How to learn Artificial Intelligence?

February 13, 2019
Artificial Intelligence

 

The final goal of AI/ Artificial intelligence is to create a robot or computer system that works as a human does and has the same or more intelligence in handling data rapidly. Obviously, the term is simple enough to read, in that it says the intelligence is artificially induced. From calculations, data-handling, drawing inferences, automated processes, self-driven automobiles to path-breaking machines in healthcare, customer service, and more AI is scoring over human abilities. However, they are meant to aid humans and not replace them. Have you seen the movies based on AI like Lucy, Terminator, and Chappie?

AI goals:

AI mimics the human brain in its concepts, building methodology and functioning logic. What it does include is the ability for NLP, deduction, planning, reasoning logic, inferential techniques, learning from data, and the ability to move or control objects. To this exhaustive list try adding creativity, sociability and intelligence or wisdom and you have the direction that AI is bound to grow into.

AI varieties:

Artificial intelligence itself is a very broad definition that can be subdivided as follows
Reactive applications: Such basic applications do not really count as having memory or self-learning capacities. Ex: Calculators.
Strong application: Such applications are the ‘smart’ variety and very close to human intelligence levels. They are multi-tasking and can network efficiently. A lot of development has to still come but the Robot is a good example. They understand emotions, can interactively respond to queries and can self-learn from data.
Weak application: Such applications are the single-tasked, lack self-awareness and have limited intelligence. Try having a conversation with the Google assistant to see its weak application limitations. Please note that even such applications have several embedded AI programs to help it achieve its single task of being an assistant. These applications may develop memories for a limited time-period and act driven from the memory. For example traffic signals, chatbots, automatic self-steering vehicles and industrial lifting arms.
Super application: These applications combine human-like intelligence with superb ability to calculate, strategize, plan and execute decisions and tasks. Perhaps they are a tad better than the human’s limited ability to process very large volumes of data, recognize patterns over large databases and use the information for self-learning. It does have a fine line to its limitations and learned people have expressed concerns of being completely taken over by machines. Many movies have also raised the issue of rogue machines running amok!

Here’s An Easy Learn Artificial Intelligence Tutorial to Follow

  • Start with learning programming languages like Python, R etc.
  • For this, you will need to refresh your fundamentals in algebra, mathematics, calculus, statistics and probability theory.
  • Then do formal training in getting up to speed with your programming and application skills.
  • Start applying your knowledge to practical applications and build your bot in Python.
  • Between Python and R the syntax in Python is more user-friendly and it has great libraries and supported applications that need very little coding.

 

Here is a step-by-step approach to learning Python for newbies complete with tools as they do in an Artificial Intelligence Course.

Step 1: Read, learn and understand why you are using Python
Learn artificial intelligence and zero in on your reasons for learning to use Python, its features and why it scores in AI and data science applications.

Step 2: Machine set-up procedures:
Download Anaconda from the net/ Continuum.io. Also, refer to complete instructions for the OS by just clicking on the link.

Step 3: Python language fundamentals learning:
It is always a wise option to gain experience from formal learning at a reputed institute like Imarticus Learning for doing a course. Their curriculum is excellent and includes hands-on practice, mentoring and enhancing practical skills in Python.

Step 4: Use Python in interactive coding and Regular Expressions:
When using data from various sources the data will need cleaning before the analytics stage.

Step 5: Gain proficiency in Python libraries like Matplotlib, NumPy, Pandas and SciPy. Try out these following tasks and resources like NumPy arrays, tutorials for NumPy, Matplotlib, SciPy, the notebook ipython, Data munging, Pandas, and exploratory data-analysis. Practice these frequently used libraries which is essential and very important in AI.

Step 6: Use Python for building your first bot

Step 7: Imbibe ML and Scikit-learn:
These are very important data analysis steps.

Step 8: Use Python and keep practicing:
Try hackathons like Kaggle, DataHack and many others.

Step 9: Neural networks and Deep Learning
Try out short courses on the above topics to enhance your skills.

Interesting daily applications:

Very widely popular are the smart devices from phones, TVs, burglar alarms, security systems etc. Assistants like Google, Alexa, Cortana, Siri are being used by millions globally on Windows, Android devices and the i-OS. We also have self-driven cars, motion detectors, talk-back and voice recognition features and more incorporated into household appliances.
Organizations use Artificial intelligence technology for a variety of applications like CAD, CAM, robotic steering, warehousing, logistics, planning, monitoring, risk management and many, many, more applications. The health sector has seen an overhaul with MRI, CAT scans, Lasers, VR equipment for operations, and such. Fintech startups have even cracked the stock-price predictions and are set to revolutionize financial transactions with blockchains. Gaming, VR, AR, Visualization and nearly every vertical has benefited from AI applications.

Concluding notes:

Artificial intelligence has permeated nearly all fields of life and the human brain remembers innovations only when new. This permeation of all markets and the never-ending upgrades to technologies, software and programming languages implies ample scope for those who choose to make a career in AI. Yes, jobs are aplenty and would need you to match your technical skills to the role. Learn artificial intelligence by doing a practically-oriented course at Imarticus, which can help you get the formal education required, the practical experience in building bots, and of course certifications which are an endorsement of your job-readiness. The salaries are generous and get better with experience. AI can also be clubbed with other skills like ML, AR, VR etc which can offer career choices of your interest and lateral options. Be the early bird and begin your career journey in AI today.

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