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Course Outline

Introduction to Python

  • Variables, tuples, and lists
  • Loops and control statements
  • Modules and imports

Development Environment Setup

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

Data Vectorization with Numpy

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

Data Analysis with Pandas

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

Data Visualization

  • Creating plots with matplotlib
  • Using matplotlib within Pandas
  • Designing high-quality visualizations
  • Visualizing data in Jupyter notebooks
  • Exploring other Python visualization libraries

Using Scikit-learn (Sklearn)

  • Constructing Supervised Learning Models
  • Developing Classification Models
  • Model training and evaluation
  • Visualizing results
  • Calculating and plotting confusion matrices

Introduction to Deep Learning with Keras and TensorFlow

  • Installing TensorFlow and Keras
  • Understanding Neural Networks
  • Building and training Artificial Neural Networks (ANN)
  • Introduction to Convolutional Neural Networks (CNN)
  • Building and training image classifiers using CNNs
  • Training and evaluating Deep Learning Models

Requirements

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

 14 Hours

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