Create a micro course online - Machine Learning

What do we do?

We create short online courses for college students majoring in Computer Science and Engineering. Our micro-courses cover emerging subjects like AI, machine learning, data science, cybersecurity, software engineering, and more. Students can access these courses at no cost to supplement their education with skills that are highly on-demand on the job market.

Why do we do this?

Our mission is to prepare students for STEM careers. By offering these self-paced courses, we provide students the flexibility to continue learning beyond the constraints of the traditional classroom model. Our course modules feature hands-on projects and interactive exercises to engage learners. We believe exposure to applied skills will equip students to be more competitive in the workforce.

What is a micro-course?

A micro course is a short online course focused on a specific subject. It typically has a duration of 4 to 6 hours.

Some examples of micro courses are:

• Introduction to Machine Learning Concepts
• Supervised Learning: Regression and Classification
• Unsupervised Learning: Clustering and Dimensionality Reduction
• Neural Networks and Deep Learning
• Natural Language Processing (NLP) Fundamentals
• Computer Vision Basics
• Recommender Systems
• Time Series Analysis and Forecasting
• Reinforcement Learning Essentials
• Deploying Machine Learning Models

As a micro course developer, you will he lp create slides, tutorials, and videos that teach a subject of your choice. Your micro-course will be available to students worldwide. The target audience is college undergraduate and graduate students.

Minimum expectations:

• Availability: Commit 4-8 hours per week to course development.
• Collaboration: Collaborate with other instructional designers.
• Commitment: Passionate about computer science and education
• The applicant must reside in the United States or Canada.

Preferred Tech Stacks and Skills:

1. Programming:

• Python (NumPy, Pandas, Matplotlib, Scikit-learn)

2. Machine Learning Frameworks:

• TensorFlow
• Keras

3. Data Manipulation and Analysis:

• Data preprocessing and feature engineering
• Exploratory Data Analysis (EDA)

4. Machine Learning Algorithms:

• Supervised learning (linear regression, logistic regression, decision trees, random forests, SVM)
• Unsupervised learning (clustering, dimensionality reduction)
• Deep learning (CNNs, RNNs)

5. Model Evaluation and Validation:

• Evaluation metrics (accuracy, precision, recall, F1-score, MSE, MAE)
• Cross-validation techniques

6. Data Visualization:

• Matplotlib, Seaborn, Plotly

7. Version Control and Collaboration:

• Git, GitHub/GitLab

8. Strong mathematical foundation (linear algebra, calculus, probability, statistics)