Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition, and machine vision.

Machine learning (ML) is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.

Deep Learning (DP) is a type of machine learning based on artificial neural networks in which multiple layers of processing are used to extract progressively higher-level features from data.

The Learning Process

There are five steps in the Learning Process. The first two steps are shared with the Data Science methodology, which are Data Gathering and Data Cleaning. Feature Extraction, Model Training, and Prediction are specific tasks for ML/DP Projects.

Data Gathering

Data Gathering

This Machine Learning process is based on the first step of the Data Science step. And just like the name states, it is simply the step where we obtain all available data needed from various data sources.

Data Cleaning

The real-world data is not perfect, sometimes the data is mistakenly placed, missing, or invalid. Data cleaning is the process of identifying and removing (or correcting) inaccurate records from a dataset, table, or database and refers to recognizing unfinished, unreliable, inaccurate, or non-relevant parts of the data and then restoring, remodeling, or removing the dirty or crude data.

Data cleaning techniques may be performed as batch processing through scripting or interactively with data cleansing tools.

Data Cleaning
Feature Extraction

Feature Extraction

Feature extraction is a technique used to reduce a large input data set into relevant features. This is done with dimensionality reduction to transform large input data into smaller, meaningful groups for processing.

This step also involves Feature Engineering, in which you use the domain knowledge of the data to transform it into the features which would improve the accuracy of your Machine Learning model.

Model Training

This is the main step in which the Machine Learning model is actually built by using a particular algorithm and inputting training data from the previous step. Depending upon the size of the data, the type of algorithm used, and/or the hardware on which it is run, this step can take anywhere from a few minutes to hours to learn the model.

Data Visualization


The final step of the pipeline is to evaluate the performance of the model you just trained. If the performance of the model does not meet the acceptance criteria, then the model needs to be retrained again with the updated information. Model Training and Predictions steps often have to be repeated back and forth multiple times before a good enough model can be trained.

We utilize open-source, proprietary, and licensed libraries to help you achieve your objectives. We determine the ideal solution for your requirements.

Matlab Azure Machine Learning AWS Machine Learning Google Cloud Machine Learning Python Tensor Flow Scikit Learn Keras SciPy NumPy Matplotlib Pandas

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