Buzz About Machine Learning – Do you want to learn ?

What is Machine Learning ?
Before introducing machine learning, let us answer some of these questions.

  • Why machine learning?
  • How does it matter to you? etc.
  • If not master the art of Machine Learning, why do you need to understand the basics of it?

The answer of all these questions is very simple, with each passing day, the significance of data is improving. Data is future. And one of the primary goal of Machine Learning is to understand the structure of data and fit that data into models that can be understood and utilized by people.

Machine Learning a quick overview
Machine learning is a subfield of artificial intelligence (AI). Although machine learning is a field within computer science, it differs from traditional computational approaches. Machine learning algorithms allow for computers to train on data inputs and use statistical analysis in order to get output values that fall within a specific range. Simply put together Machine Learning will help to structure data, generate mathematical models on the data and make predictions.
A few examples:

  • Virtual Personal Assistants : Siri, Alexa, Google Now are some of the popular examples of virtual personal assistants. As the name suggests, they assist in finding information, when asked over voice. Machine learning is an important part of these personal assistants as they collect and refine the information on the basis of your previous involvement with them. Later, this set of data is utilized to render results that are tailored to your preferences.
  • Personalized health monitoring : Smart watches and other wearable devices have made health telemetry a reality. But machine learning is taking things one step further, allowing doctors and relatives to monitor the health of elderly family members. The more personal data these algorithms are fed, the better they understand a user’s profile, enabling healthcare professionals to spot potential anomalies earlier on.


“So it’s all about patterns and predicting the results and future on the basis of those patterns. As a conclusion to the definition we can say that machine learning plays an important role in data mining, image processing, and language processing”

The Buzz About Machine Learning :
Firstly, if we see a machine learning process we figure out that to correctly do with machine learning; we need to feed it with lots of data. The data could be structured or un-structured, and the good thing is that we have that data. In this era of Big Data, we not only have a lot of data but we also have the computation power to process that data. Not only this; we talk about cloud computing which requires operative and implied machine learning algorithms, which we also have. All these things were not available earlier as they are now to us.
Does all this sound interesting ? Do you really want to be a part of this ever expanding area and make your future align with one of the most in demand area ?
ATA and DevOps++ Alliance is organizing CP-ML&DS program in Pune.
Learn Machine Learning and Data Science through this program (CP-ML&DS)
Key Highlights of (CP-ML&DS):

  1. The World of Machine Learning
  2. Setting up Environment for Machine Learning
    (Anaconda, Python introduction, Numpy, Panda, Matpotlib, case studies and relevant exercises)
  3. Exploratory Data Analysis
    (Need of Data, pre-processing of data, boxplot to visualize data, data and column relationships)
  4. Simple Linear Regression
    (Using scikit-learn for Machine Learning basic concepts of linear regression, its application on a data set, accuracy of linear model, scikit-learn usage)
  5. Multiple Linear Regression
    (How to solve multiple linear regression use case, applying linear regression on different datasets, selecting best feature for accuracy)
  6. Classification using Logistic Regression
    (Apply logistic regression to different datasets using scikit-learn, where logistic regression can be applied. )

This program addresses two basic needs

  1. Practical tool based Machine learning and Data Science exposure for every working professional
  2. Allow working professionals to acquire this knowledge in the most agile manner

Tool Coverage : Pandas, NumPy, Jupyter, Anaconda, Scikit, Python
The program brochure and the detailed learning objectives is available on the following URL :

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