[0:11]Hello friends, welcome to Icon Engineering Tuition Classes channel. Today in this session we will talk about the subject IML (Introduction to Machine Learning) in computer branch semester 4. We will cover all the IMP questions from unit 1 to unit 5. We will discuss how these questions are asked in the exam, how many marks they carry, which chapter provides which concepts, and how to write them in the exam.
[0:40]We will understand which concept from which chapter is asked, how many marks it carries, and how to write it in the exam. We will also cover its marking scheme. If you are new to the channel, please subscribe and press the bell icon so that you can easily get all the updates from Icon Engineering Tuition Classes channel through notifications.
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[1:53]Today we will start our lecture from Unit 1 to Unit 5 with all those questions which are 110% sure to be asked in the exam. Okay, let's look at the first concept of the first unit, what it wants to say.
[2:08]The first question of the first unit is, define human learning. How does human learning differ from machine learning? You have to write that. Second, there will be difference between human learning and machine learning. This is also very important. Third concept will be define machine learning (ML). List types of ML. What are the types of machine learning and how many types are there? You may be asked to explain the types of machine learning in depth in 7 marks. You may also be asked to draw figures, and write advantages and disadvantages. Okay. Fourth will be compare the different type of machine learning. All the types of machine learning are asked to explain with figures. At the same time, you may be asked to differentiate them. Supervised, unsupervised and reinforcement learning. There are three types of machine learning. You may be asked to compare all three in the exam. Okay.
[3:05]Next comes the fifth concept: list applications of machine learning. Where is machine learning used? You have to state that here. Okay. Sixth will be list tools and technology used in machine learning with explanation. What type of tools and technology are used in machine learning? You may be asked to explain that. Okay. Many tools and technologies are used in machine learning. You may be asked to explain them in depth. Seventh will be how does machine learning work? Explain it with block diagram. You may also be asked to do that. Eighth will be provide the example of an application for each type of machine learning (supervised, unsupervised, and reinforcement learning). You have to write two examples for each of supervised, unsupervised, and reinforcement machine learning. Ninth will be identify types of machine learning are used for following problems. Intelligent robot, stock market prediction, and customer segmentation. When to provide customer segmentation, which machine learning is used? You have to write that. Tenth concept will be which type of machine learning system should you use to make a robot learn how to walk? Brief about selected model. Which type of machine learning model is used to create a robot? Meaning, which type of machine learning can create a robot? You have to write that. Very important concept.
[4:17]Eleventh concept will be which types of machine learning system should you use to learn spam email detection? Brief about selected model. Now the email that comes to you, is it spam or not spam? Which type of machine learning is used to define that? You have to explain it in depth. Let's look at the concepts of Unit 2. Now, the syllabus of Unit 2 in the IML subject of your entire semester 4 is the longest. It will almost contain Python programs.
[4:45]The first concept given is define Numpy. What is Numpy and explain its features? Second concept is given that you will be given functions here and you have to implement the given functions in Numpy library using Python. Meaning, you have to write a Python program in which all these functions should be included. Okay.
[5:06]Next will be any one function. You can be asked to explain any single function with an example. It's very important. Third concept will be how to define an array in Numpy? Create two Numpy arrays. 1. Array filled with all zeros and 2. Array filled with all ones. Combine both in single array and display their elements. You have to write programs according to the given conditions. Okay. Unit 2 will almost contain Python programs. Fourth will be how to create series from a list, Numpy array, and dict? How can we create a series from a list, Numpy array and dictionary? You have to write that. Fifth will be list and explain in brief commonly used mathematical functions in Numpy. You have to explain which mathematical functions are commonly used in Numpy by listing them. Sixth will be write a Numpy program to implement following operations. According to the two conditions given here, 1. to find the maximum and minimum value of a given single dimensional array. 2. to compute the mean, standard deviation, and variance of a given array along the second axis. Now you have to write a Python program for both of these. Seventh will be create a Numpy array with values [9,8,7,6,5,4]. Access the third element of the array. You have to create a Numpy array based on the given data and access the third element of the array. Eighth will be define Pandas. What is Pandas and explain the features of Pandas? You may be asked to do that. It's a very important concept. Ninth will be differentiate between Series and DataFrame in Pandas. What is the difference between Series and DataFrame in Pandas? You have to write that. Tenth concept will be write a Pandas program to implement following. 1. to convert the first column of a DataFrame as a Series. 2. to sort a given series [12,25,6,4,8,10,21,22]. You have to convert the first column into a DataFrame and sort the data. Okay.
[6:34]Next will be write a Pandas program to implement following operations. You will be given two conditions again and you have to write a Python program based on both conditions. Second will be differentiate between Numpy and Pandas. Create a series having the names of 3 students in your class and assign their roll numbers as index values. Also use attributes index, dtype, shape, ndim with series. Differentiate between Numpy and Pandas and create a series with 3 student names, assign roll numbers as index values and use attributes index, dtype, shape, ndim with series. Thirteenth will be write a Pandas program to find and drop the missing values from the given dataset. You have to write that. Fourteenth concept will be write a Pandas program to filter all columns where all entries present, check which rows and columns has a NaN and finally drop rows with any NaN from the given dataset. You have to find the NaN values from the given dataset and remove them from the system. You will also have to write a program for that. Okay.
[7:33]Fifteenth concept will be write and explain syntax of following operation in Pandas DataFrame. You have to remove duplicate rows and clean empty cells. You will have to write a Python program for that. Sixteenth concept will be discuss the steps involved in reading a CSV file in Pandas. What is a CSV file? You have to represent that with steps. Seventeenth concept will be write a Python program to implement the following function in Pandas. DataFrame(), drop(), dropna(), duplicated(). You have to write a Python program that includes all these four functions. Eighteenth will be what is Matplotlib? Explain features of Matplotlib library. What is Matplotlib and explain its features? Nineteenth concept will be how can you set the font size of a plot using Matplotlib? How can we decide the font size of a plot using Matplotlib? You have to write that. Next, you have to create a Python program. X-value is given which should be on x-axis, y-value is given which should be on y-axis. So you have to create a graph by representing the values on x-axis and y-axis using Matplotlib library. Next will be differentiate between plt.show() and plt.savefig() in Matplotlib. Write a program to plot line, pie, and bar graph with blue color, legends, and title. You have to differentiate between plt.show() and plt.savefig(), which are functions of Matplotlib library. Based on that, you have to create plot line, pie chart, bar graph, using colorful legends and titles. Twenty-second concept will be explain Grid chart with example. What is Grid chart? You may be asked to explain it in the exam. Twenty-third concept will be explain Histogram with example. What is Histogram? You may be asked to explain it. Twenty-fourth concept will be what is Scikit-learn? Explain features of it. What is Scikit-learn, also known as S-K-learn? You may be asked to explain its features. And the steps of Scikit-learn are especially important for building a machine learning model in the exam. Twenty-fifth will be list and explain steps involved in building a model in scikit-learn. How can you load a dataset in scikit-learn? Describe steps to build a model in scikit-learn.
[9:26]Unit number 3, let's go. First concept given is describe machine learning activities with diagram. You have to explain machine learning activities, describe them. Second concept will be list types of data in machine learning with explanations and examples. What types of data are used in machine learning? You have to explain them with examples. Third concept will be write the difference between 1) Nominal and Ordinal data, 2) Interval and Ratio data. You have to create two differences: one for nominal and ordinal, and one for interval and ratio data. Fourth will be relate the appropriate data type of the following example. You are given an example here. What type of data is used for it? You have to write that. Fifth concept will be differentiate between numerical and categorical data. You have to differentiate between numerical and categorical data. Sixth will be explain confusion matrix with suitable example. Explain confusion matrix with example. Seventh concept will be describe K-fold cross validation. K-fold cross validation method, asked in every single exam, is 110% sure to be asked. K-fold cross validation method, what is it? You have to explain it here. Eighth will be explain performance improvement in machine learning. How does performance improve in machine learning and what is it? You have to write that. Ninth concept will be how to handle missing values in data remediation? How should missing values in data remediation be handled? You have to explain that. Tenth concept will be define outliers. What are outliers and how to remove them by explaining with examples? You have to write that. Eleventh concept will be explain the importance of dimensionality reduction and feature subset selection in data preprocessing. Dimensionality reduction and feature subset selection are types of data preprocessing. You have to explain them in depth, which can be asked in seven marks.
[11:16]Unit number 4, let's see. First will be write applications of supervised machine learning. You have to write the applications of supervised machine learning. Second will be steps of supervised machine learning. You have to write the steps to create supervised machine learning. Third will be list out types of supervised learning explain any one in detail. You have to list the types of supervised machine learning and explain any one of them. If you know classification, then explain classification. If you know regression, then explain regression. Both are types of supervised learning. You can explain any one of them according to your choice. Fourth concept will be define classification. Classification, what is it, and write the steps of classification. You will have to show the steps of classification using a flowchart. The flowchart should be included inside, then you will get 7 marks. Next will be define classification, illustrate classification learning steps in details. Explain classification steps in detail. Okay.
[12:00]Sixth concept will be K-NN algorithm. K-NN algorithm is asked in every single machine learning exam. All the machine learning exams that have happened so far have included the K-NN algorithm concept. It is a very important algorithm. You may be asked to explain it. Seventh will be what is decision tree and explain decision tree in detail? Explain decision tree here. Eighth will be concept of support vector machine (SVM) in classification. What is SVM in classification? You have to write that. Ninth concept will be explain logistic regression. What is logistic regression? You have to explain that. Tenth concept will be define regression, list types of regression. What is regression? You may be asked to explain any one regression with an example. And one more important thing, when we explain the types of regression, we should not miss the advantages and disadvantages. Okay. Eleventh concept will be list out types of regression analysis. Explain linear regression in detail. And explain with diagram. Twelfth concept will be write the application of linear regression. Where is linear regression used? You have to write its applications. Next will be write the mathematical equation of linear regression. The mathematical equation used in linear regression can be asked for one mark. Fourteenth concept will be define simple linear regression using a graph explaining slope. Find the slope of the graph where the lower point on the line is represented as (-3,-2) and the higher point on the line is represented as (2,2). You have to solve the regression sum here. You have to create a graph by representing two values in minus and two values in plus using a line. You have to calculate the sum. Fifteenth concept will be differentiate between classification and regression. Classification and regression, you have to differentiate between them. Sixteenth concept will be differentiate linear regression with logistic regression. You have to differentiate linear regression and logistic regression.
[13:05]Unit number 5 concepts, let's see. First will be list applications of unsupervised learning. What is unsupervised machine learning? You have to explain its application. Second will be explain the advantages of unsupervised machine learning. Third will be real-world examples of unsupervised machine learning. What are the real-world examples of unsupervised machine learning? You have to list them out and explain them. Fourth will be answer the following 1. Need of unsupervised learning. 2. Working of unsupervised learning. What is the use of unsupervised machine learning? You have to explain its working. Unsupervised machine learning's working, you may be asked to explain it. Fifth concept will be list clustering methods. What is clustering? Any one clustering method can be explained in the exam. Next, what is clustering and explain its techniques briefly? Or it can directly say explain K-means clustering algorithm. K-means clustering algorithm is also a very important algorithm, which can be asked for 7 marks in the exam. Sixth will be what are the broad three categories of clustering techniques? Explain the characteristics of each briefly. What are the main three techniques of clustering? You have to explain them with their characteristics. Seventh will be what is association with diagram? What is association and you have to explain the process of association step by step with diagram. Eighth will be explain how the Market Basket Analysis uses the concepts of association analysis. Market Basket Analysis is a very important concept, which is asked in every single exam. Ninth will be explain any one method of association. Any one method of association can be explained here. Okay. Here you are given different types of applications of unsupervised machine learning. How can we manage through that application? You have to write that. Eleventh concept will be differentiate between supervised machine learning and unsupervised machine learning. Supervised and unsupervised, you have to differentiate between both. Twelfth concept will be define generative AI, explain working of generative AI. What is generative AI? You have to explain its working here. Thirteenth concept will be list applications of generative AI. You have to list out the applications of generative AI and explain them. Fourteenth concept will be case study, simulate port-wide hydrogen adoption, and forecast emission reductions with generative AI and green hydrogen. You have to explain hydrogen adoption and forecast emission reduction using generative AI and green hydrogen. Okay. So these are your concepts from Unit 1 to Unit 5, which are 110% sure to be asked as IMP in the exam. And if you prepare only this much, you can get the best ranking in GTU, 70 out of 70. For that, you can get the PDF of all the questions I gave you from Icon Engineering Tutorials app by buying your branch-related semester course and you can start your GTU preparation. Thank you guys for watching this video.
