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Course Outline

Introduction to Python

  • Variables, Tuples, and Lists
  • Loops and control statements
  • Modules and imports

Setting Up the Development Environment

  • Installing Python
  • Installing Jupyter
  • Installing Python modules via Pip

Vectorizing Data with Numpy

  • Creating Numpy arrays
  • Common matrix operations
  • Utilizing ufuncs
  • Views and broadcasting on Numpy arrays
  • Optimizing performance by minimizing loops
  • Performance optimization using cProfile

Data Analysis with Pandas

  • Data cleaning
  • Leveraging vectorized data in Pandas
  • Data wrangling
  • Sorting and filtering data
  • Aggregate operations
  • Analyzing time series

Data Visualization

  • Plotting charts with Matplotlib
  • Using Matplotlib within Pandas
  • Creating high-quality charts
  • Visualizing data in Jupyter Notebooks
  • Exploring other Python visualization libraries

Utilizing Scikit-learn

  • Developing Supervised Learning Models
  • Constructing Classification Models
  • Model training and evaluation
  • Visualizing results
  • Calculation and plotting of the Confusion Matrix

Introduction to Deep Learning with Keras and TensorFlow

  • Installing TensorFlow and Keras
  • Overview of Neural Networks
  • Building and Training Artificial Neural Networks (ANN)
  • Overview of Convolutional Neural Networks (CNN)
  • Building and Training Image Classifiers using CNN
  • Training and evaluating Deep Learning models

Requirements

Participation in this course is strictly limited to those who attended the "Python and Data Visualization" session with Ahmed on February 11, 2021.

 14 Hours

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