HCTPL

Data Science Training

“Start with foundational concepts in data science, including statistics, mathematics, data manipulation, and programming languages such as Python or Introduce machine learning algorithms, techniques for model training and evaluation, and practical applications in areas like classification, regression, clustering, and anomaly detection.”

  • Definition and applications of data science
  • Importance of data science across various industries
  • Fundamental principles of machine learning
  • Supervised, unsupervised, and reinforcement learning paradigms
  • Supervised Learning: Algorithms learn from labeled data for prediction and decision-making.
  • Unsupervised Learning: Algorithms discover patterns and structures within unlabeled data.
  • Semi-supervised Learning: Algorithms leverage both labeled and unlabeled data for learning.
  • Reinforcement Learning: Agents learn through trial and error to interact with an environment and achieve goals.

   – Reinforcement Learning: Agents learn to interact with an environment to achieve a goal through trial and error.

  • Importance of data preprocessing in machine learning
  • Techniques for cleaning, normalizing, and transforming data:
    • Handling missing values and outliers
    • Addressing imbalanced datasets
  • Feature selection, extraction, and transformation from raw data
  • Creating new features and encoding categorical variables
  • Dimensionality reduction techniques: Principal Component Analysis (PCA) and feature scaling

 

  • Exploration of various machine learning models and algorithms:
    • Regression: Linear, polynomial, ridge, and lasso regression
    • Classification: Logistic regression, decision trees, random forests, support vector machines (SVM), k-nearest neighbors (k-NN)
    • Clustering: K-means, hierarchical, and density-based clustering
    • Neural Networks: Feedforward, convolutional (CNN), and recurrent (RNN) networks
  • Selecting appropriate models based on task types
  • Model performance evaluation metrics: Accuracy, precision, recall, F1-score
  • Exploration of various machine learning models and algorithms:
    • Regression: Linear, polynomial, ridge, and lasso regression
    • Classification: Logistic regression, decision trees, random forests, support vector machines (SVM), k-nearest neighbors (k-NN)
    • Clustering: K-means, hierarchical, and density-based clustering
    • Neural Networks: Feedforward, convolutional (CNN), and recurrent (RNN) networks
  • Selecting appropriate models based on task types
  • Model performance evaluation metrics: Accuracy, precision, recall, F1-score
  • Best practices for deploying models into production environments
  • Packaging models and creating APIs for model inference
  • Real-time model performance monitoring
  • Ethical considerations and responsible AI practices
  • Applying acquired knowledge and skills to real-world data science projects and case studies
  • Hands-on exercises and projects tackling problems in:
    • Healthcare
    • Finance
    • E-commerce
    • Natural language processing

24/7 Customer support

Contact us

- Quick Submission -

Contact us

+91 9014531029

Refund Policy

At Hari Cornucopia Tech Private Limited, we prioritize customer satisfaction. Therefore, we offer a refund policy to ensure that participants have peace of mind when enrolling in our courses. If a participant is dissatisfied after the first class, they are eligible for a refund. However, once they attend the second class, the refund policy becomes void.