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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
  • Setting up Jupyter
  • Managing Python modules via Pip

Vectorizing Data in Numpy

  • Creating Numpy arrays
  • Performing common matrix operations
  • Utilizing ufuncs
  • Implementing views and broadcasting on Numpy arrays
  • Optimizing performance by eliminating loops
  • Profiling performance using cProfile

Data Analysis with Pandas

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

Data Visualization

  • Generating diagrams with Matplotlib
  • Integrating Matplotlib within Pandas
  • Producing high-quality diagrams
  • Visualizing data in Jupyter Notebooks
  • Exploring additional Python visualization libraries

Using Sklearn

  • Developing Supervised Learning Models
  • Constructing 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
  • Understanding Neural Networks
  • Building and Training Artificial Neural Networks (ANN)
  • Overview of Convolutional Neural Networks (CNN)
  • Constructing and Training an Image Classifier using CNN
  • Training and evaluating Deep Learning models

Requirements

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

 14 Hours

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