Code With Kal

Starting of october that is week 10 brought something interesting,i was introduced to Recommendation system by Dr. Sarabjot Singh Anand, Co-Founder Sabudh Foundation ,Er. Niharika Arora ,Data Scientist at Tatras Data and Er. Gurmukh Singh ,Trainee Data Scientist at Tatras Data. Recommender systems are one of the most successful and widespread application of machine learning technologies in business . You can apply recommender systems in scenarios where many users interact with many items. You can find large scale recommender systems in  retail ,  video on demand , or music streaming . In order to develop and maintain such systems, a company typically needs a group of expensive data scientist and engineers. That is why even large corporates such as BBC decided to  outsource  its recommendation services. Machine learning algorithms in recommender systems are typically classified into two categories — content based and collaborative filtering m...

ISB Videos This week we ended our ISB's Machine Learning course with the last two lectures which were on Text Analysis and Mining graphs. The topics covered were as follows: Word2vec Word2vec is a two-layer neural net that processes text. Its input is a text corpus and its output is a set of vectors: feature vectors for words in that corpus. While Word2vec is not a  deep neural network , it turns text into a numerical form that deep nets can understand. The purpose and usefulness of Word2vec are to group the vectors of similar words together in vector space. That is, it detects similarities mathematically. Word2vec creates vectors that are distributed numerical representations of word features, features such as the context of individual words. It does so without human intervention. T here are two types of Word2Vec, Skip-gram and Continuous Bag of Words (CBOW). I will briefly describe how these two methods work in the following paragraphs. Skip-gra...

Introduction to Machine Learning  As we humans always have the curiosity to do invention and do automation in the industrial area so that the work could be done automatically which requires lesser manpower. Recently ISRO(Indian Space Research Organization) had carried there Chandrayan-2  Mission where a rover was attached with the orbiter and its aim to extract information from the moon after its soft landing. But the mission seems not to be successful as the lander had a hard landing and due to failure in its component and the connection with the lander (VIKRAM) is lost. ISRO scientist is analyzing the data to establish a connection with the lander Vikram. Although it's a very tedious task ISRO scientist are trying there best to make the mission 100% Successful.  ISRO has many other Mission in hand to complete in the upcoming years. But how the scientist comes to any conclusion just like Chandrayan -1 mission which was not fully successful its seems ISRO did ...

                                                 DAY 1 Today , we first started with our ISB video lecture no.3 which was about "Bayesian Learning".In this lecture we learnt about different distributions(Bernoulli, Categorical & continuous probability densities).Next we learnt about Joint probability distributions and marginalisation.It was explained using the concept of generative and discriminative model. After break Vikram sir give us overview about the "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.After that sir discussed the ...

Continuing our ISB course on Unsupervised Learning, we completed 2 videos this week whose topics are as follows: Introduction to Bayesian Learning Bayes' Theorem :   Bayes’ theorem describes how the conditional probability of an event or a hypothesis can be computed using evidence and prior knowledge. The Bayes’ theorem is given by: P ( θ | X ) = P ( X | θ ) P ( θ ) P ( X )   P ( θ ) - P ( θ ) Prior Probability is the probability of the hypothesis  θ θ  being true before applying the Bayes’ theorem. Prior represents the beliefs that we have gained through past experience, which refers to either common sense or an outcome of Bayes’ theorem for some past observations. P ( X | θ ) P ( X | θ )  - Likelihood is the conditional probability of the evidence given a hypothesis. P ( X ) P ( X )  - Evidence term denotes the probability of evidence or data. Types of distributions:  Binomial distribution Bernoull...