Week 5 (30/7/18 - 3/8/18 )

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Hands-on ML Algorithms

During the session with Vikram Sir, we did hands on various machine learning algorithms namely linear regression and logistic regression. We implemented logistic regression on a dataset provided by sir. The Steps are as follows:
  • Perform EDA on the dataset.
  • Using Feature Engineering techniques extract important features from the dataset.
  • Model Building

EDA

 Exploratory Data Analysis helps to remove noise , missing values from the dataset. EDA gives a better insight about the dataset .

Feature Engineering

 Feature engineering is the process of using domain knowledge of the data to create features that make machine learning algorithms work. If feature engineering is done correctly, it increases the predictive power of machine learning algorithms by creating features from raw data that help facilitate the machine learning process.

Model Building

 Pyhton's scikit learn library has various built-in ML Algo which are efficient enough. We learned about how to use them on the data and get predictions.


ISB Lecture 2

Prof. Sarabjot gave a lecture about machine learning providing statistical and theoratical indepth knowledge.

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