Doubt asked @1:33:20:=> Dear Learner, please note that only the scores achieved by students officially registered for the project in the current term are considered for the final grade calculation (i.e., maximum marks). We do not include the scores of external participants in the grading process.
K-Nearest Neighbors (KNN) is a non-parametric, supervised learning algorithm used for classification and regression. Here are some key points and issues discussed in the video: KNN Basics: KNN involves choosing a number ( K ) of nearest neighbors. It assigns a class based on the majority vote of the nearest neighbors. It does not learn any weights or parameters from the data. Issues with KNN: Computationally Expensive: Finding the distance of a new point from all training points can be computationally expensive ([00:05:35]1). Scaling: Features with different scales can affect the distance calculation, so data scaling is necessary ([00:05:00]2). Overfitting and Underfitting: Choosing too few neighbors can lead to overfitting, while too many can lead to underfitting ([00:02:00]3). Memory Intensive: KNN requires storing all training data, making it memory-intensive ([00:04:00]4).
The video covers several key topics related to machine learning algorithms and techniques. Here are the major topics discussed: 1. **K-Nearest Neighbors (KNN) Algorithm** [00:00:41][^1^][1] * Explanation of KNN as a non-parametric algorithm * Importance of choosing the right number of neighbors (K) * Issues with KNN, such as computational expense and the need for data scaling 2. **KNN Imputer** [00:07:05][^2^][2] * Using KNN for imputing missing values in datasets * Explanation of Euclidean distance with missing values * Implementation details and code examples 3. **Support Vector Machines (SVM)** [00:45:01][^3^][3] * Overview of SVM and its applications * Importance of parameters like C and kernel functions * Practical tips for using SVM in machine learning projects 4. **Decision Trees** [00:47:17][^4^][4] * Explanation of decision trees and their advantages * How decision trees handle data without scaling * Examples and practical applications 5. **Ensemble Methods** [01:10:03][^5^][5] * Introduction to bagging and boosting techniques * Explanation of weak learners and their combination * Examples of voting estimators and random forests 6. **Clustering Algorithms** [01:28:05][^6^][6] * Overview of K-means clustering and its limitations * Real-time examples and applications of clustering * Introduction to hierarchical agglomerative clustering These topics provide a comprehensive review of various machine learning techniques and their practical applications.
Video summary [00:00:00][^1^][1] - [00:35:49][^2^][2]: This video is a revision session focused on data manipulation using the Pandas library in Python. It covers various methods to create, manipulate, and analyze data frames, including importing data, creating data frames from lists and dictionaries, indexing, and accessing data. The session also discusses useful functions like `info()`, `describe()`, and `value_counts()`, as well as methods for selecting data based on conditions, renaming columns, sorting data frames, and handling missing values. Additionally, it touches on advanced topics like method chaining, concatenating data frames, and using the `groupby()` function for aggregation. Highlights: + [00:00:00][^3^][3] **Introduction to Pandas** * Loading and manipulating data sets * Creating data frames from lists and dictionaries * Indexing and accessing data + [00:04:28][^4^][4] **Data frame attributes and methods** * Using `info()` and `describe()` * Summary statistics and data types * Copying data frames + [00:06:33][^5^][5] **Accessing data elements** * Using `iloc` and `loc` methods * Selecting rows and columns * Boolean indexing + [00:16:08][^6^][6] **Advanced data selection** * Selecting data with multiple conditions * Using `query()` method * String methods for columns + [00:25:08][^7^][7] **Data manipulation techniques** * Renaming columns and sorting data frames * Method chaining * Concatenating data frames and handling duplicates
Major topics discussed were : Video summary [00:00:00][^1^][1] - [02:27:02][^2^][2]: This video is a revision session for a machine learning course, focusing on data manipulation using the Pandas library. It covers various methods to create and manipulate data frames, handle missing values, and perform data transformations. Highlights: + [00:00:00][^3^][3] **Introduction to Pandas** * Loading and manipulating datasets * Creating data frames and series * Indexing and accessing data + [00:04:28][^4^][4] **Data frame operations** * Using info and describe methods * Copying data frames * Accessing elements with ioc and loc methods + [00:27:39][^5^][5] **Sorting and method chaining** * Sorting data frames by columns * Method chaining for efficient operations * Inserting records into data frames + [00:53:55][^6^][6] **Handling missing values** * Using simple imputer * Strategies like mean, median, and most frequent * Dealing with text data and feature hashing + [01:22:59][^7^][7] **Scaling and transforming data** * Different scaling methods * Function transformers * Label binarizer and one-hot encoding
Video summary [00:00:01][^1^][1] - [00:31:47][^2^][2]: This video is a revision session for an end-term exam, covering weeks 8 to 11 of a machine learning course. The instructor explains key concepts and algorithms, focusing on K-Nearest Neighbors (KNN) and its applications. Highlights: + [00:00:01][^3^][3] **Introduction and session overview** * Covers weeks 8 to 11 * Focus on KNN algorithm * Explanation of non-parametric nature + [00:01:00][^4^][4] **K-Nearest Neighbors (KNN)** * Non-parametric algorithm * Voting mechanism for classification * Importance of choosing the right K value + [00:04:00][^5^][5] **Scaling and distance computation** * Impact of feature scaling * Computational expense of KNN * Example of distance calculation + [00:07:00][^6^][6] **KNN imputer** * Handling missing values * Euclidean distance with weights * Implementation in code + [00:18:00][^7^][7] **Radius Neighbors Classifier** * Difference from KNN * Handling outliers * Voting within a defined radius + [00:28:00][^8^][8] **Support Vector Machines (SVM)** * Maximizing margin between classes * Hyperplanes and decision boundaries * Comparison with perceptron algorithm
Very good. I did MLP 3 terms ago, and all sessions (live and lectures) assumed that students are already good at Pandas, which most times is not true. I'm glad you've included basic ways of working with Pandas...Overall, the program is improving over the terms, and that's nice to see.
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Doubt asked @1:33:20:=> Dear Learner, please note that only the scores achieved by students officially registered for the project in the current term are considered for the final grade calculation (i.e., maximum marks). We do not include the scores of external participants in the grading process.
1:32:15 Exactly the same question 😂 Bro was actually fighting demons with his buddies, took a break just to attend the meeting.
K-Nearest Neighbors (KNN) is a non-parametric, supervised learning algorithm used for classification and regression. Here are some key points and issues discussed in the video: KNN Basics: KNN involves choosing a number ( K ) of nearest neighbors. It assigns a class based on the majority vote of the nearest neighbors. It does not learn any weights or parameters from the data. Issues with KNN: Computationally Expensive: Finding the distance of a new point from all training points can be computationally expensive ([00:05:35]1). Scaling: Features with different scales can affect the distance calculation, so data scaling is necessary ([00:05:00]2). Overfitting and Underfitting: Choosing too few neighbors can lead to overfitting, while too many can lead to underfitting ([00:02:00]3). Memory Intensive: KNN requires storing all training data, making it memory-intensive ([00:04:00]4).
The video covers several key topics related to machine learning algorithms and techniques. Here are the major topics discussed: 1. **K-Nearest Neighbors (KNN) Algorithm** [00:00:41][^1^][1] * Explanation of KNN as a non-parametric algorithm * Importance of choosing the right number of neighbors (K) * Issues with KNN, such as computational expense and the need for data scaling 2. **KNN Imputer** [00:07:05][^2^][2] * Using KNN for imputing missing values in datasets * Explanation of Euclidean distance with missing values * Implementation details and code examples 3. **Support Vector Machines (SVM)** [00:45:01][^3^][3] * Overview of SVM and its applications * Importance of parameters like C and kernel functions * Practical tips for using SVM in machine learning projects 4. **Decision Trees** [00:47:17][^4^][4] * Explanation of decision trees and their advantages * How decision trees handle data without scaling * Examples and practical applications 5. **Ensemble Methods** [01:10:03][^5^][5] * Introduction to bagging and boosting techniques * Explanation of weak learners and their combination * Examples of voting estimators and random forests 6. **Clustering Algorithms** [01:28:05][^6^][6] * Overview of K-means clustering and its limitations * Real-time examples and applications of clustering * Introduction to hierarchical agglomerative clustering These topics provide a comprehensive review of various machine learning techniques and their practical applications.
Video summary [00:00:00][^1^][1] - [00:35:49][^2^][2]: This video is a revision session focused on data manipulation using the Pandas library in Python. It covers various methods to create, manipulate, and analyze data frames, including importing data, creating data frames from lists and dictionaries, indexing, and accessing data. The session also discusses useful functions like `info()`, `describe()`, and `value_counts()`, as well as methods for selecting data based on conditions, renaming columns, sorting data frames, and handling missing values. Additionally, it touches on advanced topics like method chaining, concatenating data frames, and using the `groupby()` function for aggregation. Highlights: + [00:00:00][^3^][3] **Introduction to Pandas** * Loading and manipulating data sets * Creating data frames from lists and dictionaries * Indexing and accessing data + [00:04:28][^4^][4] **Data frame attributes and methods** * Using `info()` and `describe()` * Summary statistics and data types * Copying data frames + [00:06:33][^5^][5] **Accessing data elements** * Using `iloc` and `loc` methods * Selecting rows and columns * Boolean indexing + [00:16:08][^6^][6] **Advanced data selection** * Selecting data with multiple conditions * Using `query()` method * String methods for columns + [00:25:08][^7^][7] **Data manipulation techniques** * Renaming columns and sorting data frames * Method chaining * Concatenating data frames and handling duplicates
Major topics discussed were : Video summary [00:00:00][^1^][1] - [02:27:02][^2^][2]: This video is a revision session for a machine learning course, focusing on data manipulation using the Pandas library. It covers various methods to create and manipulate data frames, handle missing values, and perform data transformations. Highlights: + [00:00:00][^3^][3] **Introduction to Pandas** * Loading and manipulating datasets * Creating data frames and series * Indexing and accessing data + [00:04:28][^4^][4] **Data frame operations** * Using info and describe methods * Copying data frames * Accessing elements with ioc and loc methods + [00:27:39][^5^][5] **Sorting and method chaining** * Sorting data frames by columns * Method chaining for efficient operations * Inserting records into data frames + [00:53:55][^6^][6] **Handling missing values** * Using simple imputer * Strategies like mean, median, and most frequent * Dealing with text data and feature hashing + [01:22:59][^7^][7] **Scaling and transforming data** * Different scaling methods * Function transformers * Label binarizer and one-hot encoding
Video summary [00:00:01][^1^][1] - [00:31:47][^2^][2]: This video is a revision session for an end-term exam, covering weeks 8 to 11 of a machine learning course. The instructor explains key concepts and algorithms, focusing on K-Nearest Neighbors (KNN) and its applications. Highlights: + [00:00:01][^3^][3] **Introduction and session overview** * Covers weeks 8 to 11 * Focus on KNN algorithm * Explanation of non-parametric nature + [00:01:00][^4^][4] **K-Nearest Neighbors (KNN)** * Non-parametric algorithm * Voting mechanism for classification * Importance of choosing the right K value + [00:04:00][^5^][5] **Scaling and distance computation** * Impact of feature scaling * Computational expense of KNN * Example of distance calculation + [00:07:00][^6^][6] **KNN imputer** * Handling missing values * Euclidean distance with weights * Implementation in code + [00:18:00][^7^][7] **Radius Neighbors Classifier** * Difference from KNN * Handling outliers * Voting within a defined radius + [00:28:00][^8^][8] **Support Vector Machines (SVM)** * Maximizing margin between classes * Hyperplanes and decision boundaries * Comparison with perceptron algorithm
I missed this live...thanks for sharing.
Wow
Where do I find the remaining recorded sessions?
ruclips.net/video/oCJHtHzBVxw/видео.html
33:32 How to do submission
Good session. Covered lot of interesting topics
Very good. I did MLP 3 terms ago, and all sessions (live and lectures) assumed that students are already good at Pandas, which most times is not true. I'm glad you've included basic ways of working with Pandas...Overall, the program is improving over the terms, and that's nice to see.
But sir why are you watching the live sessions now after 3 terms?
@@storiesshubham4145 oh I'm doing the mlp project this time. That's why..
Great class! Loved it.
How can we join this practice session
MLP Project Session 1
Promo_SM 🤩
Hey!:<<<
End Term Sep Term
week 5
ρ尺ｏΜ𝐎ᔕᗰ
time stamp : 0 - 10:15 - Doubt clarification about MLP viva 10:15 onwards : OPPE-2 specifics
𝔭𝔯𝔬𝔪𝔬𝔰𝔪 💖