Online Machine Learning Courses with Verified Certificates

Machine Learning Javascript Algorithms TensorFlow Applications Python Programming R Data Analysis SQL Excel Data Science Neural Networks Courses Certificates

Online Machine Learning Courses with Verified Certificates

By ridhigrg |Email | Nov 20, 2019 | 5430 Views

Machine Learning with Javascript

Created by Stephen Grider

Master Machine Learning from scratch using Javascript and TensorflowJS with hands-on projects.

What you'll learn

Assemble machine learning algorithms from scratch!
Build interesting applications using Javascript and ML techniques
Understand how ML works without relying on mysterious libraries
Optimize your algorithms with advanced performance and memory usage profiling
Use the low-level features of Tensorflow JS to supercharge your algorithms
Grow a strong intuition of ML best practices

The Complete Machine Learning Course with Python

Created by Codestars by Rob Percival

Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning & More!

What you'll learn

Machine Learning Engineers earn on average $166,000 - become an ideal candidate with this course!
Solve any problem in your business, job or personal life with powerful Machine Learning models
Train machine learning algorithms to predict house prices, identify handwriting, detect cancer cells & more
Go from zero to hero in Python, Seaborn, Matplotlib, Scikit-Learn, SVM, unsupervised Machine Learning, etc

Data Science and Machine Learning Bootcamp with R

Created by Jose Portilla

Learn how to use the R programming language for data science and machine learning and data visualization!

What you'll learn

Program in R
Use R for Data Analysis
Create Data Visualizations
Use R to handle CSV, excel, SQL files or web scraping
Use R to manipulate data easily
Use R for Machine Learning Algorithms
Use R for Data Science

Machine Learning, Data Science and Deep Learning with Python

Created by Sundog Education by Frank Kane

Complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks

What you'll learn

Build artificial neural networks with Tensorflow and Keras
Classify images, data, and sentiments using deep learning
Make predictions using linear regression, polynomial regression, and multivariate regression
Data Visualization with MatPlotLib and Seaborn
Implement machine learning at massive scale with Apache Spark's MLLib
Understand reinforcement learning - and how to build a Pac-Man bot
Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA
Use train/test and K-Fold cross-validation to choose and tune your models
Build a movie recommender system using item-based and user-based collaborative filtering
Clean your input data to remove outliers
Design and evaluate A/B tests using T-Tests and P-Values

Machine Learning

Offered By Stanford University

About this Course

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI.

This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

Introduction to Machine Learning

Learn to train and assess models performing common machine learning tasks such as classification and clustering.

Course Description

This online machine learning course is perfect for those who have a solid basis in R and statistics but are complete beginners with machine learning. After a broad overview of the discipline's most common techniques and applications, you'll gain more insight into the assessment and training of different machine learning models. The rest of the course is dedicated to a first reconnaissance with three of the most basic machine learning tasks: classification, regression, and clustering.

Source: HOB

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