Data Science, E-learning, Education, Online Courses, Skills 0 comments on Best online machine learning courses

Best online machine learning courses

With the advent of Artificial Intelligence (AI), machine learning and deep learning- two of its subsets- started gaining wide applications. Institutions offer online courses in machine learning and deep learning. Although some of these courses are free to audit, others require a fee. Even the free courses require a fee for the certificate of completion.

Some of the best online machine learning courses have been listed below.

  • Machine Learning Course by Andrew Ng

This course covers machine learning basics and advanced topics as well. Google Brain’s founder Andrew Ng, teaches this course. You can audit this course for free, but a fee is required to obtain the certification of completion. Stanford University provides accreditation for this course. This is one of the best machine learning courses available.

  • Machine Learning Course by Columbia University

If you are looking for a machine learning course that provides Ivy League-level education, look up this one. This free online course spans for 12 weeks. At the end of this, you can opt for a certificate of completion. Columbia University provides certification for this course.

  • Machine Learning Course by Georgia Tech

This course is not for amateurs! If you possess some prior knowledge about machine learning, this course is the best option for you to go a step further. Like the other courses in this list, this machine learning course provided by Udacity is also free and makes use of several open-source software.

  • Online Machine Learning Course by

A plethora of courses on machine learning and AI are offered by, which range from beginner to expert level. These free online courses are self-paced and cover a wide range of topics. 

  • Courses on Machine Learning by Kirill Eremenko, Hadelin de Ponteves, and the SuperDataScience Team

Data Science and Machine Learning Bootcamp with R is one of the several great courses on machine learning available online. 

  • Online Machine Learning Course by California Institute of Technology

This free, online course spans ten weeks and is self-paced. If you are looking for courses offered by reputed institutes, this course should be one of your choices as the California Institute of Technology provides it. This is one of the best machine learning course available.

The list of online machine learning courses is practically endless. This list features some of the best opportunities that are available online and can be your next stepping stone in the direction of machine learning. Check out these and more options at Get Me A Course.

All, Big Data, Data Science 0 comments on Top 10 courses for data analytics online

Top 10 courses for data analytics online

With the ever-advancing world, data analytics has become an essential part of career development. With the surge in popularity of data science, a multitude of online courses have come up to help anyone with a chance to enhance their data analytics skills.

To help you choose the best, the top 10 data science online courses are listed below: 

  • Data analysis and presentation skill course, the PwC specialization: This program is a perfect suit for people without a programming background. It consists of 5 courses such as problem-solving in Excel, project management, advanced Excel working, and business presentation in PowerPoint, along with data-driven decision-making skills.
  • Data Science Specialization: Consisting of 10 courses, including concepts of algorithms, statistics, and data science, it takes 43 weeks for completion. It also pays attention to R programming. This course is recommended for people with a programming background.
  • Big Data Specialization: 30 weeks are required to cover all the six courses under this topic. It focuses on the main aspects of big data, ranging from a basic introduction to processing and visual analytics.
  • Statistics with R: With five courses in total, the aim is to visualize and analyze data in R. This helps in creating reproducible data analytics reports, and understand the statistical inference with specifics of Bayesian statistical inference.
  • EDX: Microsoft Professional Program in Data Science has nine courses in total, covering up the basics and advanced programming of data science. It involves the programming languages, Python and R, as well. It takes 56-58 weeks for completion.
  • Marketing Analytics: This program is classified into four courses: Marketing Measurement Strategy, Price and Promotion Analytics, Competitive Analytics & Marketing Segmentation, and Products, Distribution, and Sales. It takes at least 16 weeks to complete this course.
  • Big Data Fundamentals: The shortest data science online course of just 14 hours, this one comprises of 3 courses that give you a brief introduction to Big data.
  • Advanced-Data Structures: As the name indicates, this is a broader spectrum comprising of all different kinds of data structures. It covers all the types, starting from statistics to maps, geometric data structures, etc. It also focuses on the significant aspects of research in the data structure field.
  • Python: The course concentrates on the basics of the Python programming language. Knowledge of this language is crucial for data analytics.
  • Java-tutorial for complete beginners: Suitable for beginners with no prior knowledge or experience in programming. It provides the necessary training to learn basic Java.

Now that you know what specifics to look for, head for the listings at Get Me A Course to find a course that suits your needs.

Data Science, User Experience (UX) 0 comments on Text Vs Graphics

Text Vs Graphics

We live in a visual world. Images are better captured and processed than text. Visual data is processed 60,000 times faster than text. Information conveyed better by pictures and infographics. It’s easier to remember too. Learners respond to visual information faster than text.

Given an abundance of online courses,  it’s crucial for course designers to get their strategy right so that they can claim a slice of the elearning pie. Reaching the right audience with the right information in the right way is the strategy to adopt to stand out in a crowd. 

Some interesting figures I found floating around the internet: 

  • Ninety percent of the information that a human brain receives is visual information
  • More than sixty percent of people are visual learners.
  • Social media users share more of visual content than the other types 
  • Visual content has a better chance of securing a video rank on the first page of Google than the other types of content

Visual content scores over text in several ways. Some of those are: 

  • Visuals help learners grasp information more quickly.
  • Visuals ensure that information stays in a learner’s memory for a long time. Visuals are easier to recall too. 
  • Visuals simulate imaginations and motivate learners.
  • Visuals simplifys topics with heavy text. 
  • Visuals are shared more often than text.

However, text scores over visuals in some ways:  

  • Text loads faster compared to visuals. 
  • Text is accessible on most devices where visuals fail to load on older devices/browsers and the like. 
  • Search engine optimization uses keywords to determine results.

Text and visuals are inseparable for a course. So use them well in winning combinations. Use good images to break long paragraphs of text. Balance space usage in such a way that learners stay focused but do not get distracted. 

Data Science 0 comments on How to get started with Deep Learning

How to get started with Deep Learning

Deep Learning is the hottest thing in machine learning right now. But what’s behind all the buzz? Well to begin with, deep learning is well… deep!

Pic credit :

Deep learning makes use of multiple layers of nodes or neurons to extract complex relations between data.  Each layer processes the data and passes it onto the next all the way to the output layer. The reason deep learning is such a popular model is because of the versatility of layers, which allows them to be used across a wide range in anything from classifying dog photos, to sequence analysis or extracting user sentiment from text.  

So let’s jump right in ! I’m assuming you already have python installed. If not go right ahead and download python from the official website. 

  1. Go to Command Prompt (Terminal in Mac) 

We’ll be looking at Keras, one of the most popular deep learning frameworks that is also very user friendly. So first we’ll need to install Keras and a few backend libraries that support it. Type in-

2. Open Jupyter notebook

If you notice we installed something called Jupyter notebook. This is a very popular integrated development environment (IDE) for python that is favoured by the machine learning community due to its clean look, easy customizability and ease with which graphs and plots can be displayed. So go ahead and type into the terminal –

3. Create your model

You should see the jupyter notebook tab open up in your browser. Go ahead and create a new file. You’re all set to create your first model. First, let’s import Keras –  

Let’s go ahead and define our model – 

That’s it! You’ve built your first deep learning model.  Don’t worry if you don’t understand all the code you’re writing yet. The drawback of deep learning is that due to the complexity of the model it has a lot of parameters to customize. However, the default parameters work well enough for simple cases. 

Check out the Keras tutorial if you want a deeper look at Keras and want to start feeding data into your model. 

Big Data, Data Science 0 comments on How to start your Data Analytics education

How to start your Data Analytics education

Jumping into Data Analytics can seem as though you’re walking into a whirlpool. There are so many options, websites and courses that it can be tough to figure out which direction to go in. However, as long as you approach it smartly there’s no reason to get overwhelmed. In today’s open digital economy you can get a top science education for free or for almost free. How do you do that? 

First things first – 

  • Don’t try to string it out on your own

It might be tempting to just learn as you go along especially if you come from a mathematics or programming background. However, regardless of your background, it’s better to build your skills through a structured course. The danger of a do-it-yourself approach lies not in the chance of failure but rather in appearing to succeed. Unless you’re careful it’s easy to develop relations or derive insights from data where none exists. This could easily lead to catastrophic problems, especially if you plan to take business decisions on these data insights. Don’t try to be a cowboy. Go learn from the pros.

  • Consider your goals

Ok, you want to learn Data Analytics, but to what end? Are you looking for a career change? Do you want to polish your resume? Do you want to utilize the data that your business generates, in a better way?  

These are important questions to consider. Depending on what your answer is, your learning path will differ vastly. For those considering a career change, a micro masters package might be ideal, while a simple 3-week course could do for a business owner looking to get savvy. 

Once you’ve done that you can browse through the thousands of courses available on Coursera, Udemy and  Edx. If you feel overwhelmed by the number of options, you might just want to use something like Get Me A Course to find the most appropriate course for you. 

  • Avoid buying stuff

I cannot emphasize this enough. There are loads of free resources available from both the top companies and colleges across the world. Stanford, Google and IBM are just some of the top organizations in the world offering free data analytics courses. Don’t pay astronomical prices when you can get great content for free.

However, this doesn’t mean don’t buy material. If you’re sure about the path you’re pursuing and are confident you’re getting the best results for your money, then it might make sense to go for paid courses. Make sure you’re fully aware of certification type and criteria before you start. 

  • Slog it out no matter what

An education half-done is worse than no education at all, especially when it comes to data analytics. No matter how tough it gets, slog it out till the end. There are terabytes of data waiting for you at the other end!

Artificial Intelligence, Big Data, Data Science, E-learning, Education, Machine Learning 0 comments on 13 Top Tech Skills In High Demand For 2018

13 Top Tech Skills In High Demand For 2018


The number of tech employment opportunities is expected to increase by 12% by 2024, which will lead to more and more jobs becoming available to IT professionals looking to get into the space, according to a report by Modis. With the number of tech positions in web development, biomedical engineering, cybersecurity and analysis expected to grow exponentially within the next year, one may find the competition to acquire a skilled job candidate harder than they think. Continue Reading “13 Top Tech Skills In High Demand For 2018”

Big Data, Data Science, E-learning, Online Courses, Science 0 comments on Deep Learning helps you process images like a Pro

Deep Learning helps you process images like a Pro

Image processing is one of the most exciting applications of Artificial Intelligence and Deep Learning. Through it, you can train a computer to see and interpret images similar to the way humans perceive images.

In this article, you’ll learn how to use a deep learning model to transfer painting styles with TensorFlow, a machine learning software library first developed by Google. This is a project straight from our Deep Learning Nanodegree program. Continue Reading “Deep Learning helps you process images like a Pro”

Data Science, Education 0 comments on Data Science: Lucrative New Age Career

Data Science: Lucrative New Age Career

Charlotte Stevens hails from the Rhondda Valley in Wales but has found a home in London with her sister and two flatmates. Growing up in financially straitened circumstances meant that she always had a desire for financial security at the back of her mind. A university education left her with little clue about applying herself. Surely not a profile for a great career!

But then Charlotte found she had an aptitude for Data Science – the journey started by signing up on to the Johns Hopkins specialisation track in Data Science. The courses and a change in job ensured that she found the financial security she yearned for – her income rose to three times the average of others her age. 

Charlotte credits the e-learning she acquired on data science via Coursera for changing her life. She is one among millions around the world transforming their lives by upskilling and reskilling themselves for the new age economies blooming worldwide. These economies are built on cutting edge technologies such as data science, machine learning, IoT, artificial intelligence and the like.

A search for “Data Science Johns Hopkins” throws up 57 courses on Coursera – an incredible range of courses, tracks and certifications from one of the premier institutes for learning in the world. The data science track Charlotte took consists of the concepts and tools students will need throughout the entire data science pipeline. These include asking the right kinds of questions, making inferences and publishing results. Learners also learn to apply  the skills they have acquired by building a data product using real-world data. The bonus? Upon completion of the course, students will have a demonstrable portfolio of code and assets that they can use to showcase their mastery of the subject.

According to, Data Science is “…a multidisciplinary blend of data inference, algorithm development, and technology in order to solve analytically complex problems.” It is a science that has evolved to help modern-day societies answer the question “We have lots of data – now what?” Technological advances such as IoT (Internet of Things), hardware advances that allow for seamless storage of petabytes of data, business advances such as massive data farms by organisations such as Amazon Web Services and others all have ensured that companies and governments around the world are sitting on massive amounts of data. Data Analysts like Charlotte work to unlock real value from the data, performing functions such as cluster analysis, segmentation, etc to derive insights from the data. These insights are used by analysts to make recommendations not just to improve processes, products and services but also to improve the design of new products with advanced capabilities.  

Data science is one of those ‘hot’ areas where the demand for talent far outstrips the supply. The financial remuneration for data scientists is huge, prompting many professionals to upskill and move into the field.

Such data are testament to the fact that Charlotte made the right choice in opting for Data Science!