Gradient Descent and Cross Validation: Gradient Descent is an iterative approach to approximating the Parameters that minimize a Differentiable Loss Function. Cross Validation is a resampling procedure used to evaluate Machine Learning Models on a limited Data sample which has a parameter that splits the data into number of groups. On my Journey of Machine Learning and Deep Learning, Today I have read in brief about the fundamental Topics such as Calculus, Matrices, Matrix Calculus, Random Variables, Density Functions, Distributions, Independence, Maximum Likelihood Estimation and Conditional Probability. I have also read and Implemented about Gradient Descent and Cross Validation. I am starting this Journey from Scratch and I am following the Book:Machine Learning From Scratch. I have presented the Implementation of Gradient Descent and Cross Validation here in the Snapshots. I hope you will also spend some time reading the Topics from the Book mentioned above. I am excited about the days to come!!
Ordinary Linear Regression: Linear Regression is a linear approach to modelling the relationships between a scalar response or dependent variable and one or more explanatory variables or independent variables. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Ordinary Linear Regression, Parameter Estimation, Minimizing Loss and Maximizing Likelihood along with the Construction and Implementation of the LR from the Book Machine Learning From Scratch. I have also started reading the Book A Comprehensive Guide to Machine Learning which focuses on Mathematics and Theory behind the Topics. I have read about Regression, Ordinary Least Squares, Vector Calculus, Orthogonal Projection, Ridge Regression, Feature Engineering, Fitting Ellipses, Polynomial Features, Hyperparameters and Validation, Errors and Cross Validation from this book. I have presented the Implementation of Linear Regression along with Visualizations using Python here in the Snapshots. I hope you will also spend some time reading the Topics and Books mentioned above. Excited about the days ahead!!
On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Regularized Regression such as Ridge Regression and Lasso Regression, Bayesian Regression, GLMs, Poisson Regression along with Construction and Implementation of the same from the Book Machine Learning From Scratch. I have also read the Book A Comprehensive Guide to Machine Learning which focuses on Mathematics and Theory behind the Topics. I have read about Maximum Likelihood Estimation or MLE and Maximum a Posteriori or MAE for Regression, Probabilistic Model, Bias Variance Tradeoff, Metrics, Bias Variance Decomposition, Alternative Decomposition, Multivariate Gaussians, Estimating Gaussians from Data, Weighted Least Squares, Ridge Regression, and Generalized Least Squares from this Book. I have presented the Implementation of Ridge Regression, Lasso Regression along with Cross Validation, Bayesian Regression and Poisson Regression using Python here in the Snapshot. I hope you will also spend some time reading the Topics and Books mentioned above. Excited about the days ahead!!
On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Discriminative Classifiers such as Binary and Multiclass Logistic Regression, The Perceptron Algorithm, Parameter Estimation, Fishers Linear Discriminant and Fisher Criterion along with Construction and Implementation of the same from the Book Machine Learning From Scratch. I have also read the Book A Comprehensive Guide to Machine Learning which focuses on Mathematics and Theory behind the Topics. I have read about Kernels and Ridge Regression, Linear Algebra Derivation, Computational Analysis, Sparse Least Squares, Orthogonal Matching Pursuit, Total Least Squares, Low rank Formulation, Dimensionality Reduction, Principal Component Analysis, Projection, Changing Coordinates, Minimizing Reconstruction Errors and Probabilistic PCA from this Book. I have presented the Implementation of Binary and Multiclass Logistic Regression, The Perceptron Algorithm and Fishers Linear Discriminant using Python here in the Snapshot. I hope you will also spend some time reading the Topics and Books mentioned above. Excited about the days ahead!!
On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Generative Classifiers such as Linear Discriminative Analysis or LDA, Quadratic Discriminative Analysis or QDA, Naive Bayes, Parameter Estimation and Data Likelihood along with Construction and Implementation of the same from the Book Machine Learning From Scratch. I have also read the Book A Comprehensive Guide to Machine Learning which focuses on Mathematics and Theory behind the Topics. I have read about Generative and Discriminative Classification, Bayes Decision Rule, Least Squares Support Vector Machines, Feature Extension, Neural Network Extension, Binary and Multiclass Logistic Regression, Loss Function, Training, Multiclass Extension, Gaussian Discriminant Analysis, QDA and LDA Classification and Support Vector Machines from this Book. I have presented the Implementation of LDA, QDA and Naive Bayes along with Visualizations using Python here in the Snapshot. I hope you will also spend some time reading the Topics and Books mentioned above. Excited about the days ahead!!
Decision Trees: A Decision Tree is an interpretable machine learning for Regression and Classification. It is a flow chart like structure in which each internal node represents a Test on an attribute and each branch represents the outcome of the Test. On my Journey of Machine Learning and Deep Learning, Today I have read about Decision Trees such as Regression Trees and Classification Trees, Building Trees, Making Splits and Predictions, Hyperparameters, Pruning and Regularization along with Construction and Implementation of the same from the Book Machine Learning From Scratch. I have also read the Book A Comprehensive Guide to Machine Learning which focuses on Mathematics and Theory behind the Topics. I have read about Decision Tree Learning, Entropy and Information, Gini Impurity, Stopping Criteria, Random Forests, Boosting and AdaBoost, Gradient Boosting and KMeans Clustering from this Book. I have presented the Implementation of Regression Trees and Classification Trees using Python here in the Snapshot. I hope you will also spend some time reading the Topics and Books mentioned above. Excited about the days ahead!!
Tree Ensemble Methods: Ensemble Methods combine the outputs of multiple simple Models which is often called Learners in order to create the fine Model with low variance. Due to their high variance, a decision trees often fail to reach a level of precision comparable to other predictive algorithms and Ensemble Methods minimize the variance. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Tree Ensemble Methods such as Bagging for Decision Trees, Bootstrapping, Random Forests and Procedure, Boosting, AdaBoost for Binary Classification, Weighted Classification Trees, The Discrete AdaBoost Algorithm and AdaBoost for Regression along with Construction and Implementation of the same from the Book Machine Learning From Scratch. I have presented the Implementation of Bagging, Random Forests and AdaBoost along with different base estimators using Python here in the Snapshot. I hope you will also spend some time reading the Topics and Book mentioned above. Excited about the days ahead !!
On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Neural Networks from the Book Machine Learning From Scratch. I have read about Model Structure, Communication between Layers, Activation Functions such as ReLU, Sigmoid, The Linear Activation Function, Optimization, Back Propagation, Calculating Gradients, Chain Rule and Observations, Loss Functions along with Construction using The Loop Approach and The Matrix Approach and Implementation of the same from this Book. I have also read the Book A Comprehensive Guide to Machine Learning which focuses on Mathematics and Theory behind the Topics. I have read about Convolutional Neural Networks and Layers, Pooling Layers, Back Propagation for CNN, ResNet and Visual Understanding of CNNs from this Book. Besides, I have seen a couple of videos of Neural Networks and Deep Learning. I have presented the simple Implementation of Neural Networks with The Functional API and The Sequential API using TensorFlow here in the Snapshot. I hope you will also spend some time reading the Topics and Books mentioned above. Excited about the days ahead !!
Reinforcement Learning: In Reinforcement Learning, The Learning system called an agent in a particular context can observe the environment, select and perform actions and get rewards in return or penalties in the form of negative rewards. It must learn by itself what is the best policy to get the most reward over time. On my Journey of Machine Learning and Deep Learning, Today I have started reading and Implementing from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have read briefly about The Machine Learning Landscape viz. Types of Machine Learning Systems such as Supervised and Unsupervised Learning, Semisupervised Learning, Reinforcement Learning, Batch Learning and Online Learning, Instance Based Learning and Model Based Learning from this Book. I have presented the simple Implementation of Linear Regression and KNearest Neighbors along with a simple plot using Python here in the Snapshot. I hope you will also spend some time reading the Topics and Book mentioned above. Excited about the days ahead!!
Book:
Hands On Machine Learning with Scikit Learn, Keras and TensorFlow
Day10 of 300DaysOfData!
On my Journey of Machine Learning and Deep Learning, Today I have read about the Main Challenges of Machine Learning such as Insufficient Quantity of Training Data, Non representative Training Data, Poor Quality Data, Irrelevant Features, Overfitting and Underfitting the Training Data and Testing and Validating, Hyperparameter Tuning and Model Selection and Data Mismatch from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have started working on California Housing Prices Dataset which is included in this Book. I will build a Model of Housing Prices in California in this Project. I have presented the simple Implementation of Data Processing and few techniques of EDA using Python here in the Snapshot. I have also presented the Implementation of Sweetviz Library for Analysis here. I really appreciate Chanin Nantasenamat for sharing about this Library in one of his videos. I hope you will also spend some time reading the Topics and Book mentioned above. Excited about the days ahead!!
Book:
Hands On Machine Learning with Scikit Learn, Keras and TensorFlow
On my Journey of Machine Learning and Deep Learning, Today I have learned and Implemented about Creating categories from attributes, Stratified Sampling, Visualizing Data to gain insights, Scatter Plots, Correlations, Scatter Matrix and Attribute Combinations from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have continued working with California Housing Prices Dataset which is included in this Book. This Dataset was based on Data from the 1990 California Census. I will build a Model of Housing Prices in California in this Project. I am still working on the same. I have presented the Implementation of Stratified Sampling, Correlations using Scatter Matrix and Attribute combinations using Python here in the Snapshots. I have also presented the Snapshots of Correlations using Scatter plots here. I hope you will spend some time working on the same and reading the Topics and Book mentioned above. Excited about the days ahead !!
Book:
Hands On Machine Learning with Scikit Learn, Keras and TensorFlow
On my Journey of Machine Learning and Deep Learning, Today I have learned and Implemented about Preparing the Data for Machine Learning Algorithms, Data Cleaning, Simple Imputer, Ordinal Encoder, OneHot Encoder, Feature Scaling, Transformation Pipeline, Standard Scaler, Column Transformer, Linear Regression, Decision Tree Regressor and Cross Validation from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have continued working with California Housing Prices Dataset which is included in this Book. This Dataset was based on Data from the 1990 California Census. I will build a Model of Housing Prices in California in this Project. The Notebook contains almost every Topics mentioned above. I have presented the Implementation of Data Preparation, Handling missing values, OneHot Encoder, Column Transformer, Linear Regression, Decision Tree Regressor along with Cross Validation using Python here in the Snapshots. I hope you will spend some time working on the same and reading the Topics and Book mentioned above. Excited about the days ahead !!
Book:
Hands On Machine Learning with Scikit Learn, Keras and TensorFlow
On my Journey of Machine Learning and Deep Learning, Today I have learned and Implemented about Random Forest Regressor, Ensemble Learning, Tuning the Model, Grid Search, Randomized Search, Analyzing the Best Models and Errors, Model Evaluation, Cross Validation and few more Topics related to the same from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have completed working with California Housing Prices Dataset which is included in this Book. This Dataset was based on Data from the 1990 California Census. I have built a Model using Random Forest Regressor of California Housing Prices Dataset to predict the price of the Houses in California. I have presented the Implementation of Random Forest Regressor and Tuning the Model with Grid Search and Randomized Search along with Cross Validation using Python here in the Snapshot. I hope you will spend some time working on the same and reading the Topics and Book mentioned above. Excited about the days ahead!!
Book:
Hands On Machine Learning with Scikit Learn, Keras and TensorFlow
Confusion Matrix: Confusion Matrix is a better way to evaluate the performance of a Classifier. The general idea of Confusion Matrix is to count the number of times instances of Class A are classified as Class B. This approach requires to have a set of predictions so that they can be compared to the actual targets. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Classification, Training a Binary Classifier using Stochastic Gradient Descent, Measuring Accuracy using Cross Validation, Implementation of CV, Confusion Matrix, Precision and Recall and their Curves and few more Topics related to the same from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have presented the Implementation of SGD Classifier in MNIST Dataset along with Precision and Recall using Python here in the Snapshots. I have also presented the curves of Precision and Recall here. I hope you will spend some time working on the same and reading the Topics and Book mentioned above. I am excited about the days ahead!!
Book:
Hands On Machine Learning with Scikit Learn, Keras and TensorFlow
Day15 of 300DaysOfData!
On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about The ROC Curve, Random Forest Classifier, SGD Classifier, Multi Class Classification, One vs One and One vs All Strategies, Cross Validation, Error Analysis using Confusion Matrix, Multi Class Classification, KNeighbors Classifier, Multi Output Classification, Noises, Precision and Recall Tradeoff and few more Topics related to the same from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have completed the Topic Classification from this Book. I have presented the Implementation of The ROC Curve, Random Forest Classifier in Multi Class Classification, The One vs One Strategy, Standard Scaler, Error Analysis, Multi Label Classification and Multi Output Classification using Scikit Learn here in the Snapshots. I hope you will also work on the same. I hope you will also spend some time reading the Topics and Book mentioned above. I am excited about the days ahead!!
Book:
Hands On Machine Learning with Scikit Learn, Keras and TensorFlow
Day16 of 300DaysOfData!
Ridge Regression: Ridge Regression is a regularized Linear Regression viz. a regularization term is added to the cost function which forces the learning algorithm to not only fit the Data but also keep the model weights as small as possible. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Training the Models, Linear Regression, The Normal Equations and Computational Complexity, Cost Function and Gradient Descent such as Batch Gradient Descent, Convergence Rate, Stochastic Gradient Descent, Mini batch Gradient Descent, Polynomial Regression and Poly Features, Learning Curves, Bias and Variance Tradeoff, Regularized Linear Models such as Ridge Regression and few more related to the same from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have presented the Implementation of Polynomial Regression, Learning Curves and Ridge Regression along with Visualization using Python here in the Snapshots. I hope you will spend some time working on the same and reading the Topics and Book mentioned above. Excited about the days ahead!!
Book:
Hands On Machine Learning with Scikit Learn, Keras and TensorFlow
Day17 of 300DaysOfData!
Elastic Net: Elastic Net is a middle grouped between Ridge Regression and Lasso Regression. The regularization term r is a simple mix of both Ridge and Lasso's regularization terms. When r equals 0, it is equivalent to Ridge and when r equals 1, it is equivalent to Lasso Regression. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Lasso Regression, Elastic Net, Early Stopping, SGD Regressor, Logistic Regression, Estimating Probabilities, Training and Cost Function, Sigmoid Function, Decision Boundaries, Softmax Regression or Multinomial Logistic Regression, Cross Entropy and few more Topics related to the same from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have just started reading the Topic Support Vector Machines. I have presented the simple Implementation of Lasso Regression, Elastic Net, Early Stopping, Logistic Regression and Softmax Regression using Scikit Learn here in the Snapshots. I hope you will spend some time working on the same and reading the Topics and Book mentioned above. Excited about the days ahead!!
Book:
Hands On Machine Learning with Scikit Learn, Keras and TensorFlow
Day18 of 300DaysOfData!
Support Vector Machines: A Support Vector Machines or SVM is a very powerful and versatile Machine Learning model which is capable of performing Linear and Nonlinear Classification, Regression and even outlier detection. SVMs are particularly well suited for classification of complex but medium sized datasets. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Support Vector Machines, Linear SVM Classification, Soft Margin Classification, Nonlinear SVM Classification, Polynomial Regression, Polynomial Kernel, Adding Similarity Features, Gaussian RBF Kernel, Computational Complexity, SVM Regression which is Linear as well Nonlinear and few more Topics related to the same from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have presented the Implementation of Nonlinear SVM Classification using SVC and Linear SVC along with Visualization using Python here in the Snapshots. I hope you will spend some time working on the same and reading the Topics and Book mentioned above. Excited about the days ahead!!
Book:
Hands On Machine Learning with Scikit Learn, Keras and TensorFlow
Day19 of 300DaysOfData!
Voting Classifiers: Voting Classifiers are the classifiers which aggregates the predictions of different Classifiers and predicts the class that gets the most votes. The majority vote classifier is called a Hard Voting Classifier. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Ensemble Learning and Random Forests, Voting Classifiers such as Hard Voting and Soft Voting Classifiers and few more topics related to the same. Actually, I have also started working on a Research Project with an amazing Team. I have presented the Implementation of Hard Voting and Soft Voting Classifiers using Scikit Learn here in the Snapshots. I hope you will spend some time working on the same and reading the Topics mentioned above. Excited about the days ahead!!
Book:
Hands On Machine Learning with Scikit Learn, Keras and TensorFlow
Day20 of 300DaysOfData!
The CART Training Algorithm: The Algorithm which represents Scikit Learn's Implementation of the Classification and Regression Tree or CART Training algorithm to train Decision Trees also called Growing Trees. It's working principle is splitting the Training set into two subsets using a feature and a threshold. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Decision Functions and Predictions, Decision Trees, Decision Tree Classifier, Making Predictions, Gini Impurity, White Box Models and Black Box Models, Estimating Class Probabilities, The CART Training Algorithm, Computational Complexities, Entropy, Regularization Hyperparameters, Decision Tree Regressor, Cost Function and Instability from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have presented the simple Implementation of Decision Tree Classifier and Decision Tree Regressor along with Visualization of the same using Python here in the Snapshots. I hope you will spend some time working on the same and reading the Topics and Book mentioned above. Excited about the days ahead!!
Book:
Hands On Machine Learning with Scikit Learn, Keras and TensorFlow
Day21 of 300DaysOfData!
Bagging and Pasting: It refers to the approach which uses the same Training Algorithm for every predictor but to train them on different random subsets of the Training set. When sampling is performed with replacement, it is called Bagging and when sampling is performed without replacement, it is called Pasting. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Ensemble Learning and Random Forests, Voting Classifiers, Bagging and Pasting in Scikit Learn, Out of Bag Evaluation, Random Patches and Random Subspaces, Random Forests, Extremely Randomized Trees Ensemble, Feature Importance, Boosting, AdaBoost, Gradient Boosting and few more Topics related to the same from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have presented the Implementation of Bagging Ensembles, Decision Trees, Random Forest Classifier, Feature Importance, AdaBoost Classifier and Gradient Boosting using Python here in the Snapshots. I hope you will spend some time working on the same and reading the Topics and Book mentioned above. Excited about the days ahead!!
Book:
Hands On Machine Learning with Scikit Learn, Keras and TensorFlow
Day22 of 300DaysOfData!
Manifold Learning: Manifold Learning refers to the Dimensionality Reduction Algorithms that work by modeling the manifold on which the training instances lie which relies on manifold hypothesis which holds that most real world high dimensional datasets to a much lower dimensional manifold. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Gradient Boosting, Early Stopping, Stochastic Gradient Boosting, Extreme Gradient Boosting or XGBoost, Stacking and Blending, Dimensionality Reduction, Curse of Dimensionality, Approaches for Dimensionality Reduction, Projection and Manifold Learning and few more Topics related to the same from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have presented the Implementation of Gradient Boosting with Early Stopping along with Visualization using Scikit Learn here in the Snapshots. I hope you will spend some time working on the same and reading the Topics and Book mentioned above. Excited about the days ahead!!
Book:
Hands On Machine Learning with Scikit Learn, Keras and TensorFlow
Day23 of 300DaysOfData!
Incremental PCA: Incremental PCA or IPCA Algorithms are the algorithms in which we can split the Training set into mini batches and feed an IPCA Algorithm one mini batch at a time. It is useful for large Training sets and also to apply PCA online. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Principal Component Analysis or PCA, Preserving the Variance, Principal Components, Projecting Down the Dimensions, Explained Variance Ratio, Choosing the Right Number of Dimensions, PCA for Compression and Decompression, Reconstruction Error, Randomized PCA, SVD, Incremental PCA and few more Topics related to the same from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have presented the Implementation of PCA, Randomized PCA and Incremental PCA along with Visualizations using Scikit Learn here in the Snapshots. I hope you will spend some time working on the same. I hope you will also spend some time reading the Topics and Book mentioned above. Excited about the days ahead!!
Book:
Hands On Machine Learning with Scikit Learn, Keras and TensorFlow
Day24 of 300DaysOfData!
Clustering: Clustering Algorithms are the algorithms whose goal is to group similar instances together into Clusters. It is a great tool for Data Analysis, Customer Segmentation, Recommender Systems, Search Engines, Image Segmentation, Dimensionality Reduction and many more. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Kernel Principal Component Analysis, Selecting a Kernel and Tuning Hyperparameters, Pipeline and Grid Search, Locally Linear Embedding, Dimensionality Reduction Techniques such as Multi Dimensional Scaling, Isomap and Linear Discriminant Analysis, Unsupervised Learning such as Clustering and KMeans Clustering Algorithm and few more Topics related to the same from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have presented the Implementation of Kernel PCA and Grid Search CV, and KMeans Clustering Algorithm along with a Visualization using Python here in the Snapshots. I hope you will spend some time working on the same and reading the Topics and Book mentioned above. Excited about the days ahead!!
Book:
Hands On Machine Learning with Scikit Learn, Keras and TensorFlow
Day25 of 300DaysOfData!
Image Segmentation: Image Segmentation is the task of partitioning an Image into multiple segments. In Semantic Segmentation, all the pixels that are part of the same object type get assigned to the same segment. In Instance Segmentation, all pixels that are part of the individual object are assigned to the same segment. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about KMeans Algorithms, Centroid Initialization, Accelerated KMeans and Mini Batch KMeans, Finding the Optimal Numbers of Clusters, Elbow rule and Silhouette Coefficient score, Limitations of KMeans, Using Clustering for Image Segmentation and Preprocessing such as Dimensionality Reduction and few more Topics related to the same from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have presented the Implementation of Clustering Algorithms for Image Segmentation and Preprocessing along with Visualizations using Python here in the Snapshots. I hope you will spend some time working on the same and reading the Topics and Book mentioned above. Excited about the days ahead!!
Book:
Hands On Machine Learning with Scikit Learn, Keras and TensorFlow
Day26 of 300DaysOfData!
Gaussian Mixtures Model: A Gaussian Mixture Model or GMM is a probabilistic Model that assumes that the instances were generated from the mixture of several Gaussian distributions whose parameters are unknown. All the instances generated from a single Gaussian Distributions form a cluster that typically looks like an Ellipsoid. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about using Clustering Algorithms for Semi Supervised Learning, Active Learning and Uncertainty Sampling, DBSCAN, Agglomerative Clustering, Birch Algorithms, Mean Shift and Affinity Propagation Algorithms, Spectral Clustering, Gaussian Mixtures Model, Expectation Maximization Algorithm and few more Topics related to the same from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have presented the Implementation of Clustering Algorithms for Semi supervised Learning and DBSCAN along with Visualizations using Python here in the Snapshots. I hope you will spend some time working on the same and reading the Topics and Book mentioned above. Excited about the days ahead!!
Book:
Hands On Machine Learning with Scikit Learn, Keras and TensorFlow
Day27 of 300DaysOfData!
Anomaly Detection: Anomaly Detection also called Outlier Detection is the task of detecting instances that deviate strongly from the norm. These instances are called anomalies or outliers while the normal instances are called inliers. It is useful in Fraud Detection and more. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Gaussian Mixture Models, Anomaly Detection using Gaussian Mixtures, Novelty Detection, Selecting the Number of Clusters, Bayesian Information Criterion, Akaike Information Criterion, Likelihood Function, Bayesian Gaussian Mixture Models, Fast MCD, Isolation Forest, Local Outlier Factor, One Class SVM and few more Topics related to the same from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have just started Neural Networks and Deep Learning from this Book. I have presented the Implementation of Gaussian Mixture Model along with Visualizations using Python here in the Snapshots. I hope you will spend some time working on the same and reading the Topics and Book mentioned above. Excited about the days ahead!!
Book:
Hands On Machine Learning with Scikit Learn, Keras and TensorFlow
Day28 of 300DaysOfData!
Rectified Linear Unit Function or ReLU : It is a continuous but not differentiable at 0 where the slope changes abruptly and makes the Gradient Descent bounce around. It works very well and has the advantage of fast to compute. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Introduction to Artificial Neural Networks with Keras, Biological Neurons, Logical Computations with Neurons, The Perceptron, Hebbian Learning, Multi Layer Perceptron and Backpropagation, Gradient Descent, Hyperbolic Tangent Function and Rectified Linear Unit Function, Regression MLPs, Classification MLPs, Softmax Activation and few more Topics related to the same from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have presented the Implementation of Building an Image Classifier using the Sequential API along with Visualization using Keras here in the
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