раздаваемого материала: Видеоурок
Язык: Английский
Описание:
Machine Learning is the application of algorithms and mathematical models by software system to progressively improve their performance on a specific task. This skill covers the workflows, modeling techniques, and strategies behind any machine learning solution.
What you will learn:
The machine learning workflow ( data sourcing -> data cleaning -> data preparing -> data modeling and training -> model evaluation -> deployment -> monitoring & maintenance )
Commonly employed data models
Common techniques employed in machine learning (reinforcement learning, model validation strategies, etc)
Prerequisites:
Data Analytics Literacy
Python for Data Analysts
Statistics and Probability
Related Topics:
Machine Learning Literacy — Practical Application | Path
Deep Learning Literacy — Practical Application | Path
Deep Learning Literacy | Path
Feature Engineering (in Machine Learning) | Path
scikit-learn
PyTorch
TensorFlow
Working with Multidimensional Data Using NumPy
Building Data Visualizations Using Matplotlib
Pandas Fundamentals ► Advanced Pandas
Interpreting Data with Python | Path
Содержание
Building Your First Machine Learning Solution (Mohammed Osman, 2021)
1. Course Overview
2. Getting Your Feet Ready to Run
3. Feeding Your Machine Learning Pipeline
4. Understanding the Overall Data Trends
5. Making Your Data Ready for the ML Model
6. Implementing Your Regression Solution
7. What Is Next
Preparing Data for Machine Learning (Janani Ravi, 2019)
1. Course Overview
2. Understanding the Need for Data Preparation
3. Implementing Data Cleaning and Transformation
4. Transforming Continuous and Categorical Data
5. Understanding Feature Selection
6. Implementing Feature Selection
Designing a Machine Learning Model (Janani Ravi, 2019)
1. Course Overview
2. Exploring Approaches to Machine Learning
3. Choosing the Right Machine Learning Problem
4. Choosing the Right Machine Learning Solution
5. Building Simple Machine Learning Solutions
6. Designing Machine Learning Workflows
7. Building Ensemble Solutions and Neural Network Solutions
Creating Machine Learning Models (Janani Ravi, 2019)
1. Course Overview
2. Understanding Approaches to Machine Learning
3. Understanding and Implementing Regression Models
4. Understanding and Implementing Classification Models
5. Understanding and Implementing Clustering Model
Deploying Machine Learning Solutions (Janani Ravi, 2019)
1. Course Overview
2. Understanding Factors that Impact Deployed Models
3. Deploying Machine Learning Models to Flask
4. Deploying Machine Learning Models to Serverless Cloud Environments
5. Deploying Machine Learning Models to Google AI Platform
6. Deploying Deep Learning Models to AWS SageMaker
Файлы примеров: присутствуют
Субтитры: присутствуют
Формат видео: MP4
Видео: H.264/AVC, 1280x720, 16:9, 29fps, 120 kb/s
Аудио: AAC 48000Hz 2.0 chn 96 kbit/s
