Course Title: Machine Learning & Data Science


70 hrs.

Course Description:

  • Build expertise in data manipulation, visualization, predictive analytics, machine learning, and data science. With the skills you learn in a program, you can launch or advance a successful data career. Start acquiring valuable skills right away, create a project portfolio to demonstrate your abilities, and get support from mentors, peers, and experts in the field.
  • The demand for Machine Learning and Data science professionals is booming, far exceeding the supply of personnel skilled in this field. The industry is clearly embracing AI, embedding it within its fabric. The demand for Machine Learning and Data science skills by employers -- and the job salaries of Machine Learning and Data Science practitioners -- are only bound to increase over time, as AI becomes more pervasive in society. Machine Learning and Data Science are a future-proof career.
  • Gain real-world data science experience with projects designed by industry experts. Build your portfolio and advance your data science and machine learning career.
  • Throughout this program you will practice your Data Science and Machine Learning skills through a series of hands-on labs, assignments, and projects inspired by real world problems and data sets from the industry. You will also complete the program by preparing a Data Science and Machine Learning capstone project that will showcase your applied skills to prospective employers.

Target Audience:

  • Developers and Software Engineers.
  • Analytics Managers and Professionals.
  • Statisticians with an interest in Machine.

Course Prerequisite:

  • Basic skills with at least one programming language are desirable - optional.
  • Familiar with the basic math and statistic concepts – optional

Course Contents:

  1. Python 3
    • Git & GitHub. o Environment Setup (Anaconda).
    • Command Line.
    • Conda & pip package managers.
    • Jupyter Notebook.
    • Input & Output.
    • Variables.
    • Data types.
    • File Handling.
    • If Conditions.
    • For Loops.
    • Built-in functions & Operators (zip, enumerate, range, …).
    • List Comprehensions.
    • Functions.
    • Lambda Expressions.
    • Map, Filter, Reduce.
    • Modules & Packages.
    • Object-Oriented Programming (OOP).
  2. Mathematics For AI
    • Calculus.
    • Linear Algebra.
    • Probability.
    • Statistics.
  3. Exploratory Data Analysis with NumPy & Pandas.
    • NumPy
    • Pandas
      • Project #1 (Play with SF Salaries dataset from Kaggle)
  4. Data Visualization with Matplotlib & Seaborn
    • Data Visualization.
    • Project #2 (Titanic Analysis Project).
    • Project #3 (911 calls dataset from Kaggle analysis).
  5. Data Preprocessing & ETL
    • Machine Learning.
    • Data Preprocessing.
    • Feature Transformations.
    • Project #4 (Stock Market Analysis Project).
  6. Machine Learning
    • Supervised Learning.
      • Regression
      • Project #5 (Predict student marks based on hours of study)
      • Project #6 (Housing Prices Prediction Project(Dataset)
      • Classification.
    • Unsupervised Learning
      • Clustering.
      • Dimension Reduction.
      • Evaluating Model Performance.
    • Model Selection & Evaluation