Machine Learning

course outline


  • Introduction to Python
  • Data Structure: List, Tuple, Dictionary.
  • Methods: Inbuilt functions and Self-Declared Functions.
  • Object-Oriented Programming with Classes: Inheritance
  • Introduction to Anaconda.
  • Introduction to Jupyter
  • Notebook.
  • Introduction to top data science
  • libraries: NumPy, pandas, seaborn, matplotlib.
  • Plotting Graphs: line plot, multiple line plots, pie chart, histogram, bar chart, scatter plot, etc


  • Introduction to PostgreSQL: Understanding Basic Queries using PostgreSQL.
  • Statistics data analysis: mean, mode, median, assume mean, standard deviation, etc
  • Data collection and wrangling: web scraping, using open source data,
  • purchasing data online, etc
  • Introduction to Modelling: Predictive and prescriptive modelling
  • Introduction to Sklearn


  1. Tuning model parameters
  2. Introduction to deep learning:
  3. Artificial Neural Networks,
  4. Introduction to deep learning
  5. libraries: TensorFlow, PyTorch, autogluon, etc
  6. Introduction to unstructured data: data collection, data preprocessing, data visualization, etc
  7. Training a deep learning model with unstructured data.

Other Info

Virtual: Live online classes

  • Location: Favored Online Learning¬†
  • Virtual App: Zoom

Internet & A laptop


12 Saturdays

18 HRS

Application fee of $3 or (1,150 NGN) will be required to finalize your application

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