Best Machine Learning, Deep Learning, AI & IOS Courses Online


Statistics and Data Science in R

Taught by a Stanford-educated, ex-Googler and an IIT, IIM — educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce.

This course is a gentle yet thorough introduction to Data Science, Statistics and R using real life examples.

Let’s parse that.

  • Gentle, yet thorough: This course does not require a prior quantitative or mathematics background. It starts by introducing basic concepts such as the mean, median etc and eventually covers all aspects of an analytics (or) data science career from analysing and preparing raw data to visualising your findings.
  • Data Science, Statistics and R: This course is an introduction to Data Science and Statistics using the R programming language. It covers both the theoretical aspects of Statistical concepts and the practical implementation using R.
  • Real life examples: Every concept is explained with the help of examples, case studies and source code in R wherever necessary. The examples cover a wide array of topics and range from A/B testing in an Internet company context to the Capital Asset Pricing Model in a quant finance context.
  • What you will learn
  • Harness R and R packages to read, process and visualize data
  • Understand linear regression and use it confidently to build models
  • Understand the intricacies of all the different data structures in R
  • Use Linear regression in R to overcome the difficulties of LINEST() in Excel
  • Draw inferences from data and support them using tests of significance
  • Use descriptive statistics to perform a quick study of some data and present results

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Complete iOS 11 Machine Learning Masterclass

If you want to learn how to start building professional, career-boosting mobile apps and use Machine Learning to take things to the next level, then this course is for you. The Complete iOS Machine Learning Masterclass™ is the only course that you need for machine learning on iOS. Machine Learning is a fast-growing field that is revolutionizing many industries with tech giants like Google and IBM taking the lead. In this course, you’ll use the most cutting-edge iOS Machine Learning technology stacks to add a layer of intelligence and polish to your mobile apps. We’re approaching a new era where only apps and games that are considered “smart” will survive. (Remember how Blockbuster went bankrupt when Netflix became a giant?) Jump the curve and adopt this innovative approach; the Complete iOS Machine Learning Masterclass™ will introduce Machine Learning in a way that’s both fun and engaging.

In this course, you will:

  • Master the 3 fundamental branches of applied Machine Learning: Image & Video Processing, Text Analysis, and Speech & Language Recognition
  • Develop an intuitive sense for using Machine Learning in your iOS apps
  • Create 7 projects from scratch in practical code-along tutorials
  • Find pre-trained ML models and make them ready to use in your iOS apps
  • Create your own custom models
  • Add Image Recognition capability to your apps
  • Integrate Live Video Camera Stream Object Recognition to your apps
  • Add Siri Voice speaking feature to your apps
  • Dive deep into key frameworks such as coreML, Vision, CoreGraphics, and GamePlayKit.
  • Use Python, Keras, Caffee, Tensorflow, sci-kit learn, libsvm, Anaconda, and Spyder–even if you have zero experience
  • Get FREE unlimited hosting for one year
  • And more!

What you will learn

  • Build smart iOS 11 & Swift 4 apps using Machine Learning
  • Use trained ML models in your apps
  • Convert ML models to iOS ready models
  • Create your own ML models
  • Apply Object Prediction on pictures, videos, speech and text
  • Discover when and how to apply a smart sense to your apps

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Introduction to Data Science with Python

This course introduces Python programming as a way to have hands-on experience with Data Science. It starts with a few basic examples in Python before moving onto doing statistical processing. The course then introduces Machine Learning with techniques such as regression, classification, clustering, and density estimation, in order to solve various data problems.

What you will learn

  • Writing simple Python scripts to do basic mathematical and logical operations
  • Loading structured data in a Python environment for processing
  • Creating descriptive statistics and visualizations
  • Finding correlations among numerical variables
  • Using regression analysis to predict the value of a continuous variable
  • Building classification models to organize data into pre-determined classes
  • Organizing given data into meaningful clusters
  • Applying basic machine learning techniques for solving various data problems

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Introduction to Data Science with R

This course introduces R programming environment as a way to have hands-on experience with Data Science. It starts with a few basic examples in R before moving onto doing statistical processing. The course then introduces Machine Learning with techniques such as regression, classification, clustering, and density estimation, in order to solve various data problems.

What you will learn

  • Writing simple R programs to do basic mathematical and logical operations
  • Loading structured data in a R environment for processing
  • Creating descriptive statistics and visualizations
  • Finding correlations among numerical variables
  • Using regression analysis to predict the value of a continuous variable
  • Building classification models to organize data into pre-determined classes
  • Organizing given data into meaningful clusters
  • Applying basic machine learning techniques for solving various data problems

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Machine Learning In The Cloud With Azure Machine Learning

The history of data science, machine learning, and artificial Intelligence is long, but it’s only recently that technology companies — both start-ups and tech giants across the globe have begun to get excited about it… Why? Because now it works. With the arrival of cloud computing and multi-core machines — we have enough compute capacity at our disposal to churn large volumes of data and dig out the hidden patterns contained in these mountains of data.

This technology comes in handy, especially when handling Big Data. Today, companies collect and accumulate data at massive, unmanageable rates for website clicks, credit card transactions, GPS trails, social media interactions, and so on. And it is becoming a challenge to process all the valuable information and use it in a meaningful way. This is where machine learning algorithms come into the picture. These algorithms use all the collected “past” data to learn patterns and predict results or insights that help us make better decisions backed by actual analysis.

You may have experienced various examples of Machine Learning in your daily life (in some cases without even realizing it). Take for example

Credit scoring, which helps the banks to decide whether to grant the loans to a particular customer or not — based on their credit history, historical loan applications, customers’ data and so on

Or the latest technological revolution from right from science fiction movies — the self-driving cars, which use Computer vision, image processing, and machine learning algorithms to learn from actual drivers’ behavior.

What you will learn

  • Learn about Azure Machine Learning
  • Learn about various machine learning algorithms supported by Azure Machine Learning
  • Learn how to build and run a machine learning experiment with real world datasets
  • Learn how to use classification machine learning algorithms
  • Learn how to use regression machine learning algorithms
  • Learn how to expose the Azure ML machine learning experiment as a web service or API
  • Learn how to integrate the Azure ML machine learning experiment API with a web application

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Source: Deep Learning on Medium