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Data Science & Machine Learning

Course Category

Data Science & Machine Learning

This course introduces data science fundamentals, covering data collection, cleaning, and visualization. Learners will explore machine learning algorithms including regression, classification, clustering, and neural networks. Hands-on projects with Python and libraries such as Pandas, Scikit-learn, and TensorFlow build practical skills. The program emphasizes model evaluation, optimization, and deployment in real-world scenarios. Students will gain experience with big data tools and cloud-based platforms for scalable analysis. Ethical considerations in AI and data use are highlighted to ensure responsible practice. By completion, participants will be equipped to design, train, and deploy intelligent systems using data-driven insights.

4 Courses
Certificate Included
Lifetime Access

What You'll Learn

1
Foundations (Python, Data Analysis & SQL)
  • Python Basics & Structures
    Learn how Python supports multiple paradigms, including object-oriented and functional programming, making it highly versatile for web development, data science, and automation.
  • Numerical Computing & Pandas
    Learn the use of computers to solve mathematical problems approximately using numerical methods, rather than symbolic manipulation through Numerical Computing. Also learn data manipulation, cleaning, and analysis through Pandas
  • Visualization & SQL
    Learn components of data analysis that work together to turn raw data into actionable insights
  • Advanced SQL & EDA Project
    Learn and showcase your ability to transform raw, complex datasets into actionable business insights using high-level database techniques.
2
Statistics and ML Fundamentals
  • Stats & Probability
    Learn the mathematical tools used to quantify uncertainty and analyze data.
  • Regression & Time Series
    Learn how to understand how variables (x) influence a target variable (y). and how to forecast future values based on previous trends.
  • Tree-Based Models & SVM
    Learn two of the most popular supervised learning techniques used for both classification and regression tasks.
  • Unsupervised & Tuning
    Learn about two distinct, often sequential, steps in developing artificial intelligence models.
3
Deep Learning & NLP
  • Neural Networks & Keras
    Learn computational models using layers of connected nodes to process data, detect patterns, and make predictions by adjusting weights through training.
  • Computer Vision
    Learn how to enable computers to "see" and understand the visual world.
  • Transformers & PyTorch
    Learn about a powerful duo in modern AI, where one provides the innovative "architecture" and the other provides the "tools" to build it.
  • Optimization & Project
    Learn how to adjust a model's internal parameters (weights and biases) to minimize error and improve performance.
4
MLOps, Bag Data & Capstone
  • Big Data & Deployment
    Learn the strategic process of moving analytics models, AI/ML tools, and data processing workflows from a test environment into a live, production setting to generate actionable, real-time insights.
  • Cloud & GenAI
    Learn about the on-demand delivery of IT resources (servers, storage, databases) over the internet, and machine learning models to create new, original contents such as text, images, code, or audio, based on patterns learned from vast datasets.
  • Ethics & Capstone Build
    The final, comprehensive academic endeavor where candidates apply theoretical knowledge to solve real-world problems while integrating moral reasoning and ethical decision-making frameworks
  • Career & Graduation
    End of Program
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