Python For Data Science and Machine Learning Bootcamp

20+ Best Resources To Learn and Start Your Career In Artificial Intelligence (AI) and Machine Learning (ML)

Develop a passion for learning. If you do, you will never cease to grow.
– Anthony J. D’Angelo

What is Machine Learning and Why Now?

The era of digital revolutions using machine learning (ML), artificial intelligence (AI) and intelligent systems has arrived. Remember, the sci-fi novels and movies that we saw a decade ago seem to be somehow realistic now. The term “Machine Learning” was firstly coined by an American pioneer “Arthur Samuel” in 1959 while working at IBM. Machine learning is so inescapable today that you likely utilize it many times each day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. Moreover, according to Gartner’s Top 10 Strategic Technology Trends for 2018 – Report, it predicts that the joined AI and propelled ML will dominate Artificial Intelligence application development in 2018.

“Machine learning is a branch of data analytics where the machine based on the input Models (Experience) predicts certain behaviours and also learn to adapt without much programming intervention.”

Raw data and data models are the two main drivers of Machine Learning solutions. In Machine Learning, the algorithms have the capability to study and learn from past data, and then simulate the human decision-making process by using predictive analysis and decision trees. It enables data-driven decision systems to continuously learn from new data and adapt itself to deliver “reliable and repeatable” results.

Gartner Emerging Technology Hype Cycle - 2017
Gartner Emerging Technology Hype Cycle – 2017

By seeing the rapid change in technological advancements and industry readiness, I personally feel that now is the high time for AI and ML skilled professionals to come up and grab the opportunity to learn and create some impact before others. It will definitely give them a head start from others and on the other side, it will raise their range of abilities to next level.

Building Foundations Before Specialization in ML or AI

You can’t go profoundly into each machine learning topic. The field is vastly spread and there’s a lot to learn, moreover the field is progressing quickly. I will suggest you to ace foundational ideas first and after that emphasis on doing projects in a particular space of interest — whether it’s natural language understanding, computer vision, deep reinforcement learning, robotics, or whatever else.

Being motivated is much more important for some long term goal plan. In case you’re having a great time, you’ll gain quick ground. In case you’re endeavoring to compel yourself forward, you’ll back off.

Baby Steps Towards Python and Machine Learning

Python or R Programming?

Multiple programming languages are available as the option to use while learning and implementing machine learning concepts. I know many of you might be confused between Python and R, that which one is the right, to begin with. Well, my personal suggestion would be to go with  Python  because it is comparatively faster than R, more easy to learn and it has huge StackOverflow community support. Moreover, you are free to choose any of the programming languages with which you are comfortable with but for detailed comparison, you can have a look here.

Pic Credits: KD Nuggets | Python or R Programming Graph
Pic Credits: KD Nuggets | Python or R Programming Graph
Do Online Practice: Codechef | CodingGround | HackerRank.

Mathematical fundas to look after?

A quick recap of few of the basic mathematical fundas will really boost your learning rate. I would recommend you to quickly brush up below-mentioned topics to accelerate your learning curve.

  • Linear Algebra
  • Probability and Statistics
  • Calculus

Linux, Windows or MAC?

Most research work is done on Unix (Linux) boxes. So personally, I chose Ubuntu 16.04 as lots of the tutorials are fairly available for Ubuntu, and I’m comfortable with Linux. Windows also seem to have decent support but sometimes you need to go few extra miles to perform few operations, which you can do easily with few commands in Ubuntu terminal. The big thing to keep an eye out for is CUDA support. Luckily, most of the operating systems (Windows, Linux and Mac) are supported. So don’t go choosing some fancy OS nobody’s ever heard of!

 Conclusion Pick a Linux distro, preferably Ubuntu unless you’re familiar with Linux and comfortable with the command line, or pick Windows and go find yourself some other tutorial.

Best Available Resources For Machine Learning | AI

Books To Read –

  1. Hands-On Machine Learning with Scikit-Learn & TensorFlow
  2. Python Machine Learning by Example
  3. Introduction to Machine Learning with Python
  4. Head First Python
  5. Beginning Programming with Python For Dummies

Popular Blogs –

  1. PyImageSearch Blog – By Adrian Rosebrock (One of the blogs that I personally follow)
  2. Machine Learning Mastery – Blog
  3. Google Research Blog Updates on Machine Learning and AI
  4. Reddit – Machine Learning
  5. KD Nuggets Blog
  6. AI Trends
  7. The Artificial-Intelligence Blog
  8. Open AI – Website
  9. Blogs on Medium – Machine Learning For Humans
  10. Medium – Adam Geitgey (Amazing Content especially on Face Recognition)

Online Courses –

Machine Learning A-Z Hands on Python and R in Data Science
Python For Data Science and Machine Learning Bootcamp
Python For Data Science and Machine Learning Bootcamp
Machine Learning By Andrew Ng - Coursera
Machine Learning By Andrew Ng – Coursera
Machine Learning Crash Course By Google
Machine Learning Crash Course By Google

Popular Youtube Video Channels –

  • Sentdex Youtube Channel

  • Siraj Raval Youtube Channel

  • Google Developers Youtube Channel

  • Data School Youtube Channel

Token of Thanks!

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