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Supervised, Unsupervised & Deep Learning: Exercise: Working with Data Frames and Centroids
After watching this video, you will be able to utilize data frames and centroids.
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Supervised, Unsupervised & Deep Learning: Restricted Boltzmann
After watching this video, you will be able to work with Restricted Boltzmann machines.
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Supervised, Unsupervised & Deep Learning: Working with CNN
After watching this video, you will be able to build models using Convolution Neural Network.
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Supervised, Unsupervised & Deep Learning: Text Mining and Data Assembly
After watching this video, you will be able to demonstrate the process involved in text mining and data assembly.
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Supervised, Unsupervised & Deep Learning: Deep and Reinforcement Learning Concepts
After watching this video, you will be able to specify the concepts of deep and reinforcement learning.
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Supervised, Unsupervised & Deep Learning: Hierarchical Clustering
After watching this video, you will be able to demonstrate how to implement hierarchical clustering.
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Supervised, Unsupervised & Deep Learning: Text Mining and Recommender Systems
After watching this video, you will be able to demonstrate how to facilitate text mining and work with recommender systems.
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Deep Learning & Neural Network Implementation: Applying PCA
After watching this video, you will be able to implement dimensionality reduction with PCA.
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Deep Learning & Neural Network Implementation: Gaussian Regression Process
After watching this video, you will be able to demonstrate how to use the Gaussian processes for regression.
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Deep Learning & Neural Network Implementation: Recurrent Neural Network
After watching this video, you will be able to implement recurrent neural network.
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Deep Learning & Neural Network Implementation: Data Sampling
After watching this video, you will be able to work with data sampling.
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Deep Learning & Neural Network Implementation: Logistic Regression Using Linear Method
After watching this video, you will be able to demonstrate how to implement Logistic regression using linear methods.
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Deep Learning & Neural Network Implementation: Exercise: Working with Linear Regression
After watching this video, you will be able to create and fit linear regression on a dataset and get the feature coefficient.
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Deep Learning & Neural Network Implementation: Classification and Bayesian Ridge
After watching this video, you will be able to work with Classification and Bayesian Ridge regression using scikit-learn.
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Deep Learning & Neural Network Implementation: Linear Regression Modelling
After watching this video, you will be able to describe the core concept of Linear Regression model.
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Deep Learning & Neural Network Implementation: Linear Model
After watching this video, you will be able to describe the core concepts and features of Linear model.
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Deep Learning & Neural Network Implementation: Pre-Model and Workflow
After watching this video, you will be able to identify the pre-model and post-model workflow in analytics.
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Implementing ML Algorithm Using scikit-learn: Decision Tree Classification
After watching this video, you will be able to implement classifications with decision trees using scikit-learn.
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Implementing ML Algorithm Using scikit-learn: Vector Machine Using scikit-learn
After watching this video, you will be able to demonstrate how to work with data classification using vector machines in scikit-learn.
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Implementing ML Algorithm Using scikit-learn: Bayesian Ridge Regression Using scikit-learn
After watching this video, you will be able to demonstrate how to apply Bayesian Ridge regression using scikit-learn.
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Implementing ML Algorithm Using scikit-learn: Data Classification
After watching this video, you will be able to describe data classification using scikit-learn.
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Implementing ML Algorithm Using scikit-learn: Least Absolute Shrinkage
After watching this video, you will be able to work with least absolute shrinkage and selection operator.
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Implementing ML Algorithm Using scikit-learn: Exercise: Working with Decision Tree Classifiers
After watching this video, you will be able to create labels and features to classify data into train and test datasets and apply decision tree classifiers.
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Implementing ML Algorithm Using scikit-learn: Using Shufflesplit
After watching this video, you will be able to demonstrate how to work with cross model implementation using Shufflesplit.
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Implementing ML Algorithm Using scikit-learn: Brute Force Grid Search
After watching this video, you will be able to implement poor man's grid search and brute force grid search.
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Implementing ML Algorithm Using scikit-learn: Document Classification and Naive Bayes
After watching this video, you will be able to demonstrate how to classify documents with Naive Bayes using scikit-learn.
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Implementing ML Algorithm Using scikit-learn: Post Model Validation
After watching this video, you will be able to work with Post model validation using the Cross model algorithm.
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Implementing Robotic Process Automation: Task Scheduler and Program Auto Launch
After watching this video, you will be able to demonstrate how to schedule tasks and launch programs using Python.
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Implementing Robotic Process Automation: Manipulate Images
After watching this video, you will be able to demonstrate how to manipulate images and automate image manipulation.
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Implementing Robotic Process Automation: RPA Frameworks
After watching this video, you will be able to identify the various prominent RPA frameworks that are being implemented today.
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Implementing Robotic Process Automation: Implement Pattern Matching Using Python
After watching this video, you will be able to demonstrate how to implement pattern matching with Regular expressions in python.
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Implementing Robotic Process Automation: Fake Estimator
After watching this video, you will be able to demonstrate how to create fake estimator to compare results.
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Implementing Robotic Process Automation: Introducing Robotic Process Automation
After watching this video, you will be able to recognize the various capabilities and features of RPA.
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Implementing Robotic Process Automation: Implement RPA using UiPath
After watching this video, you will be able to implement RPA using the various features and capabilities of UiPath.
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Implementing Robotic Process Automation: Exercise: Working with Image Filters
After watching this video, you will be able to read image from an image file, filter image, and save as a new file.
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Implementing Robotic Process Automation: File Operation Automation
After watching this video, you will be able to demonstrate how to automate CSV and JSON file operations.
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Implementing Robotic Process Automation: UiPath Fundamentals
After watching this video, you will be able to identify the essential RPA features and capabilities provided by UiPath.
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Supervised, Unsupervised & Deep Learning: Unsupervised Learning
After watching this video, you will be able to list the various types of algorithms used in unsupervised learning.
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Supervised, Unsupervised & Deep Learning: K-Mean Clustering
After watching this video, you will be able to demonstrate how to implement K-Mean clustering .
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Supervised, Unsupervised & Deep Learning: Working with Classification
After watching this video, you will be able to demonstrate how to implement classification.
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Machine Learning & Data Analytics: Installing Python for Machine Learning
After watching this video, you will be able to set up the development environment for machine learning using Python.
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Machine Learning & Data Analytics: Analytics Types and Techniques
After watching this video, you will be able to list the various types and techniques of analytics .
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Machine Learning & Data Analytics: Machine Learning and Deep Learning
After watching this video, you will be able to identify the critical features and comparable features of machine learning and deep learning.
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Machine Learning & Data Analytics: Machine Learning and AI Correlation
After watching this video, you will be able to recognize the correlation and comparable features of machine learning and AI.
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Machine Learning & Data Analytics: Machine Learning Concepts
After watching this video, you will be able to describe the core concepts of machine learning.
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Machine Learning & Data Analytics: Exercise: Working with Data Frames
After watching this video, you will be able to load data set, create data frames, and print the shape of data frames.
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Machine Learning & Data Analytics: Supervised Learning Algorithm
After watching this video, you will be able to classify the various algorithms used in supervised learning.
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Machine Learning & Data Analytics: Implementing Regression
After watching this video, you will be able to demonstrate how to implement regression algorithm .
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Machine Learning & Data Analytics: Benefits of Predictive and Descriptive Analytics
After watching this video, you will be able to identify the essential benefits of predictive and descriptive analytics.
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Machine Learning & Data Analytics: Nominal, Ordinal, Interval, and Ratio Data Metrics
After watching this video, you will be able to define the various data metrics that are used to quantify the data for analytics.
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Machine Learning Implementation: Logistics Regression
After watching this video, you will be able to illustrate the implementation of logistic regression using Java.
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Machine Learning Implementation: Probabilistic Classifier
After watching this video, you will be able to recognize the usage and objective of probabilistic classifiers for statistical classification.
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Neural Network & Neuroph Framework: Hyperparameter
After watching this video, you will be able to demonstrate how to work with hyperparameters in neural networks.
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Neural Network & Neuroph Framework: Neuroph Java Neural Framework Capabilities
After watching this video, you will be able to recall the capabilities and practical implementation of Neuroph framework.
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Neural Network & Neuroph Framework: Hyperparameter Implementation using DL4J
After watching this video, you will be able to work with the Arbiter hyperparameter optimization library designed to automate hyperparameter.
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Neural Network & Neuroph Framework: Deep Learning
After watching this video, you will be able to describe the concept of the deep learning and list its various components.
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Neural Network & Neuroph Framework: Comparing Deep Learning and Graph Models
After watching this video, you will be able to recognize the similarities and differences between deep learning and graph model.
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Neural Network & Neuroph Framework: Combining Deep Learning and Graph Model
After watching this video, you will be able to work with the collaboration of deep learning and graph model.
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Neural Network & NLP Implementation: Classifying Text and Documents
After watching this video, you will be able to classify text and documents using the NLP model.
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Neural Network & NLP Implementation: Using Parser to Extract Relationships
After watching this video, you will be able to Illustrate the relationships, extraction and dependencies using parser API.
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Expert Systems & Reinforcement Learning: Clustering Concept
After watching this video, you will be able to recognize the clustering implementation algorithms and illustrate the validation and evaluation techniques.
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Expert Systems & Reinforcement Learning: Hierarchical Clustering
After watching this video, you will be able to implement hierarchical clustering using the top down approach with Java.
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Machine Learning Implementation: Linear Regression Analysis
After watching this video, you will be able to recall how to use and work with linear regression analysis.
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Machine Learning Implementation: Gradient Boosting Algorithms
After watching this video, you will be able to implement gradient boosting algorithms using Java.
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Machine Learning Implementation: KNN Algorithms
After watching this video, you will be able to describe how to implement KNN algorithms.
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Machine Learning Implementation: Decision Tree and Random Forest
After watching this video, you will be able to implement decision tree and random forest.
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Machine Learning Implementation: Supervised and Unsupervised Learning
After watching this video, you will be able to describe the differences between supervised and unsupervised learning.
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Machine Learning Implementation: K-Means Cluster
After watching this video, you will be able to state how to implement K-Means clusters.
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Machine Learning Implementation: Machine Learning and Artificial Intelligence
After watching this video, you will be able to identify the critical relation between machine learning and artificial intelligence.
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Machine Learning Implementation: Machine Learning Algorithm Types
After watching this video, you will be able to specify the various classifications of machine learning algorithms.
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Machine Learning Implementation: Naïve Bayes Classifier
After watching this video, you will be able to implement Naïve Bayes classifier using Java.
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Machine Learning Implementation: Exercise: Implementing Machine Learning Algorithms
After watching this video, you will be able to demonstrate how to use the K-Mean algorithm in ML applications.
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Neural Network & Neuroph Framework: Neural Network and its Essential Components
After watching this video, you will be able to recognize the concept of neural network, neurons and the different layers of neuron.
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Neural Network & Neuroph Framework: Implementing Activation Functions and Loss Functions
After watching this video, you will be able to implement activation functions and loss functions using DL4J.
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Neural Network & Neuroph Framework: Deep Learning and Graph Model Use Cases
After watching this video, you will be able to identify the relevant use cases for implementing deep learning and graph model.
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Neural Network & Neuroph Framework: Role of Activation Function
After watching this video, you will be able to identify the relevance of activation functions and list the various types of activation functions in neural networks.
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Neural Network & Neuroph Framework: Loss Functions and their Benefits
After watching this video, you will be able to recognize the benefits of loss functions and list the various types of loss functions in practice today.
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Neural Network & Neuroph Framework: Implementing Hopfield Neural Networks
After watching this video, you will be able to Implementing Hopfield Neural Networks.
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Neural Network & Neuroph Framework: Implementing Back Propagation Neural Networks
After watching this video, you will be able to describe how to implement back propagation neural networks using Java.
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Neural Network & Neuroph Framework: Implement a Simple Neural Network
After watching this video, you will be able to describe the practical implementation of a simple neural network using Java.
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Neural Network & Neuroph Framework: Neural Network Types
After watching this video, you will be able to list the various types of neural networks that are prominently used today.
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Neural Network & Neuroph Framework: Exercise: Working with Neuroph and Neural Networks
After watching this video, you will be able to create and modify a Neuroph project using Neural networks.
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Neural Network & NLP Implementation: Multilayer Networks and Computation Graphs
After watching this video, you will be able to describe the essential features of multilayer networks and computation graphs.
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Neural Network & NLP Implementation: Implementing Multilayer Networks
After watching this video, you will be able to describe how to use multilayer networks and computation graphs.
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Neural Network & NLP Implementation: Speech Implementation
After watching this video, you will be able to implement recognizer, synthesizer and translator using Java.
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Neural Network & NLP Implementation: Exercise: Working with NLP Components
After watching this video, you will be able to illustrate how to use NLP components.
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Neural Network & NLP Implementation: Detecting Parts of Speech
After watching this video, you will be able to describe how to detect parts of speech to assign tags to the words and sentences.
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Neural Network & NLP Implementation: Language and Sentence
After watching this video, you will be able to implement language and sentence detector.
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Neural Network & NLP Implementation: Tokenizer and Name Finder
After watching this video, you will be able to describe the utilization of Tokenizer and Name Finder in NLP.
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Neural Network & NLP Implementation: NLP Introduction
After watching this video, you will be able to specify the essential features and important components of NLP.
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Neural Network & NLP Implementation: Components of NLP
After watching this video, you will be able to list the important components of NLP along with their roles and usages.
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Expert Systems & Reinforcement Learning: Working with Jess
After watching this video, you will be able to work with Jess to create rule based expert systems.
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Expert Systems & Reinforcement Learning: Defining Rules
After watching this video, you will be able to describe how to define rules and work with expert system shell using Java.
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Expert Systems & Reinforcement Learning: Expert Systems Tools
After watching this video, you will be able to list the tools, shells, and programming languages that are being used for Expert Systems.
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Expert Systems & Reinforcement Learning: Exercise: Working with Datasets and Clustering
After watching this video, you will be able to demonstrate how to use datasets with clustering.
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Expert Systems & Reinforcement Learning: Principal Component Analysis Data Transformation
After watching this video, you will be able to implement principal component analysis data transformation using Java pca-tranform.
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Expert Systems & Reinforcement Learning: Graph Modeling
After watching this video, you will be able to describe the concept of graph modelling and the various approaches of implementing graphs in machine learning.
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Expert Systems & Reinforcement Learning: Outlier Types
After watching this video, you will be able to identify the various types of Outliers and their impact on the accuracy of the models.
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Expert Systems & Reinforcement Learning: Feature Search and Feature Evaluation Techniques
After watching this video, you will be able to describe the various approaches of feature relevance search and the evaluation techniques.
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Expert Systems & Reinforcement Learning: Supervised Learning and Notations
After watching this video, you will be able to recognize data notations from the perspective of quality, descriptive, and visualization notations.
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Expert Systems & Reinforcement Learning: Datasets and Training Models
After watching this video, you will be able to list the different types of datasets and their utility over the various phases of supervised learning.
