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Course Outline

Introduction to Python

  • Understanding Variables, Tuples, and Lists
  • Implementing Loops and Control Statements
  • Working with Modules and Imports

Setting Up the Development Environment

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

Vectorizing Data with NumPy

  • Creating NumPy arrays
  • Performing common matrix operations
  • Utilizing ufuncs
  • Understanding views and broadcasting on NumPy arrays
  • Optimizing performance by eliminating loops
  • Profiling performance with cProfile

Data Analysis with Pandas

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

Data Visualization

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

Utilizing Scikit-learn (Sklearn)

  • Developing Supervised Learning models
  • Constructing Classification models
  • Training and evaluating models
  • Visualizing results
  • Calculating and plotting 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)
  • Introduction to Convolutional Neural Networks (CNN)
  • Developing and training an Image Classifier using CNN
  • Training and evaluating Deep Learning models

Requirements

Attendance at the "Python and Data Visualization" course taught by Ahmed on February 11, 2021, is a strict requirement for enrollment in this course.

 14 Hours

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