Learn Data Science, Data Analysis, Machine Learning (Artificial Intelligence) and Python with Tensorflow, Pandas & more!
InstructorAndrei Neagoie
Instruction utilizes a work simulation style, framing lessons around professional scenarios to maintain focus and practical relevance.
Editorial course preview
These 4 complementary views highlight concrete, legible examples from the course presentation.
This slide outlines the course structure with a six-step iterative process diagram covering problem definition, data, evaluation, features, modelling, and experiments.
This image shows a Jupyter Notebook cell where Python code is used to add a 'Number of wheels' column to a car sales DataFrame, demonstrating practical data manipulation.
This screenshot shows a Jupyter Notebook output featuring a correlation heatmap matrix generated by Python, illustrating data visualization techniques used in machine learning analysis.
The Machine Learning Playground interface displays a visualization of K Nearest Neighbors classification with distinct decision boundaries separating two classes.
The curriculum implements a specific 6-step framework to guide model selection and implementation throughout the training.
One signal from a pre-update review suggests that outdated API versions or missing Google Colab files can disrupt progress in specific modules like TensorFlow; while the current update label may have addressed this, it does not guarantee a correction.
A pre-update review indicates the curriculum lacks significant coverage of unsupervised learning and model deployment, which may limit its utility as a complete program; the recent update label does not confirm these topics have been added.
The curriculum aligns well with the goal of providing a foundation for those starting from zero or transitioning into data science.