Best Machine Learning and Data Science Courses for 2018
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.
- Data Analysis with R: Datatypes and Data structures in R, Vectors, Arrays, Matrices, Lists, Data Frames, Reading data from files, Aggregating, Sorting & Merging Data Frames
- Linear Regression: Regression, Simple Linear Regression in Excel, Simple Linear Regression in R, Multiple Linear Regression in R, Categorical variables in regression, Robust regression, Parsing regression diagnostic plots
- Data Visualization in R: Line plot, Scatter plot, Bar plot, Histogram, Scatterplot matrix, Heat map, Packages for Data Visualisation : Rcolorbrewer, ggplot2
- Descriptive Statistics: Mean, Median, Mode, IQR, Standard Deviation, Frequency Distributions, Histograms, Boxplots
- Inferential Statistics: Random Variables, Probability Distributions, Uniform Distribution, Normal Distribution, Sampling, Sampling Distribution, Hypothesis testing, Test statistic, Test of significance
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!
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.
Basic knowledge
- This course is for beginners, but it helps to have some basic understanding of statistics (mean, median, scatter plot) and preliminary knowledge of any programming. The course also assumes that you know how to download and install various programs/apps, and you are able to edit and debug simple programs
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
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.
Basic knowledge
- This course is for beginners, but it helps to have some basic understanding of statistics (mean, median, scatter plot) and preliminary knowledge of any programming. The course also assumes that you know how to download and install various programs/apps, and you are able to edit and debug simple programs
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
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.
Or Amazon’s recommendation engine which recommends products based on buying patterns of millions of consumers.
In all these examples, machine learning is used to build models from historical data, to forecast the future events with an acceptable level of reliability. This concept is known as Predictive analytics. To get more accuracy in the analysis, we can also combine machine learning with other techniques such as data mining or statistical modeling.
This progress in the field of machine learning is great news for the tech industry and humanity in general.
But the downside is that there aren’t enough data scientists or machine learning engineers who understand these complex topics.
Well, what if there was an easy to use a web service in the cloud – which could do most of the heavy lifting for us? What if scaled dynamically based on our data volume and velocity?
The answer – is new cloud service from Microsoft called Azure Machine Learning. Azure Machine Learning is a cloud-based data science and machine learning service which is easy to use and is robust and scalable like other Azure cloud services. It provides visual and collaborative tools to create a predictive model which will be ready-to-consume on web services without worrying about the hardware or the VMs which perform the calculations.
The advantage of Azure ML is that it provides a UI-based interface and pre-defined algorithms that can be used to create a training model. And it also supports various programming and scripting languages like R and Python.
In this course, we will discuss Azure Machine Learning in detail. You will learn what features it provides and how it is used. We will explore how to process some real-world datasets and find some patterns in that dataset.
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