AI and ML Solutions with Python: Deep Learning and Neural Network Implementation

AI and ML Solutions with Python: Deep Learning and Neural Network Implementation

• implement recurrent neural network
• work with data sampling
• implement dimensionality reduction with PCA
• demonstrate how to use the Gaussian processes for regression
• describe the core concepts and features of Linear model
• identify the pre-model and post-model workflow in analytics
• work with Classification and Bayesian Ridge regression using scikit-learn
• describe the core concept of Linear Regression model
• demonstrate how to implement Logistic regression using linear methods

Discover how to implement neural network with data sampling and workflow models using scikit-learn, and explore the pre and post model approaches of implementing machine learning workflows.

AI and ML Solutions with Python: Implementing ML Algorithm Using scikit-learn

AI and ML Solutions with Python: Implementing ML Algorithm Using scikit-learn

• work with least absolute shrinkage and selection operator
• demonstrate how to apply Bayesian Ridge regression using scikit-learn
• describe data classification using scikit-learn
• implement classifications with decision trees using scikit-learn
• demonstrate how to work with data classification using vector machines in scikit-learn
• demonstrate how to classify documents with Naive Bayes using scikit-learn
• work with Post model validation using the Cross model algorithm
• demonstrate how to work with cross model implementation using Shufflesplit
• implement poor man's grid search and brute force grid search

Discover how to implement data classification using various techniques, including Bayesian, and learn to apply various search implementations with Python and scikit-learn.

AI and ML Solutions with Python: Implementing Robotic Process Automation

AI and ML Solutions with Python: Implementing Robotic Process Automation

• demonstrate how to create fake estimator to compare results
• recognize the various capabilities and features of RPA
• identify the various prominent RPA frameworks that are being implemented today
• demonstrate how to implement pattern matching with Regular expressions in python
• demonstrate how to schedule tasks and launch programs using Python
• demonstrate how to manipulate images and automate image manipulation
• demonstrate how to automate CSV and JSON file operations
• identify the essential RPA features and capabilities provided by UiPath
• implement RPA using the various features and capabilities of UiPath

Discover how to implement Robotic Process Automation (RPA) using Python, and explore various RPA frameworks with the practical implementation of UiPath.

AI and ML Solutions with Python: Machine Learning and Data Analytics

AI and ML Solutions with Python: Machine Learning and Data Analytics

• describe the core concepts of machine learning
• identify the critical features and comparable features of machine learning and deep learning
• recognize the correlation and comparable features of machine learning and AI
• set up the development environment for machine learning using Python
• list the various types and techniques of analytics
• identify the essential benefits of predictive and descriptive analytics
• define the various data metrics that are used to quantify the data for analytics
• classify the various algorithms used in supervised learning
• demonstrate how to implement regression algorithm

Explore critical machine learning (ML) and deep learning concepts and the various categorizations of algorithms and their implementations using Python.

AI and ML Solutions with Python: Supervised, Unsupervised and Deep Learning

AI and ML Solutions with Python: Supervised, Unsupervised and Deep Learning

• demonstrate how to implement classification
• list the various types of algorithms used in unsupervised learning
• demonstrate how to implement K-Mean clustering
• demonstrate how to implement hierarchical clustering
• demonstrate how to facilitate text mining and work with recommender systems
• demonstrate the process involved in text mining and data assembly
• specify the concepts of deep and reinforcement learning
• work with Restricted Boltzmann machines
• build models using Convolution Neural Network

Discover how to implement various supervised and unsupervised algorithms of machine learning using Python, with the primary focus of clustering and classification.