Practical Machine Learning for Data Scientists

Dive into the world of Artificial Intelligence (AI) and Machine Learning (ML) with this comprehensive course designed for aspiring and experienced Data Scientists and Machine Learning Engineers. Gain a solid foundation in the core concepts and practical applications of these transformative technologies.

This course begins by clarifying the distinctions between AI, ML, Deep Learning, and their interconnectedness within the broader field of Data Science. Understand the roles and responsibilities within an AI team and learn how to effectively communicate with stakeholders. We'll explore the realistic possibilities and limitations of AI projects, and define the key elements of a successful AI project.

You'll master the fundamentals of supervised learning, including the essential components of a machine learning problem and various solution approaches. We'll delve into both linear models (such as Linear Regression, Logistic Regression, and Support Vector Machines (SVM)) and non-linear models (including Polynomial Regression, Kernel SVM, and Deep Neural Networks (DNN)).

Learn a universal approach to tackling any machine learning problem systematically. This includes crucial steps like data preparation, Exploratory Data Analysis (EDA), model selection, model evaluation, model design, fine-tuning, and regularization. An end-to-end example, complete with code in accessible Google Colab Notebooks, will demonstrate this process in action.

Explore powerful Machine Learning Meta-algorithms and Ensemble Methods, including Voting, Bagging, Boosting Decision Trees, and Random Forests.

Finally, unlock the potential of unsupervised learning. We'll cover essential dimensionality reduction algorithms like Manifold Learning (including Locally Linear Embedding (LLE)) and Projection methods (such as Principal Component Analysis (PCA)), as well as clustering techniques like K-Means.

Throughout the course, you'll gain hands-on experience using Python and popular machine learning libraries, including scikit-learn, pandas, and Keras. Develop the skills to build and deploy impactful AI and ML solutions.