Data science & Machine Learning

This course provides a comprehensive introduction to data science and machine learning using Python. Students will learn the foundational concepts of data science, including data collection, cleaning, and preparation, as well as key Python libraries like Pandas, NumPy, and Matplotlib. The course emphasizes hands-on experience, where students can explore real-world datasets and engage in practical exercises to understand data wrangling, preprocessing, and exploratory data analysis (EDA). These essential skills lay the groundwork for advanced topics like machine learning and deep learning.

As the course progresses, students will delve into machine learning techniques, including supervised and unsupervised learning algorithms. They will gain proficiency in building predictive models using the Scikit-learn library and learn to evaluate and fine-tune model performance. Additionally, students will explore time series analysis and deep learning, including neural networks, to understand how these technologies are transforming industries. By the end of the course, students will have developed a strong foundation in data science and machine learning, preparing them to apply these skills in real-world data-driven projects and pursue further career opportunities in the field.

Upon completion of the course, learners are able to

1. Develop a foundational understanding of data science and machine learning concepts.
2. Gain proficiency in Python programming for data science tasks.
3. Explore data preprocessing, cleansing, and evaluation techniques.
4. Master the use of core Python data science libraries and tools, including Scikit-Learn.
5. Build and evaluate machine learning models for practical applications.
6. Acquire hands-on experience with real-world data science projects.

• Professionals interested in transitioning into data science or machine learning roles.
• Data analysts and statisticians looking to enhance their technical skillset with Python.
• IT professionals and software developers keen on exploring data science applications.
• Students and fresh graduates seeking a foundational understanding of machine learning.
• Business leaders or decision-makers who wish to understand data-driven strategies.
• Enthusiasts who want to learn Python-based data science techniques.

Topic 1: Foundations of Data Science

Topic 2: Data Wrangling & Preparation

Topic 3: Exploratory Data Analysis (EDA)

Topic 4: Machine Learning Fundamentals

Topic 5: Supervised Learning

Topic 6: Unsupervised Learning Algorithms

Topic 7: Time Series Analysis

Topic 8: Demystifying Deep Learning and Neural Networks