Intermediate Trainings

This page is designed for those who have a foundational understanding of Python and are looking to deepen their knowledge of data science and machine learning. The courses listed below focus on more advanced concepts and techniques, including data cleaning, analysis, visualization, machine learning models, and statistical analysis. Through these courses, you will enhance your skills in applying algorithms like regression, classification, and clustering, and you will explore techniques for analyzing complex datasets and gaining valuable insights.

These intermediate courses will help you build confidence in using tools such as Python, R, Pandas, Matplotlib, NumPy, and scikit-learn to take on real-world data science projects and build powerful machine learning models. Whether you’re looking to advance your career or apply these skills in a specific domain, these resources will provide the knowledge and hands-on experience you need to excel.

  • Machine Learning with Python – IBM (Coursera):
    This course focuses on intermediate-level concepts of machine learning, including algorithms such as regression, classification, and clustering, all using Python. It’s ideal for those who want to build on their Python skills and dive deeper into machine learning.
  • Introduction to Data Science in Python:
    At the end of this training, students will be able to receive, clean, manipulate tabular data and perform basic inferential statistical analyses.
  • Applied Data Science Specialization Pack with Python:
    This skill-based specialization is aimed at students with a basic background in Python or programming, who want to apply techniques in statistics, machine learning, data visualization, text analysis, and social network analysis using popular Python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insights from their data.
  • Python for Data Science Project:
    You will perform specific data science and data analytics tasks, such as data extraction, web scraping, visualizing data, and creating a dashboard. This project will show you your competence to use libraries such as Pandas with Python and Beautiful Soup in a Jupyter Notebook. When completed, you will have an impressive project that you can add to your business portfolio.
  • Vector Database Fundamentals:
    Vector databases are the engines behind AI applications. Companies investing heavily in AI need expertise to build AI-powered technologies such as recommendation engines, search engine information retrieval, machine learning tasks, data analysis, semantic matching, and content generation.
  • Data Analysis with R:
    First, you will learn key techniques for preparing (or cleaning) your data for analysis. Then, you will discover how to better understand your data through exploratory data analysis, which will help you summarize your data and identify relevant relationships between variables that may lead to insights. Once your data is ready for analysis, you will learn how to build your model and how to evaluate and fine-tune its performance. By following this process, you can ensure that your data analysis performs according to the standards you set, and you can trust the results.
  • Python for Data Science – Course for Beginners(Learn Python, Pandas, NumPy, Matplotlib):
    This Python data science course will take you from knowing nothing about Python to coding and analyzing data with Python using tools like Pandas, NumPy, and Matplotlib.