The concept of Machine Learning and Data Science Need revision

The concept of Machine Learning and Data Science Need revision.


Hello @Bhagwan_Das2 , thanks for using Discourse. This course was designed to provide a general introduction to Artificial Intelligence for a wide audience. Due to the sudden influx of interest caused by ChatGPT and other things, So it is important to supplement this course with additional resources that cover the latest developments in the field and help you make the most out of the course.

I recommend signing up for the DeepLearning.AI newsletter “The Batch” to remain up to date on the newest AI news and breakthroughs. This newsletter offers essential insights into the most recent developments, research articles, and industry updates in the field of artificial intelligence. It’s an excellent resource for staying up to date on the most recent advances in the AI community.

Towards AI” is another newsletter to consider. It offers in-depth coverage of AI news, research, and industry advancements. Subscribing to this newsletter will keep you informed on the most recent trends and breakthroughs in the area.

Furthermore, “Nivedan from Future & AI” is another newsletter focusing on the intersection of AI and the future of labour. It delves into how AI is affecting numerous industries, as well as its impact on jobs and society. This newsletter might provide you with a broader perspective on the implications and applications of AI in several industries.

This will keep you up to date on the newest advances in AI and will supplement your understanding obtained from this introductory course.

Hope this answers your query.

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I am having difficulty understanding the concept of Machine Learning. It would be very nice if someone explains it.

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Hi @nithish_kannan

Welcome to the community.

At its core, Machine Learning (ML) is a subset of artificial intelligence (AI) that focuses on enabling computers to learn from data and improve their performance on a task over time without being explicitly programmed. In traditional programming, humans write explicit instructions for a computer to follow, but in Machine Learning, the computer learns patterns from data and uses these patterns to make predictions or decisions.

With that in mind, imagine you’re teaching a computer to perform a task by showing it examples instead of giving it explicit instructions. For instance, consider teaching a computer to differentiate between images of cats and dogs. Instead of writing a step-by-step program to identify each feature of a cat or a dog, you would provide the computer with a large set of images labeled as either “cat” or “dog.” The computer uses this collection of images to learn patterns on its own.

Think of Machine Learning as training a virtual brain within the computer. Just like our brains learn from experiences, a Machine Learning model learns from data. It looks for patterns, relationships, and trends in the data that allow it to make informed predictions or decisions. This learning process is analogous to how humans learn to recognize objects, understand languages, or even play games.

The process starts with data. This data serves as the “experience” for the virtual brain. It’s the raw material from which the computer extracts knowledge. This data can be anything: pictures, texts, numbers, or even sensor readings from devices.

Before feeding the data to the virtual brain, we need to prepare it. This is like cleaning up the data to make sure it’s in a format that the virtual brain can understand. Sometimes data might be messy, incomplete, or inconsistent, and this preprocessing step helps to refine it.

Once the data is prepared, the computer uses it to “train” the virtual brain. During training, the model tries to find patterns in the data that can help it make accurate predictions or classifications. It adjusts its internal parameters based on the examples it sees. For instance, if the model is learning to distinguish between cats and dogs, it starts to recognize features that differentiate the two, like shapes of ears, sizes of noses, and so on.

After the model is trained, it’s tested on new, unseen data to see how well it performs. This evaluation tells us if the model has learned meaningful patterns that can be generalized to real-world situations. If the model’s performance is not satisfactory, adjustments are made. This might involve changing the model’s structure, fine-tuning its parameters, or even acquiring more data.

One of the fascinating aspects of Machine Learning is its ability to improve over time. As the model encounters more data, it continues to refine its understanding, making it increasingly accurate in its predictions. This learning process is iterative; you train, evaluate, adjust, and repeat until the model meets your desired level of performance.

In essence, Machine Learning is about enabling computers to learn from data, identify patterns, and make informed decisions or predictions without being explicitly programmed for every specific scenario. It’s like creating a digital apprentice that learns from examples to become better at tasks, often reaching a level of performance that surpasses what traditional rule-based programming can achieve.