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In this video, we will learn about CART (Classification And Regression Tree) algorithm in Machine Learning.&nbsp; CART algorithm works very similar to the decision trees but are little bit more sophisticated. There is a term known as Gini index which is the core part of the CART algorithm. Gini index is calculated by subtracting the squared summation of probability of occurrence of each class from one.&nbsp; This Gini index helps us to determine that which feature will be present at which level of the tree structure as there are 3 types of nodes in a tree.&nbsp; - Root Nodes - This is the node from which the tree start.&nbsp; - Internal Nodes - They are the internal nodes which makes the tree highly dense and sometimes leads to overfitting.&nbsp; - Leaf Nodes - They are the final predictors for the target class.&nbsp; Some of the advantages to use CART algorithm are robustness to outliers and are helps to determine non-linear boundaries between the different target classes present in the dataset. But due to high complexity sometimes it leads to overfitting and leads to unstable tree structure.&nbsp; &nbsp;CART in Machine Learning: <a href="https://www.geeksforgeeks.org/cart-classification-and-regression-tree-in-machine-learning/">https://www.geeksforgeeks.org/cart-classification-and-regression-tree-in-machine-learning/</a>

CART (Classification And Regression Tree) in Machine Learning

In this video, we will learn what is Multi-Layer Perceptron Learning and how it works. This is a concept of Neural Networks and the perceptron are nothing but another name for the neurons of the hidden layers.Main components of a multilayer perceptron are:<ul><li>Input Layer - The input features for which we would like to build our model.</li><li>Hidden Layer - This is where non-linear decision boundaries and complex functions are learned by our model.</li><li>Output Layer - This is where the objective of developing a multilayer perceptron lies as we get the final output or the predictions.</li></ul>What is an Activation Function?Activation functions are necessary to learn a non-linear complex model. A very common activation function is Sigmoid function which is used for binary classification purpose. Some other examples of activation function are Relu, tanh, selu, etc.Each layer have their own set of weights/parameters which are learned by the model by using Forward and Backward Propagation. Optimizers like gradient descent and loss function they all serve a purpose in optimizing these weights and parameters so, that the predictions made by the model are highly accurate.

Multi-Layer Perceptron Learning in Deep Learning

In this video, we will learn about RNN and its different types of architecture which are used for different types of applications. In CNN we are able to extract features from images using convolution operations but it is not able to memorize the data or the information regarding the sequence that is there in a dataset. Due to this lack of functionality RNN comes into the picture. Different types of RNN architecture are: 1) One-to-One 2) Many-to-One 3) One-to-Many 4) Many-to-Many Each type of architecture is used for different types of applications like Speech Recognition, Language Modelling, Name entity recognition, Music generation and many others as well. To improve these architectures slightly complex intermediate units like LSTM, and GRUs are used to build NLP models for real-world applications. Types of RNN: <a href="https://www.geeksforgeeks.org/types-of-recurrent-neural-networks-rnn-in-tensorflow/">https://www.geeksforgeeks.org/types-of-recurrent-neural-networks-rnn-in-tensorflow/</a>Introduction to RNN: <a href="https://www.geeksforgeeks.org/introduction-to-recurrent-neural-network/">https://www.geeksforgeeks.org/introduction-to-recurrent-neural-network/</a>

Recurrent Neural Network (RNN) and it's Types | Deep Learning

In this video, we will understand how to implement Linear Discriminant Analysis by using a dataset available in sklearn package. We will show how to use sklearn’s implementation of Linear Discriminant Analysis using wine dataset available in the sklearn package. We have three different classes of wines in the given dataset and we will apply LDA on this data set to convert it into two dimensional dataset. Then after plotting it we can see a clear distinction between the three classes of the wine. Linear discriminant analysis in Machine Learning: <a href="https://www.geeksforgeeks.org/ml-linear-discriminant-analysis/">https://www.geeksforgeeks.org/ml-linear-discriminant-analysis/</a>

Linear Discriminant Analysis (LDA) Implementation | Machine Learning

In this video, we will try to get the intuition behind the Linear Discriminant analysis.If you know PCA technique for dimensionality reduction then it will be easy understand this concept.Let us understand, what's the main difference between PCA and LDA?PCA tries to capture the maximum variance between the new set of features developed as compared to the previous ones.&nbsp; But in LDA we try to find a separation boundary for which we try to maximize the following function:$$J\left ( v \right ) = \frac{\left ( \mu _ 1 - \mu _ 2 \right )^2}{{s_1}^2 + {s_2}^2}$$Where&nbsp;\mu _ 1 and s_1 is the mean and standard deviation of the first features and similar is the definition for&nbsp;\mu _ 2 and s_2. What is Linear Discriminant Analysis (LDA)? Linear Discriminant Analysis is a dimensionality reduction technique that is commonly used for supervised classification problems. It is used for modelling differences in groups i.e. separating two or more classes. It is used to project the features in higher dimension space into a lower dimension space.&nbsp; ML | Linear Discriminant Analysis: <a href="https://www.geeksforgeeks.org/ml-linear-discriminant-analysis/">https://www.geeksforgeeks.org/ml-linear-discriminant-analysis/</a>

Linear Discriminant Analysis (LDA) Mathematical Intuition | Machine Learning

In this video, we are going to see the intuition of SMS classification problems using the Naive Bayes algorithm using Sklearn module in Python.The Naive Bayes algorithm is widely utilized to address issues in academia, government, and business thanks to a recent surge in popularity. A Naive Bayes classifier combines various useful characteristics in real-world machine learning. We'll explain the underlying assumptions later on.&nbsp; The two popular Naive Bayes classifiers are: Multinomial Naive Bayes and Bernoulli Naive Bayes.Steps involved in Naive Bayes classifiers:1) Load the dataset 2) Split X and y into training and testing sets 3) Train the model on the training set 4) Make predictions on the testing set 5) Compare actual response values with predicted response values

Naive Bayes Classifier Implementation | Machine Learning

In this video, we are going to understand the Naive Bayes algorithm in detail. The assumption of Naive Bayes is that each feature makes an equal &amp; independent contribution to the outcome. The Naive Bayes algorithm is widely utilized to address issues in academia, government, and business thanks to a recent surge in popularity. A Naive Bayes classifier combines a variety of useful characteristics in real-world machine learning. We'll explain the underlying assumptions later on. The two popular classifiers Naive Bayes are Multinomial Naive Bayes &amp; Bernoulli Naive Bayes. Other points that we can consider when studying Naive Bayes is that: 1) This classifier works well in many real-world situations. They require a small amount of training data to estimate the necessary parameters. 2) Naive Bayes learners and classifiers can be extremely fast compared to more sophisticated methods.&nbsp; Naive Bayes Algorithm <a href="https://www.geeksforgeeks.org/naive-bayes-classifiers/">https://www.geeksforgeeks.org/naive-bayes-classifiers/</a>

Naive Bayes Classifier In-depth Intuition | Machine Learning

In this video, we are going to see the implementation of the SVM Machine Learning algorithm using Python. The SVM is a supervised machine learning technique that can be used for classification as well as regression.Classification: When the output variable is a category, such as "red" or "blue," or "disease" and "no disease,".&nbsp; A classification model attempts to draw some conclusions from observed values. Given one or more inputs a classification model will try to predict the value of one or more outcomes.Regression: A regression problem is when the output variable is a real or continuous value, such as “salary” or “weight”. Many different models can be used the most basic of which being linear regression. It tries to fit data by using the best hyper-plane that passes through the points.Implementing SVM in Python -&nbsp; We will start with the standard libraries and then create a sample dataset, having linearly separable data, then divides the classes from each other by simply finding a line in case of two dimensions to achieve SVM is to divide the datasets into classes to find a maximum marginal hyperplane then we will use Scikit-Learn’s support vector classifier to train an SVM model on this data.SVM in Machine Learning: <a href="https://www.geeksforgeeks.org/support-vector-machine-algorithm/">https://www.geeksforgeeks.org/support-vector-machine-algorithm/</a> Classifying data using SVM in Python: <a href="https://www.geeksforgeeks.org/classifying-data-using-support-vector-machinessvms-in-python/">https://www.geeksforgeeks.org/classifying-data-using-support-vector-machinessvms-in-python/</a>

Support Vector Machine (SVM) Implementation in Machine Learning

In this video, we have covered the Support Vector Machine (SVM) technique in Machine Learning in detail. The SVM is a supervised machine learning technique that can be used for classification as well as regression.Classification: When the output variable is a category, such as "red" or "blue," or "disease" and "no disease,". From the observed values, the classification model tries to draw some conclusions. Given one or more inputs a classification model will try to predict the value of one or more outcomes.Regression: When the output variable is a real or continuous value, such as “salary” or “weight” then it is known as a regression problem. Many different models can be used the most basic of which being linear regression. It tries to fit data using the best hyper-plane that passes through the points.SVM in Machine Learning: <a href="https://www.geeksforgeeks.org/support-vector-machine-algorithm/">https://www.geeksforgeeks.org/support-vector-machine-algorithm/</a>

Support Vector Machine (SVM) in Machine Learning

The K-fold cross-validation technique is a technique of resampling the data set in order to evaluate a machine learning model. In this technique, the parameter K refers to the number of different subsets that the given data set is to be split into. Further, K-1 subsets are used to train the model and the left out subsets are used as a validation set.&nbsp;To get the K-fold cross-validation we will split the dataset into three sets, Training, Testing, and Validation, with the challenge of the volume of the data.&nbsp;1) Training - Here is the data being trained for the machine learning model to train the model and evaluate it on the validation set or test set2) Testing- The machine learning Model will test with an algorithm to Calculate the prediction error.3) Validation- Generate overall prediction error by taking the average of prediction errors in every case.&nbsp;Cross validation in Machine learning : <a href="https://www.geeksforgeeks.org/cross-validation-machine-learning/">https://www.geeksforgeeks.org/cross-validation-machine-learning/</a>

K Fold Cross-Validation | Machine Learning

Logistic regression is the most popular Machine Learning algorithm, it is a supervised learning algorithm that is used in binary classification problems while our target variable is categorical and it is a process of modeling the probability of a discrete outcome given an input variable. In this video, we will be implementing logistic regression using Python.The method of modeling the probability of a discrete result given an input variable is known as logistic regression. The most frequent logistic regression models have a binary outcome, which might be true or false, yes or no, and so forth.Implementation of Logistic Regression: <a href="https://www.geeksforgeeks.org/implementation-of-logistic-regression-from-scratch-using-python/">https://www.geeksforgeeks.org/implementation-of-logistic-regression-from-scratch-using-python/</a>

Logistic Regression Implementation | Machine Learning

Logistic Regression Machine learning is the study of algorithms that can improve automatically through experiences by using trained data. While training the model we use one of the popular Logistic Regression Algorithm Machine Learning.&nbsp;In this video, we will cover the following terminology-&nbsp;1) Logistic Regression: It is a supervised classification algorithm. it is one of the most popular ML algorithms, which is used to predict the categorical dependent variable using a given set of independent variables or predict the probability of a target variable (predicts the output of a categorical dependent variable).&nbsp;2) Sigmoid function: It is a mathematical function having a characteristic "S-shaped" or sigmoid curve.&nbsp;3) Mathematical imputation- We will also see mathematical concepts behind the classification kind of problem.&nbsp;4) Cost function: The cost function for logistic regression is proportional to the inverse of the likelihood of parameters. Hence, we can obtain an expression for the cost function.&nbsp;5) Threshold: it is choosing to turn a projected probability or scores into a class label.&nbsp;Logistic Regression: <a href="https://www.geeksforgeeks.org/understanding-logistic-regression/">https://www.geeksforgeeks.org/understanding-logistic-regression/</a>

Logistic Regression Algorithm (Mathematical Intuition) | Machine Learning

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