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Course Outline

Introduction to Python

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

Development Environment Installation

  • Installing Python
  • Installing Jupyter
  • Installing Python modules using Pip

Vectorizing Data in Numpy

  • Creating NumPy arrays
  • Common operations on matrices
  • Using universal functions (ufuncs)
  • Views and broadcasting on NumPy arrays
  • Optimizing performance by avoiding loops
  • Optimizing performance with cProfile

Data Analysis with Pandas

  • Data cleaning
  • Utilizing 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 charts
  • Visualizing data in Jupyter notebooks
  • Exploring other visualization libraries in Python

Using Scikit-learn

  • Building supervised learning models
  • Developing classification models
  • Model training and evaluation
  • Visualizing results
  • Calculating and plotting the confusion matrix

Introduction to Deep Learning using Keras and TensorFlow

  • Installing TensorFlow and Keras
  • Introduction to neural networks
  • Building and training artificial neural networks (ANN)
  • Introduction to convolutional neural networks (CNN)
  • Building and training an image classifier using CNN
  • Training and evaluating deep learning models

Requirements

Enrollment in this course is restricted exclusively to participants who previously attended the "Python and Data Visualization" workshop led by Ahmed on February 11, 2021.

 14 Hours

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