ML_Easily_Explained

Recently my nephew got 97 marks in his mathematics test . I told the little kid , hey !! you have an accuracy of 97% to which he asked what does it means. I told him when you divide number of correctly answered questions by total number of questions you get the accuracy . Then I told him about error as well which basically means ratio of number of wrongly answered questions (assuming he answered every questions ) to total number of questions.   Hope now you get the relationship between error and accuracy which is very much like higher the accuracy lesser  the error and vice versa. Similarly in machine learning accuracy is the ratio of correctly classified data points to the total number of data points. Let's say in cat and dog classification , features of cat is given to model and if model classifies it as a cat  then it is called as Correctly Classified point and if model classifies it as a dog then it is Misclassified point. Let's say we have total 1000 data points...

Today let's go for a long drive with car of words to the field where AI is helping in Agriculture Photo by  Isak Engström  on  Unsplash Some 30 years ago world population was around 5.5 billion .Today it's over 7.9 billion , and as per UN it will touch 9.3 billion  and to feed these humongous population  we will need to produce 60% more food . But current techniques of farming won't be able to cater the desired need ,not even close to suggested numbers. Thus we have no choice but to rely on Artificial Intelligence for scalability as well as efficiency.                                                   1. Tackling labor challenge:     It is evident that today less people are entering farming profession so there is shortage of workforce         in this area. This labor shortage can ...

Recently heard in news about ongoing 10th CBSE exams. Then suddenly I recalled  my 10th board exams days . PHEWWW..!!! how dreadful those examination days were. I remember as the exams were approaching near ,school teachers used to increase their teaching speed ,took extra lectures ,sometimes even ate up our sports time , trying to complete syllabus as soon as possible . After finishing the syllabus and taking up many revision sessions by our teachers ,finally we were ready for final battle of our school life that is final exams and fortunately we all scored well.  Hey wait!! I forgot to mention pre-board exams .After our revision session and when enemy named final exam was just outside our palace ready to attack us soon ,our teacher used to conduct an exam less dreadful than the final exams, commonly known as Pre-board exam.  I always wondered why the heck do they conduct these many exams . I asked my mother who is indeed a very good mathematics teacher as to what  ...

M achine  L earning ???? How can a machine even learn??  For a long period of time I always wondered what this fancy term meant and how would it be actually possible ..!! Let's figure it out today. To get an essence of what happens when we say Machine is learning ,why not understand in the first place how we learn. Well..!!! let us take a glance at those days when we learnt mathematics as a kid. I still remember how my maths teacher used to teach us basic addition and subtraction in class .Initially she helped us to count using fingers and then add or subtract those numbers. With a lot of practice and sometimes getting beaten up by her we finally learnt addition /subtraction. Mathematically speaking after lot of practice /training , our brain learnt a function of addition where if we get numbers to add ,it processed in a way to give the sum. Similarly Machine Learning is all about finding that magic function "f" wherein ,we give some input and desire some output . (EXC fo...

While dealing with any ML project one spends most of the time in data preprocessing .Data pre-processing is nothing but playing with the features present in the dataset ,data cleaning,data transformation ,removing or imputing missing feature values etc. Feature engineering corresponds to developing  new features based on current information . It may happen that few features are related to each other be it directly or inversely . Consider three cases:  Let's say radius and circumference are features present in the dataset ,so we know that they are directly dependent .Here if radius increases ,perimeter also increases.       2. Let's say speed and time are features present in the dataset ,so we know that they are inversely               proportional  .Here if speed increases ,time required to cover a distance decreases.         3. Let's say height and hair color are features present in the datas...