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1. Introduction to Computer Science and Programming Using Python. Topics: • A Notion of computation • Python programming language • Some simple algorithms • Informal introduction to algorithmic complexity • Data Structures and more 🔗

731,991 次观看 • 2 年前 •via X (Twitter)

11 条评论

Shruti Mishra 的头像
Shruti Mishra2 年前

MIT University just released free online courses. No payment required. Here are 10 courses you don't want to miss in 2024:

Shruti Mishra 的头像
Shruti Mishra2 年前

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 🔗

Shruti Mishra 的头像
Shruti Mishra2 年前

4. Supply Chain Analytics Topics: • Basic analytical methods • How to apply basic probability models • Statistics in supply chains • Formulating and solving optimization models 🔗

Shruti Mishra 的头像
Shruti Mishra2 年前

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 🔗

Shruti Mishra 的头像
Shruti Mishra2 年前

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 🔗

Shruti Mishra 的头像
Shruti Mishra2 年前

7. Computational Thinking for Modeling and Simulation Topics: • Interpolation methods and their impact on model convergence • Numerical integration techniques • Procedures for numerical differentiation • Solving linear and nonlinear equations 🔗

Shruti Mishra 的头像
Shruti Mishra2 年前

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 🔗

Shruti Mishra 的头像
Shruti Mishra2 年前

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 and more 🔗

Shruti Mishra 的头像
Shruti Mishra2 年前

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 and more 🔗

Shruti Mishra 的头像
Shruti Mishra2 年前

That's a wrap! If you found this helpful, Repost to share the knowledge with others. Bookmark this post and follow @heyshrutimishra for more.

Joseph 的头像
Joseph2 年前

These listed topics all align with artificial intelligence and advanced computing. " MIT OpenCourseWare (OCW)."

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if you're struggling on where to start learning ML, here’s a playlist of 30 youtube videos to learn machine learning fundamentals from scratch "Machine Learning: Teach by Doing" is a solid choice to learn both theory and code. (1) Introduction to Machine Learning Teach by Doing: (2) What is Machine Learning? History of Machine Learning: (3) Types of ML Models: (4) 6 steps of any ML project: (5) Install Python and VSCode and run your first code: (6) Linear Classifiers Part 1: (7) Linear Classifiers Part 2: (8) Jupyter Notebook, Numpy and Scikit-Learn: (9) Running the Random Linear Classifier Algorithm in Python: (10) The oldest ML model - Perceptron: (11) Coding the Perceptron: (12) Perceptron Convergence Theorem: (13) Magic of features in Machine Learning: (14) One hot encoding: (15) Logistic Regression Part 1: (16) Cross Entropy Loss: (17) How gradient descent works: (18) Logistic Regression from scratch in Python: (19) Introduction to Regularization: (20) Implementing Regularization in Python: (21) Linear Regression Introduction: (22) Ordinary Least Squares step by step implementation: (23) Ridge regression fundamentals and intuition: (24) Regression recap for interviews: (25) Neural network architecture in 30 minutes: (26) Backpropagation intuition: (27) Neural network activation functions: (28) Momentum in gradient descent: (29) Hands on neural network training in Python: (30) Introduction to Convolutional Neural Networks (CNNs):

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109,447 次观看 • 1 年前