Launching into Machine Learning
5.0 /5- Offered byCoursera
Launching into Machine Learning at Coursera Overview
Duration | 22 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Beginner |
Official Website | Explore Free Course  |
Credential | Certificate |
Launching into Machine Learning at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Intermediate Level
- Approx. 22 hours to complete
- English Subtitles: English
Launching into Machine Learning at Coursera Course details
- Starting from a history of machine learning, we discuss why neural networks today perform so well in a variety of data science problems. We then discuss how to set up a supervised learning problem and find a good solution using gradient descent. This involves creating datasets that permit generalization; we talk about methods of doing so in a repeatable way that supports experimentation.
- Course Objectives:
- Identify why deep learning is currently popular
- Optimize and evaluate models using loss functions and performance metrics
- Mitigate common problems that arise in machine learning
- Create repeatable and scalable training, evaluation, and test datasets
- This course is part of multiple programs
- This course can be applied to multiple Specializations or Professional Certificates programs. Completing this course will count towards your learning in any of the following programs:
- Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate
- Machine Learning with TensorFlow on Google Cloud Platform Specialization
Launching into Machine Learning at Coursera Curriculum
Introduction to Course
Intro to Course
Getting Started with Google Cloud and Qwiklabs
Introduction
Improve Data Quality
Lab Intro Improve Data Quality
Exploratory Data Anlaysis
Lab Intro Exploratory Data Analysis
Resources
Practice Quiz on Improve Data Quality
Practice Quiz on Exploratory Data Analysis
Practical ML
Introduction
Supervised Learning
Regression and Classification
Short History of ML: Linear Regression
Short History of ML: Perceptron
Short History of ML: Neural Networks
Lab Intro: Introduction to Linear Regression
Lab Intro: Introduction to Logistic Regression
Short History of ML: Decision Trees
Short History of ML: Random Forests
Lab Intro: Decision Trees and Random Forests in Python
Short History of ML: Kernel Methods
Short History of ML: Modern Neural Networks
Resources
Supervised Learning
Regression and Classification
Linear Regression
Perceptron
Neural Networks
Decision Trees
Kernel Methods
History of ML: Modern Neural Networks
Optimization
Introduction
Defining ML Models
Introducing the Course Dataset
Introduction Loss Functions
Gradient Descent
Troubleshooting Loss Curves
ML Model Pitfalls
Lecture Lab: Introducing the TensorFlow Playground
Lecture Lab: TensorFlow Playground - Advanced
Lecture Lab: Practicing with Neural Networks
Loss Curve Troubleshooting
Performance Metrics
Confusion Matrix
Resources
Lesson Quiz
Lesson Quiz
Lesson Quiz
Module Quiz
Generalization and Sampling
Introduction
Generalization and ML Models
When to Stop Model Training
Lecture Creating Repeatable Samples in BigQuery
LectureDemo: Splitting Datasets in BigQuery
Lab Introduction Creating Repeatable Dataset Splits in BigQuery
Lab Solution Walkthrough Creating Repeatable Dataset Splits in BigQuery
Lab Introduction Exploring and Creating ML Datasets
Lab Solution Walkthrough Exploring and Creating ML Datasets
Resources
Generalization and ML Models
Module Quiz
Course Summary
Resources - Readings Compiled as PDF
Quiz Questions as a PDF
Course Slides
Course Quiz
Launching into Machine Learning at Coursera Admission Process
Important Dates
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