MIT University released free online courses.
No textbooks or fees required.
Here are 10 courses you don't want to miss in 2024
No textbooks or fees required.
Here are 10 courses you don't want to miss in 2024
1. Introduction to Computer Science and Programming Using Python.
Topics:
• A Notion of computation
• Python programming language
• Some simple algorithms
• Testing & debugging
• Informal introduction to algorithmic complexity
• Data Structures
🔗 edx.org
Topics:
• A Notion of computation
• Python programming language
• Some simple algorithms
• Testing & debugging
• Informal introduction to algorithmic complexity
• Data Structures
🔗 edx.org
2. Machine Learning with Python
Topics:
- ML problem principles
- Implement & analyze models
- Choosing suitable models for different apps
- ML project implementation: Training, validation, tuning, and feature engineering
🔗 edx.org
Topics:
- ML problem principles
- Implement & analyze models
- Choosing suitable models for different apps
- ML project implementation: Training, validation, tuning, and feature engineering
🔗 edx.org
3. Introduction to Computational Thinking and Data Science.
Topics:
• Plotting with the pylab package
• Stochastic programming and statistical thinking
• Monte Carlo simulations
🔗 edx.org
Topics:
• Plotting with the pylab package
• Stochastic programming and statistical thinking
• Monte Carlo simulations
🔗 edx.org
4. Supply Chain Analytics
Topics:
• Basic analytical methods
• How to apply basic probability models
• Statistics in supply chains
• Formulating and solving optimization models
🔗 edx.org
Topics:
• Basic analytical methods
• How to apply basic probability models
• Statistics in supply chains
• Formulating and solving optimization models
🔗 edx.org
5. Understanding the World Through Data.
Topics:
• Python programming and the Colab notebook programming environment
• Dependent and independent variables
• Relationships between data using linear and polynomial regression models
And more
🔗 edx.org
Topics:
• Python programming and the Colab notebook programming environment
• Dependent and independent variables
• Relationships between data using linear and polynomial regression models
And more
🔗 edx.org
6. Becoming an Entrepreneur
Topics:
• Overcoming the top myths of entrepreneurship
• Defining your goals as an entrepreneur and startup
• Identifying business opportunities
• Performing market research and choosing your target customer
And more
🔗 edx.org
Topics:
• Overcoming the top myths of entrepreneurship
• Defining your goals as an entrepreneur and startup
• Identifying business opportunities
• Performing market research and choosing your target customer
And more
🔗 edx.org
7. Computational Thinking for Modeling and Simulation
- Interpolation methods and their impact on model convergence
- Numerical integration techniques
- Procedures for numerical differentiation
- Solving linear and nonlinear equations
🔗 edx.org
- Interpolation methods and their impact on model convergence
- Numerical integration techniques
- Procedures for numerical differentiation
- Solving linear and nonlinear equations
🔗 edx.org
8. Foundations of Modern Finance
Topics:
• Valuation of fixed income securities and common stocks
• Risk analysis, APT, Efficient Market Hypothesis
• Introduction to corporate finance and capital budgeting
• And more
🔗 edx.org
Topics:
• Valuation of fixed income securities and common stocks
• Risk analysis, APT, Efficient Market Hypothesis
• Introduction to corporate finance and capital budgeting
• And more
🔗 edx.org
9. The Secret of Life
Topics:
- How to describe the building blocks of life and how their interactions dictate structure and function in biology
- How to predict genotypes and phenotypes given genetics data
- More
🔗 edx.org
Topics:
- How to describe the building blocks of life and how their interactions dictate structure and function in biology
- How to predict genotypes and phenotypes given genetics data
- More
🔗 edx.org
10. The Science of Uncertainty and Data
Topics:
• The basic structure and elements of probabilistic models
• Random variables, their distributions, means, and variances
• Probabilistic calculations
• Inference methods
More
🔗 edx.org
Topics:
• The basic structure and elements of probabilistic models
• Random variables, their distributions, means, and variances
• Probabilistic calculations
• Inference methods
More
🔗 edx.org
Thanks for reading.
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That's a wrap!
Hope you enjoyed it.
If you find this post helpful, please :
1. Follow me @CodeByPoonam for more.
2. Like/Repost the first tweet below for support.
Hope you enjoyed it.
If you find this post helpful, please :
1. Follow me @CodeByPoonam for more.
2. Like/Repost the first tweet below for support.
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