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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

Vectorizing Data in NumPy

  • Creating NumPy arrays
  • Performing common operations on matrices
  • Utilizing ufuncs
  • Views and broadcasting in NumPy arrays
  • Optimizing performance by minimizing loops
  • Performance optimization using cProfile

Data Analysis with Pandas

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

Data Visualization

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

Using Scikit-learn (Sklearn)

  • Building Supervised Learning Models
  • Developing Classification Models
  • Model training and evaluation
  • Plotting results
  • Calculating and plotting Confusion Matrices

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
  • Building and Training Image Classifiers using CNNs
  • Training and Evaluating Deep Learning Models

Requirements

This course is exclusively available to participants who attended the 'Python and Data Visualization' session with Ahmed on February 11, 2021.

 14 Hours

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Price per participant

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