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MIT University offers free online courses (no applications or fees required) Here are 10 FREE courses you don't want to miss: 1. Introduction to Computer Science and Programming Using Python Learn how to write programs to tackle real-world problems using Python. 🦾 https://lnkd.in/eYmkb4bN 2. Machine Learning with Python Learn about the principles and algorithms of machine learning. 🦾 https://lnkd.in/gjzMetQC 3. Understanding the World Through Data Learn how to leverage data and basic machine learning algorithms. 🦾 https://lnkd.in/g8m5CEd6 4. Becoming an Entrepreneur Learn to define your goals as an entrepreneur, perform market research, and design and test your offering. 🦾 https://lnkd.in/gGVxRSdQ 5. Cybersecurity for Critical Urban Infrastructure Learn how to prepare and understand the scale, scope, and impact of cyberattacks on critical urban infrastructure. 🦾 https://lnkd.in/eziWEWiY 6. Foundations of Modern Finance Learn to understand corporate finance and capital budgeting. 🦾 https://lnkd.in/eqGTzfc8 7. The Secret of Life Learn how to describe the building blocks of life. 🦾 https://lnkd.in/eM-kWzZJ 8. The Science of Uncertainty and Data Learn about probabilistic models, random processes, and the basic elements of statistical inference. 🦾 https://lnkd.in/g7VQXvDy 9. Introduction to Computational Thinking and Data Science Learn how to simulate a robot vacuum cleaning a room or model the population dynamics of viruses and drug treatments in a patient's body. 🦾 https://lnkd.in/gFvmbQ24 10. Computational Thinking for Modeling and Simulation Learn how to develop the thought processes involved in formulating a problem so a computer can effectively carry out the solution. 🦾 https://lnkd.in/eRVg7itq __ Here are 100+ Data Science Resources https://lnkd.in/giD4c3FS Premium Data Science Interview Resources https://lnkd.in/gmiFf4fA

LinkedIn: MIT University just released free online courses. Here are 7 courses you don't want to miss in 2024. 1) Introduction to Computer Science and Programming Using Python: Topics: • Simple algorithms. • Understanding Computation. • Data structures and beyond. • Python Programming essentials. • Intro to algorithmic complexity. Link: https://lnkd.in/gGFTM5Te 2) Introduction to Computational Thinking and Data Science: Topics: • Monte Carlo simulations. • Plotting with pylab. • Stochastic programming. • Statistical thinking. Link: https://lnkd.in/ggaB8yns 3)Machine Learning with Python: Topics: • Model Selection. • ML Project Execution. • Model Implementation & Analysis. • Training, Validation, Tuning, Features. Link: https://lnkd.in/g--Hcewk 4) The Science of Uncertainty and Data: Topics: • Probabilistic Calculations. • Basics of Probabilistic Models. • Introduction to Inference Method. • Random variables & Distributions. Link: https://lnkd.in/gEb5rFN2 5) Startup Success: How to Launch a Technology Company in 3 steps: Topics covered: • Assess product ideas. • Understand the start-up team. • Prepare and evaluate strategies. Link: https://lnkd.in/gY4C-JUN 6) Data Analysis: Statistical Modeling: A hands-on introduction to the interplay between statistics and computation for real data analysis. Learn models, from hypotheses, and perform statistical analysis on real data. Link: https://lnkd.in/gJdVCVjN 7) Becoming an Entrepreneur: Topics: • Identifying lucrative business opportunities. • Crafting & refining your product. • Debunking common entrepreneurial myths Link: https://lnkd.in/g7gX-dpn --- Note: If you need advice on how to take Coursera courses for free, check this out: 🔗 https://lnkd.in/gN8pAGaM

Harvard University announced free online courses. Here are 9 courses  you cannot afford to miss in 2024: Check out the list below: 👇 __________ 1. Introduction to AI with Python. Learn machine learning in Python. You’ll learn: • Machine learning • Neural networks • knowledge representation • natural language processing • And many more. 2. Introduction to Computer Science. Explore computer science and programming. You’ll learn: - Algorithmic thinking - Project development. - Multiple languages (C, Python, SQL, etc.) 3. Mobile App Development Course Develop cross-platform native apps using JavaScript with React Native. Learn: - JavaScript - Debugging - Components, Props, State, Style - Components, Views, User Input - And many more. 4. Data Science: Machine Learning. Build a movie recommendation system using popular data science techniques. Learning Objectives: - Basics of ML - Popular algorithms - Recommendation system building 5. CS50's Introduction to Programming with Python. Explore programming using Python for various applications like: - Data Science - Web programming - General-purpose programming 6. Computer Science for Business Professionals. You’ll learn: → Programming languages → Internet technologies → Web development → Cloud computing 7. Web Programming with Python and JavaScript 2023. Learning Objectives: - Web apps using Python and JavaScript - Design and implement web apps - Frameworks mastery. 8. CS50's Introduction to Game Development. - Explore 2D and 3D game development - Learn how to Design and build interactive games 9. Introduction to Data Science with Python. In this online course learn how to use Python to harness and analyze data. Learning Objectives: → Use popular libraries → Run basic ML models → Practice Python for modelling __________ Repost to share with your network. ♻️ 🖐 BONUS: Subscribe to my newsletter Superbold.AI for more such resources. #courses #ai #chatgpt #interviewtips #jobsearch #openai #linkedin #jobpreparation #remotejobs 

MIT University released free online courses. No fee required. Ends March 28 Link: https://lnkd.in/dfbFJpYF 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 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 3. Introduction to Computational Thinking and Data Science. Topics: • Plotting with the pylab package • Stochastic programming and statistical thinking • Monte Carlo simulations 4. Supply Chain Analytics Topics: • Basic analytical methods • How to apply basic probability models • Statistics in supply chains • Formulating and solving optimization models 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 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 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 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 many more That's a wrap! If you find this post helpful

MIT University just released free online courses. You don't need to make a payment. 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 • Informal introduction to algorithmic complexity • Data Structures and more 🔗https://lnkd.in/gqmR28fv 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 🔗https://lnkd.in/dpARWyFc 3. Supply Chain Analytics Topics: • Basic analytical methods • How to apply basic probability models • Statistics in supply chains • Formulating and solving optimization models 🔗https://lnkd.in/dYwTk7gZ 4. 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 🔗https://lnkd.in/drxHKAFz 5. 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 🔗https://lnkd.in/dsX27BVB 6. 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 🔗https://lnkd.in/dWWnj6zk 7. 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 Part-1: https://lnkd.in/dXeGR7-8 Part-2: https://lnkd.in/drCXf23d 8. The Secret of Life 🔗https://lnkd.in/dbBsFE6u 9. The Science of Uncertainty and Data 🔗https://lnkd.in/dcnPfk-s

MIT University released free online courses. No textbooks or fees required. Here are 7 free 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 https://lnkd.in/draVuUg3 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 https://lnkd.in/d872bUAj 3. Introduction to Computational Thinking and Data Science. Topics: • Plotting with the pylab package • Stochastic programming and statistical thinking • Monte Carlo simulations https://lnkd.in/dQGBQhPi 4. Supply Chain Analytics Topics: • Basic analytical methods • How to apply basic probability models • Statistics in supply chains • Formulating and solving optimization models https://lnkd.in/gNnGSUJ5 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 https://lnkd.in/gAdWUYJ8 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 https://lnkd.in/dGxgZgK9 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 https://lnkd.in/gUPtbKYH --- We have put together 100+ Data Science Resources Which is worth 1000s dollars. https://lnkd.in/giD4c3FS

Reposting for reach MIT Professional Education Check out these free online courses for #continuouslearning

MIT University just released free online courses. No payment 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 • Informal introduction to algorithmic complexity • Data Structures and more 🔗https://lnkd.in/dmqQ6uFn 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 🔗https://lnkd.in/d_HwTAq6 3. Supply Chain Analytics Topics: • Basic analytical methods • How to apply basic probability models • Statistics in supply chains • Formulating and solving optimization models 🔗https://lnkd.in/dA-ECidW 4. 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 🔗https://lnkd.in/dh4UkrM8 5. 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 🔗https://lnkd.in/dNZtY8sB 6. 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 🔗https://lnkd.in/djqTgu36 7. 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 Part-1: https://lnkd.in/dm5epmgb Part-2: https://lnkd.in/dtBxk9JZ 8. The Secret of Life 🔗https://lnkd.in/dUhmM5JW 9. The Science of Uncertainty and Data 🔗https://lnkd.in/drfjDEJg Source: Shruti Mishra follow her for getting more valuable content.