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Top Machine Learning Companies in Houston


4 Companies

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Cybersoft North America Inc

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The Company was formed as a virtual startup in 1996 as a spin-off from Applied Research Technologies, Incorporated. Over the years, Cybersoft has established itself as a trusted, quality provider of Software Solutions & tools for multiple markets such as Artificial Intelligence (Machine Learning, Operationalization of models), Financial Services (Account Receivable Management), Enterprise Workflow based Web Applications, internet of things (IoT) and cross-platform Mobile Applications. Cybersoft takes pride in its unique, prototyped product SCRUM-based development which ensures architecting broad-range solutions. We have been developing highly scalable and comprehensive software products for over 26 years. Our methodology results in minimal defects in the post-deployment phase and higherRead More

  • dollar

    $25-49/hr

  • user

    50 to 99

  • calender

    1996

  • location

    United States (USA)

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Odyssey Analytics

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Odyssey Analytics stands out as a premier software consulting firm specializing in artificial intelligence (AI). As industry leaders in AI, we are dedicated to catalyzing digital transformation and technology modernization for our clients. Our expert team harnesses the power of AI to craft bespoke solutions that elevate businesses to new heights. From machine learning algorithms to predictive analytics, we deploy cutting-edge technologies tailored to meet unique industry challenges. At Odyssey Analytics, we redefine possibilities through the strategic application of AI, ensuring our clients stay ahead in the competitive landscape. Collaborate with us to unlock the full potential of AI and propel your business into the future.

  • dollar

    $50-99/hr

  • user

    50 to 249

  • calender

    2019

  • location

    United States (USA)

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Jindal Healthcare

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Jindal Healthcare is a leading name in Revenue Cycle Management backed by the $100 Billion OP Jindal Group. With over two decades of expertise in business process outsourcing, we specialize in maximizing healthcare providers' revenues so they can reinvest in and focus on elevating patient care standards. Our approach integrates cutting-edge HealthX AI technology with human insight to drive optimization, automation, and process enhancement. We offer a comprehensive suite of services from End-to-End Revenue Cycle Management to Practice Management Solutions, Prior Authorization support, Coding Management, and Analytics. Experience unparalleled revenue growth, cost reduction, and operational efficiency with Jindal Healthcare - your trusted partner in healthcare revenue optimization

  • dollar

    $50-99/hr

  • user

    1000+

  • calender

    2010

  • location

    United States (USA)

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Kipp Technologies

No Reviews

Kipp Technologies is a USA-based software innovation company dedicated to transforming businesses through advanced digital solutions. We collaborate with startups, growing enterprises, and global organizations to develop cutting-edge technologies that drive efficiency, scalability, and long-term success.

  • dollar

    $25-49/hr

  • user

    2 to 9

  • calender

    2024

  • location

    United States (USA)

Frequently Asked Questions

Machine Learning is a subset of Artificial Intelligence that focuses on creating algorithms and statistical models that enable computer systems to improve their performance on a specific task through experience, without being explicitly programmed. It's a key component of AI that allows systems to learn from data and make predictions or decisions.

Machine Learning can provide more adaptive and intelligent solutions compared to traditional software. It can analyze large amounts of data to identify patterns and make predictions, automate complex tasks, personalize user experiences, and continuously improve its performance over time. This can lead to increased efficiency, better decision-making, and new insights for your business.

The main types of Machine Learning are supervised learning, unsupervised learning, and reinforcement learning. Supervised learning uses labeled data to train models, unsupervised learning finds patterns in unlabeled data, and reinforcement learning trains models through a reward-based system. Each type is suited for different kinds of problems and datasets.

Machine Learning has diverse applications across industries. In healthcare, it's used for disease prediction and medical image analysis. In finance, it's applied for fraud detection and algorithmic trading. In retail, it enables personalized recommendations and demand forecasting. Other applications include autonomous vehicles, natural language processing, and predictive maintenance in manufacturing.

Some challenges include the need for large amounts of quality data, potential bias in algorithms, the "black box" nature of some models making decisions difficult to interpret, high computational requirements, and the need for skilled professionals. Additionally, there may be privacy concerns when dealing with sensitive data.

Machine Learning is a broader field that includes various algorithms and statistical methods. Deep Learning is a subset of Machine Learning that uses artificial neural networks with multiple layers (deep neural networks) to model and process complex patterns in data. Deep Learning is particularly effective for tasks like image and speech recognition but often requires more data and computational resources than other Machine Learning methods.

Implementing Machine Learning solutions typically requires a mix of skills including programming (often in languages like Python or R), statistics and mathematics, data analysis, and domain-specific knowledge. Familiarity with Machine Learning algorithms, data preprocessing techniques, and tools like TensorFlow or scikit-learn is also important. Additionally, skills in data visualization and communication are valuable for presenting results to non-technical stakeholders.

Companies new to Machine Learning can start by identifying specific business problems that could benefit from data-driven solutions. They can then explore available datasets, consider hiring or training staff in Machine Learning, or partnering with specialized consultants or service providers. Starting with small, well-defined projects can help build experience and demonstrate value before scaling to larger initiatives.

Ethical considerations in Machine Learning include ensuring data privacy and security, addressing potential biases in training data or algorithms, maintaining transparency in decision-making processes, and considering the societal impact of automated systems. It's important to have clear governance structures and to regularly audit Machine Learning systems for fairness and accuracy.

The cost of Machine Learning solutions can vary widely depending on the complexity of the problem, the amount of data involved, and the required infrastructure. While initial costs may be higher due to the need for specialized skills and potentially more powerful hardware, Machine Learning solutions can often provide long-term cost savings through increased efficiency and automation. However, ongoing costs for data management, model updating, and maintenance should also be considered.

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