Is it natural to do Natural Language processing
Natural Language processing is one of the key area of Artificial Intelligence and is quite a hype especially after recent advancement in technology but are we really succeeding in it. Are we making right process around it? Is it getting really implemented with green range of accuracy?What do you think, can anyone share any success stories around it
NLP is worth pursuing because as you said it is very interesting field in CS. There are variety of future prospects in this field. My suggestion will be while learning and working on some NLP projects, advance yourself into learning about machine learning in more detail.
Human Factor Research and Machine Learning
OEMs spend too much conducting user trials of their products. AI in human factor research can help in reduce the spend. A ML model for different personas can to be created by learning from questionnaire. The design imperatives and the HMI attributes based on Norman's Visceral, Behavioral and Reflective aspects can be quantified. The model will rate the product based on the HMI attributes.
As machine learning approaches ubiquity in industrial systems and consumer products, human factors research must attend to machine learning, specifically on how intelligent systems built on machine learning are different from early generations of automated systems, and what these differences mean for human-system interaction, design, evaluation and training.
Herding sheep, Social Media and AI
The herding anaimals tend to move in a one particular direction. There are sheepdogs who are in charge of herding the sheeps. The dogs analyze the state of a given herd and determine the best action to keep the sheep together while herding. These sheepdog think algorithmically. Social media discussion could be considered as herding sheep. Each of the smaller groups have a direction, right, left, centre etc. The dog algorithm could be employed to influence the group behavior. Machine Learning could be used to identify the groups and its direction and analyse the influence of sheepdog algorithm.
Medical Diagnostics and Machine Learning
Recently I approached my Wellness center, where I have have been doing my annual health checkup, for my historical ECG data of last 10 years. To my disappointment no ECG data was stored. The data could have helped me to understand the aging and heart related anomalies. A ML model could be created for the healthy heart and detect any deviation from the normal. This is true for not just heart, we could created a ML model for any bunch of vital parameters and detect anomalies which could accelerate medical diagnosis. Data is pure gold.
Machine learning technology is currently well suited for analyzing medical data, and in particular there is a lot of work done in medical diagnosis in small specialized diagnostic problems. Data about correct diagnoses are often available in the form of medical records in specialized hospitals or their departments.
ABC’S OF SYSTEMIC APPROACH OF MACHINE LEARNING
According to the father of Artificial Intelligence (AI), John McCarthy, AI is “The science and engineering of making intelligent machines, especially intelligent computer programs”. An AI framework is made out of an agent and its environment. The agents act in their environment. The environment may contain different agents. Machine Learning is a sub-set of artificial intelligence.
Thank you all for your great support and encouragement through your comments and likes. Thank you Santhosh for your real time examples. I will consider it.
Bias in Machine Learning
Bias and variance are two major issues we try to manage in machine learning. We are always looking for a trade-off between the two. Bias has to do with the training data while variance has to do with the model output.
Bias in Machine Learning is defined as the phenomena of observing results that are systematically prejudiced due to faulty assumptions. ... This also results in bias which arises from the choice of training and test data and their representation of the true population.
Machine Learning with Python by Ajit Singh
Machine learning can be described as a form of statistical analysis, often even utilizing well-known and familiar techniques, that has bit of a different focus than traditional analytical practice in applied disciplines. The key notion is that flexible, automatic approaches are used to detect patterns within the data, with a primary focus on making predictions on future data.
Python is a perfect choice for beginner to make your focus on in order to jump into the field of machine learning and data science.
What you are too afraid to ask about Artificial Intelligence Part II
This article follows the first piece on machine learning describing how AI interacts with neuroscience, as well as how hardware and chips are getting created and modified to be more efficient for specific AI algorithm
Explanation about Neuroscience and AI and Hardware and AI are very nice. Thank you Francesco Corea for your wonderful article.
Students launch Machine Learning Society at Imperial
Undergraduates Harry Berg (Mechanical Engineering) and Haron Shams (Design Engineering) have set up the Imperial College Machine Learning Society to get students involved in and inspired by technology that’s going to change the world.It is interesting that this society was planned by two undergraduate students and that the first event attracted 250 attendees, with over half of them being PhD students.
It is very nice initative by two undergraduate students Harry Berg (Mechanical Engineering) and Haron Shams (Design Engineering) .
Your First Machine Learning Project in Python Step-By-Step
Do you want to do machine learning using Python, but you’re having trouble getting started?In this post, you will complete your first machine learning project using Python.In this step-by-step tutorial you will:Download and install Python SciPy and get the most useful package for machine learning in Python.Load a dataset and understand it’s structure using statistical summaries and data visualization.Create 6 machine learning models, pick the best and build confidence that the accuracy is reliable.If you are a machine learning beginner and looking to finally get started using Python, this tutorial was designed for you.https://machinelearningmastery.com/machine-learning-in-python-step-by-step/
Using A Structured Step-By-Step Process Any predictive modeling machine learning project can be broken down into 4 stages: 1.) Collect Data 2.) Pick the Model 3.) Train the Model 4.) Test the Model