MIT University released free online courses.
No textbooks or fees required.
Here are 10 courses you don't want to miss:
No textbooks or fees required.
Here are 10 courses you don't want to miss:
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
đź”— edx.org
Topics:
• A Notion of computation
• Python programming language
• Some simple algorithms
• Testing & debugging
• Informal introduction to algorithmic complexity
• Data Structures
đź”— 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
đź”— edx.org
Topics:
- ML problem principles
- Implement & analyze models
- Choosing suitable models for different apps
- ML project implementation: Training, validation, tuning, and feature engineering
đź”— edx.org
3. Introduction to Computational Thinking and Data Science.
Topics:
• Plotting with the pylab package
• Stochastic programming and statistical thinking
• Monte Carlo simulations
đź”— edx.org
Topics:
• Plotting with the pylab package
• Stochastic programming and statistical thinking
• Monte Carlo simulations
đź”— 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
đź”— edx.org
Topics:
• Basic analytical methods
• How to apply basic probability models
• Statistics in supply chains
• Formulating and solving optimization models
đź”— 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
đź”— 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
đź”— 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
đź”— 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
đź”— 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
đź”— edx.org
- Interpolation methods and their impact on model convergence
- Numerical integration techniques
- Procedures for numerical differentiation
- Solving linear and nonlinear equations
đź”— 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
đź”— 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
đź”— 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
đź”— 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
đź”— 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
đź”— edx.org
Topics:
• The basic structure and elements of probabilistic models
• Random variables, their distributions, means, and variances
• Probabilistic calculations
• Inference methods
More
đź”— edx.org
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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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