Machine Learning : What is it really?

Machine Learning: What is it really?

Well, Machine Learning is a subfield of Artificial Intelligence that evolved from Pattern Recognition and Computational Learning theory. Arthur Lee Samuel defines Machine Learning as a Field of study that gives computers the ability to learn without being explicitly programmed.

So, basically, the field of Computer Science and Artificial intelligence that “learns” from data without human intervention.

But this view has a flaw. As a result of this perception, whenever the word Machine Learning is thrown around, people usually think of “A.I.” and “Neural Networks that can mimic Human brains ( as of now, that is not possible)”, Self Driving Cars and what not. But Machine Learning is far beyond that. Below we uncover some expected and some generally not expected facets of Modern Computing where Machine Learning is in action.

Machine Learning: The Expected

We’ll start with some places where you might expect Machine Learning to play a part.

Speech Recognition (Natural Language Processing in more technical terms): You talk to Cortana on Windows Devices. But how does it understand what you say? Along comes the field of Natural Language Processing, or N.L.P. It deals with the study of interactions between Machines and Humans, via Linguistics. Guess what is at the heart of NLP: Machine Learning Algorithms and Systems ( Hidden Markov Models being one).

Computer Vision: Computer Vision is a subfield of AI which deals with a Machine’s (probable) interpretation of the Real World. In other words, all Facial Recognition, Pattern Recognition, Character Recognition Techniques belong to Computer Vision. And Machine Learning once again, with its wide range of Algorithms, is at the heart of Computer Vision.

Google’s Self Driving Car: Well. You can imagine what drives it actually. More Machine Learning goodness.

But these were expected applications. Even a naysayer would have a good insight about these feats of technology being brought to life by some “mystical (and extremely hard) mind crunching Computer wizardry”.

Comments

Top 5 pattern-in-python

Pattern #1: 1 1 2 1 1 2 3 2 1 1 2 3 4 3 2 1 1 2 3 4 5 4 3 2 1 code:- n = 5 for i in range(1,n+1): for j in range(1,–i): print(j,end=” “) for k in range(–i,0,-1): print(k,end=” “) print(“\n”) Pattern #2: 5 4 3 2 1 1 2 3 4 5 5 4 3 2 2 3 4 5 5 4 3 3 4 5 5 4 4 5 5 5 code:- n = 6 for i in range(0,n): for j in range(n-1,i,-1): print(j,end=” “) for k in range(i+1,n): print(k,end=” “) print(“\n”) Pattern #3: 5 5 5 5 5 4 4 4 4 3 3 3 2 2 1 code:- n = 5 for i in range(n,0,-1): for j in range(i): print(i,end=” “) print(“\n”) Pattern #4: 1 2 3 4 5 2 2 3 4 5 3 3 3 4 5 4 4 4 4 5 5 5 5 5 5 code:- n = 5 for i in range(1,n+1): for j in range(1,n+1): if j <= i: print(i, end=’ ‘) else: print(j, end=’ ‘) print() Pattern #5: 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 code:- n = 5 for i in range(n,0,-1): for j in range(i): print(n,end=” “) print(“\n”) for i in range(1,n+1): for j in range(i): print(n,end=” “) print(“\n”)

Best digital marketing in Perth

Best digital marketing in Perth Introduction Your introduction into the brave new world of the digital space will be custom-tailored to your business needs requirements. You will be introduced to the crew who will be handling your project, from inception to the launch into the market. Assess It will be our job to not only know your customers but how they engage with the core products and brand relationships . From here we break down what we research, to identify the core elements needed to engage the customer. Create It’s imperative that the design of your vessel is done right from the start. Its shape, level of focus, and attention to detail are crucial for a prosperous, lucrative, and extended journey. We will always present concepts and suggestions as per the requirement, but we truly believe this process should be a collaborative one between the creative crew of the PWD and the client. The final form will dictate its progression into the development and manufacturi...

Multiple Choice Questions (MCQ) on Job Design

1-Work Design is concerned with the _____ of work system in any type of organisation or institution. (A) Study (B) Design (C) Study and design (D) Management 2-High levels of productivity result in (A) Good quality (B) Lower costs (C) Higher purchasing power (D) All of the above 3-The following is (are) the objective(s) for studying work system. (A) Increasing productivity (B) Developing manpower effectiveness (C) Both (A) and (B) (D) None of the above 4-Job design is the consciously planned structuring of ____ performed by an individual or a team of persons. (A) Work effort (B) Duties (C) Responsibilities (D) All of the above 5-The following consideration(s) is (are) important human factor(s) relating to job design (A) Physiological (B) Social (C) Psychological (D) All of the above 6-The smallest work activities involve various elementary movements called (A) Therbligs (B) Therbilgs (C) Therblgis (D) Theriblgs 7-Vertical straight dials a...

Python Libraries a Data Scientist must know

Top 21 Python Libraries a Data Scientist must know Python is an abundant source of libraries . A Python library is a gathering of functions that assist one to perform many actions . It has myriad inbuilt libraries . Python contains ample libraries for data science . This tutorial covers Python libraries for a data scientist. 1. Data Cleaning and Data Manipulation Pandas NumPy Spacy SciPy 2. Data Gathering Beautiful Soap Scrapy Selenium 3. Data Visualisation Matplotlib Seaborn Bokeh Plotly 4. Data Modelling Scikit-Learn PyTorch TensorFlow Theano 5. Image Processing Scikit-Image Pillow OpenCV 6. Audio Processing pyAudioAnalysis Librosa Madmom

What is the process of Data Analysis?

Data analysis is the process of collecting, cleansing, interpreting, transforming and modeling data to gather insights and generate reports to gain business profits. Refer to the image below to know the various steps involved in the process. Fig 1: Process of Data Analysis – Data Analyst Interview Questions Collect Data: The data gets collected from various sources and is stored so that it can be cleaned and prepared. In this step, all the missing values and outliers are removed. Analyse Data: Once the data is ready, the next step is to analyze the data. A model is run repeatedly for improvements. Then, the model is validated to check whether it meets the business requirements. Create Reports: Finally, the model is implemented, and then reports thus generated are passed onto the stakeholders.

ArrayLinks

Search This Blog