GIT Solutions Pvt Ltd is a comprehensive repository for online, offline courses offering high quality state-of-the-art IT and business related training and courses. GIT commenced its IT Education & training business and has trained over thousands of students. GIT is an ISO 9001:2008 certified training institute with its presence in and around Andhra Pradesh and Telangana



  • About
  • Duration

SAS (Statistical Analysis System) is a software suite used for advanced analytics, business intelligence, and data management. Widely employed in various industries, SAS enables users to analyze, visualize, and interpret complex data sets. It offers a comprehensive range of statistical and machine learning tools for predictive modeling and decision-making. SAS is known for its reliability, scalability, and versatility, making it a preferred choice for organizations seeking to extract meaningful insights from their data. The software suite encompasses modules for data integration, statistical analysis, and reporting, contributing to a holistic approach in data-driven decision support.

Length :  45 Hours

Course Content

  • Introduction to Machine Learning:
  • Definition and types of machine learning.
    Overview of supervised, unsupervised, and reinforcement learning.
  • Mathematics and Statistics for Machine Learning:
  • Linear algebra, calculus, and probability concepts.
    Statistical methods for data analysis.
  • Data Preprocessing:
  • Handling missing data and outliers.
    Feature scaling and normalization.
    Data encoding and transformation.
  • Supervised Learning:
  • Regression: Linear regression, polynomial regression.
    Classification: Logistic regression, decision trees, support vector machines.
    Model evaluation and hyperparameter tuning.
  • Unsupervised Learning:
  • Clustering: K-means, hierarchical clustering.
    Dimensionality reduction: Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE).
  • Neural Networks and Deep Learning:
  • Introduction to artificial neural networks.
    Building and training deep neural networks using frameworks like TensorFlow or PyTorch


  • Natural Language Processing (NLP):
  • Text processing and analysis.
    Building NLP models for tasks like sentiment analysis and named entity recognition.
  • Reinforcement Learning:
  • Basics of reinforcement learning.
    Markov Decision Processes (MDPs), Q-learning, and policy gradient methods.
  • Model Deployment and Serving:
  • Deploying machine learning models in real-world applications.
    Integration with web services and APIs.
  • Ethical Considerations and Bias in Machine Learning:
  • Understanding ethical implications of machine learning.
    Addressing bias and fairness issues in model development.
  • Practical Projects:
  • Hands-on projects to apply learned concepts.
    Collaborative work on real-world datasets.
  • Case Studies and Industry Applications:
  • Exploring real-world applications of machine learning in various industries.
    Analyzing case studies to understand challenges and solutions.