FREE Certification Courses Offered By MIT

FREE Certification Courses Offered By MIT :-

MIT, Is One of the world’s top Universities, Offering free courses for 2024. Free online courses provide accessible learning opportunities for individuals at all levels.

Learners can also earn certificates in specific areas at no cost, enhancing their skills and knowledge. Learn at your own pace from any location, using any device. The complete details about FREE Certification Courses Offered By MIT are as follows.

1) Introduction to Computer Science and Programming Using Python

The topics covered include a notion of computation, Python programming language, some simple algorithms, an informal introduction to algorithmic complexity, and data structures.

This course is the first of a two-course sequence: Introduction to Computer Science and Programming Using Python, and Introduction to Computational Thinking and Data Science. Together, they are designed to help people with no prior exposure to computer science or programming learn to think computationally and write programs to tackle useful problems. Some of the people taking the two courses will use them as a stepping stone to more advanced computer science courses, but for many it will be their first and last computer science courses.

This run features lecture videos, lecture exercises, and problem sets using Python 3.5. Even if you previously took the course with Python 2.7, you will be able to easily transition to Python 3.5 in future courses, or enroll now to refresh your learning.

What you’ll learn:

• A Notion of computation
• The Python programming language
• Some simple algorithms
• Testing and debugging
• An informal introduction to algorithmic complexity
• Data structures

Course Link:- Click Here To Start

2) The Science of Uncertainty and Data

Topics cover the basic structure & elements of probabilistic models, random variables, their distributions means, and variances, probabilistic calculations, and inference methods.

The course covers all of the basic probability concepts, including:

• multiple discrete or continuous random variables, expectations, and conditional distributions
• laws of large numbers
• the main tools of Bayesian inference methods
• an introduction to random processes (Poisson processes and Markov chains)

What you’ll learn:

• The basic structure and elements of probabilistic models
• Random variables, their distributions, means, and variances
• Probabilistic calculations
• Inference methods
• Laws of large numbers and their applications
• Random processes

Course Link:- Click Here To Start

3) Foundations of Modern Finance

The topics cover valuation of fixed income securities and stocks, risk analysis including APT and Efficient Market Hypothesis, and introduction to corporate finance and capital budgeting..

This is a two-part course, and part of the MicroMasters® Program in Finance. It provides a rigorous and comprehensive introduction to the fundamentals of modern finance and their applications to business challenges in valuation, investments, and corporate financial decisions under a unified framework.

Completing this first course and program will help you prepare for a career as a financial analyst, financial advisor, vice president for finance, chief financial officer, and more.

Finance provides a core function in any productive economy by providing a mechanism for savings, investment, and liquidity. Whether the learner is in a industrialized country or a developing country, financial services are essential for smooth functioning of the economy.

What you’ll learn:

• Valuation of fixed income securities and common stocks
• Risk analysis, the Arbitrage Pricing Theory (APT), and the Efficient Market Hypothesis
• Introduction to corporate finance and capital budgeting
• Valuation of derivative securities
• Portfolio theory and the Capital Asset Pricing Model (CAPM)
• Corporate financial decisions
  • Real options, capital structure, payout policy, corporate bonds; and
  • Interaction between investment and financing decisions

Course Link:- Click Here To Start

4) Supply Chain Analytics

Topics include basic analytical methods, applying basic probability models, statistics in supply chains, and formulating and solving optimization models.

Supply chains are complex systems involving multiple businesses and organizations with different goals and objectives. Many different analytical methods and techniques are used by researchers and practitioners alike to better design and manage their supply chains. This business and management course introduces the primary methods and tools that you will encounter in your study and practice of supply chains. We focus on the application of these methods, not necessarily the theoretical underpinnings.

Courses will begin with an overview of introductory probability and decision analysis to ensure that students understand how uncertainty can be modeled. Next, we will move into basic statistics and regression. Finally, Courses will introduce optimization modeling from unconstrained to linear, non-linear, and mixed integer linear programming.

This is a hands-on course. Students will use spreadsheets extensively to apply these techniques and approaches in case studies drawn from actual supply chains.

SC0x is different from our other courses as it is self-paced and has a scheduled final exam. All material is made available during the second week, allowing learners to begin with any topic at their own convenience.

What you’ll learn:

• Basic analytical methods
• How to apply basic probability models
• Statistics in supply chains
• Formulating and solving optimization models

Course Link:- Click Here To Start

5) Machine Learning with Python: from Linear Models to Deep Learning

Topics cover ML problem principles, model implementation and analysis, selecting suitable models for various apps, and ML project implementation: training, validation, tuning, and feature engineering.

Machine learning methods are commonly used across engineering and sciences, from computer systems to physics. Moreover, commercial sites such as search engines, recommender systems (e.g., Netflix, Amazon), advertisers, and financial institutions employ machine learning algorithms for content recommendation, predicting customer behavior, compliance, or risk.

As a discipline, machine learning tries to design and understand computer programs that learn from experience for the purpose of prediction or control.

In this course, students will learn about principles and algorithms for turning training data into effective automated predictions. We will cover:

• Representation, over-fitting, regularization, generalization, VC dimension;
• Clustering, classification, recommender problems, probabilistic modeling, reinforcement learning;
• On-line algorithms, support vector machines, and neural networks/deep learning.

Students will implement and experiment with the algorithms in several Python projects designed for different practical applications

What you’ll learn:

• Understand principles behind machine learning problems such as classification, regression, clustering, and reinforcement learning
• Implement and analyze models such as linear models, kernel machines, neural networks, and graphical models
• Choose suitable models for different applications
• Implement and organize machine learning projects, from training, validation, parameter tuning, to feature engineering.

Course Link:- Click Here To Start

6) Introduction to Biology- The Secret of Life

Topics include describing the building blocks of life and how their interactions influence structure and function in biology, as well as predicting genotypes and phenotypes using genetics data.

Explore the secret of life through the basics of biochemistry, genetics, molecular biology, recombinant DNA, genomics and rational medicine.

What you’ll learn:

• 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
• How to explain the central dogma of molecular biology and convert DNA sequence to RNA sequence to protein sequence
• How to use molecular tools to study biology
• How to describe the principles of early sequencing as well as modern sequencing and the effects of these technologies on the filed of genomics
• How to apply the principles of modern biology to issues in today’s society.

Course Link:- Click Here To Start

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