Sunday, February 4, 2024
Wednesday, December 13, 2023
Tuesday, December 12, 2023
Calculating Average & Total Marks
Grade Calculator
You can comment any Requirement, I will provide it free
Tuesday, August 29, 2023
Complete Machine Learning Notes for BCA Final Year Students
BCADS-517 MACHINE LEARNING
UNIT I: (8 Sessions)
Introduction: Learning theory, Hypothesis, and target class, Inductive bias and bias-variance trade-off, Occam's razor, Limitations of inference machines, Approximation and estimation errors for skill development and employability.
1. Learning Theory
Learning theory in Machine Learning (ML) is a framework that
helps us understand how algorithms can learn patterns and make predictions from
data. It provides a theoretical foundation for understanding the capabilities
and limitations of various machine learning algorithms. Learning theory
explores questions like:
1. Generalization: How well does a model
perform on new, unseen data? Can it generalize the patterns it learned from the
training data to make accurate predictions on new instances?
2. Overfitting and Underfitting: When is a model too
complex (overfitting) or too simple (underfitting)? Learning theory helps us
find the right balance between these extremes for better performance on unseen
data.
3. Sample Complexity: How much training data
is needed for a model to learn accurately? Learning theory helps us understand
how the size and quality of the training dataset affect a model's learning
process.
4. Convergence: Does the algorithm
reach a stable solution as it learns from data? Learning theory helps us
understand whether a particular algorithm will eventually converge to a
solution that accurately represents the target function.
5. Algorithmic Guarantees: Learning theory
provides insights into the performance guarantees of various algorithms. It
helps us answer questions like: How well will the algorithm perform under
different conditions? Can we expect certain levels of accuracy?
6. Bias and Variance: Learning theory ties
into the bias-variance trade-off, helping us understand how the complexity of a
model affects its bias and variance, and consequently its generalization
performance.
7. PAC Learning: Probably Approximately
Correct (PAC) learning is a key concept in learning theory. It defines
conditions under which a machine learning algorithm can learn with high
probability and generalization from a finite amount of training data.
In essence, learning theory helps us understand the fundamental
principles behind how machine learning algorithms work, how they learn from
data, and how they perform on new, unseen data. It provides a theoretical basis
for designing algorithms, selecting appropriate model complexities, and
evaluating their performance. While it can involve some mathematical concepts,
having a grasp of learning theory can greatly enhance your understanding of the
underlying principles of machine learning.
2. Hypothesis and Target Class
When you're learning about Machine Learning (ML), it's helpful
to think of it as teaching a computer to learn from data. One of the
fundamental concepts in ML is the idea of a "hypothesis" and a
"target function."
1. Target Function: The target function,
also known as the "ground truth" or "true function,"
represents the relationship between the input and the output in a dataset. In
other words, it's the actual relationship that you're trying to learn from the
data. In a simple example, let's say you're trying to predict the price of a
house based on its size. The target function in this case would be the real
relationship between the size of the house and its price, which may not be
directly observable but is the underlying pattern you want your machine
learning model to learn.
2. Hypothesis: A hypothesis, in the
context of machine learning, is your model's guess or approximation of the
target function. It's the function that your machine learning algorithm creates
based on the data you provide to it. The goal of training a machine learning model
is to have it learn a hypothesis that can accurately predict or approximate the
target function. In our house price example, your hypothesis might be a
mathematical formula that takes the size of a house as input and estimates its
price as output.
The process of training a machine learning model involves
finding the best possible hypothesis that fits the data you have. This often
involves adjusting the parameters of your hypothesis function to minimize the
difference between the predicted values (generated by your hypothesis) and the
actual values (from the target function) in your training dataset.
Imagine you have a bunch of data points where you know both the
sizes and prices of houses. Your goal is to teach your machine learning model
to learn the relationship between these two factors. You use the data to guide
your model's learning process, helping it create a hypothesis that gets closer
and closer to accurately predicting house prices based on their sizes.
3. Inductive bias and bias-variance trade Off:
How Hypothesis and target class relate with Inductive bias and bias-variance trade-offHypothesis and Target Class: Imagine you're
trying to teach a computer to recognize whether an animal is a cat or a dog
based on pictures. The "target class" here is the true label you want
the computer to learn – either "cat" or "dog." The
"hypothesis" is the computer's guess about whether the animal in a
given picture is a cat or a dog. So, your hypothesis is what your computer
thinks based on the features (like fur, ears, etc.) it observes in the
pictures.
Inductive Bias: Inductive bias is
like a set of assumptions your machine learning algorithm makes about the
problem it's trying to solve. It's like having some initial beliefs about how
things might work. In our animal example, the inductive bias might be that fur,
whiskers, and ears could be important features for differentiating between cats
and dogs.
Bias-Variance Trade-Off: Now, imagine
you're training your computer to identify cats and dogs. The
"bias-variance trade-off" is a balancing act between two things:
- Bias: This is how closely your hypothesis matches
the real target class. If your hypothesis is too simple, it might not be
able to capture the complexities in the data. For instance, if you only
consider the presence of fur, your computer might have trouble distinguishing
between certain cats and dogs.
- Variance: This is how much your
hypothesis changes when you train it on different sets of data. If your
hypothesis is too complex, it might be very sensitive to small changes in
the training data. In our example, if your algorithm tries to memorize
specific patterns in the pictures rather than learning general features,
it might not do well on new pictures it hasn't seen before.
To tie it all
together:
- Inductive bias guides your algorithm's
initial assumptions about the problem.
- Bias relates to how well your hypothesis fits the
target class.
- Variance relates to how much your hypothesis changes
with different training data.
The trade-off is
finding a balance between bias and variance. If your hypothesis is too simple
(high bias), it might not learn the complexities of the problem. If it's too
complex (high variance), it might overfit and struggle with new data.
Imagine it like
Goldilocks finding the right bowl of porridge – not too hot (high bias), not
too cold (high variance), but just right in the middle for the best chance of
getting the answer right!
4. Occam's
razor?
Occam's razor, also known as the principle of parsimony or
Ockham's razor, is a philosophical and scientific principle that suggests that
when there are multiple explanations or hypotheses for a phenomenon, the
simplest one is often the best choice. In other words, among competing
hypotheses that explain the same observations, the one with the fewest
assumptions or entities is more likely to be correct.
Occam's razor is attributed to the medieval philosopher and
theologian William of Ockham, although the principle has been used by various
thinkers throughout history. The principle is often summarized as
"entities should not be multiplied without necessity."
In the context of science and reasoning, Occam's razor
encourages simplicity and elegance in explanations. It suggests that adding
unnecessary complexities to an explanation or hypothesis doesn't necessarily
make it more accurate or valid. Instead, a simpler explanation that accounts
for the observed phenomena without unnecessary embellishments is often
preferred.
In the field of Machine Learning and model building, Occam's
razor can guide the selection of models and features. When choosing between
different models to fit a dataset, or when deciding which features to include
in a model, the principle suggests favoring simpler models and features that
can explain the data adequately. This helps guard against overfitting, where a
model becomes overly complex to fit noise in the training data and fails to
generalize well to new data.
Remember, while Occam's razor is a useful guideline, there are
situations where more complex explanations or models might be necessary to
accurately capture the underlying complexities of a phenomenon. It's a balance
between simplicity and capturing the relevant details.
5. Limitations
of inference machines
Here are some general limitations that apply to various machine
learning models:
1. Limited by Training Data: Machine learning
models learn from the data they are trained on. If the training data is biased,
incomplete, or not representative of the real-world scenarios, the model's
predictions might be inaccurate or unfair.
2. Overfitting: If a model is too
complex, it might fit the training data perfectly but fail to generalize well
to new, unseen data. This is called overfitting. Overfit models might capture
noise in the training data, leading to poor performance on real-world data.
3. Underfitting: On the other hand, if
a model is too simple, it might not capture the underlying patterns in the data
and result in poor performance both on the training and new data. This is
called underfitting.
4. Data Quality and Quantity: The performance of
machine learning models heavily depends on the quality and quantity of data
available for training. Insufficient or noisy data can lead to suboptimal
performance.
5. Interpretable vs. Complex
Models: Complex machine learning models, such as deep neural networks,
can achieve high accuracy, but they are often difficult to interpret. This lack
of interpretability can be a limitation in fields where understanding the
model's decision-making process is crucial.
6. Transferability: Models trained on one
type of data might not perform well when applied to a different, but related,
type of data. This is known as the problem of transferability.
7. Ethical and Bias Concerns: Machine learning
models can inherit biases present in the training data. If the training data
contains biased or unfair patterns, the model might perpetuate those biases in
its predictions.
8. Changing Environments: If the underlying
patterns in the data change over time, the model's performance might
deteriorate. Machine learning models might require periodic retraining to stay
relevant.
9. Dimensionality Curse: As the number of
features (dimensions) in the data increases, the amount of data needed to
generalize well grows exponentially. This can make it challenging to train
accurate models for high-dimensional data.
10. Computational Resources: Some machine learning
algorithms, especially complex ones like deep learning, require significant
computational resources for training and inference. This can limit their
applicability in resource-constrained environments.
11. Lack of Common Sense and
Context: Machine learning models lack common sense reasoning and
contextual understanding, making them prone to making predictions that are
logically correct but contextually inappropriate.
6. Approximation and estimation errors
Approximation and estimation errors are both concepts related to
the accuracy of models, predictions, or measurements, but they arise in
slightly different contexts. Let's break down each term:
Approximation Error: Approximation error
refers to the difference between the actual or true value and the value
estimated or predicted by a model or algorithm. In other words, it's the
measure of how well a model approximates the underlying truth. This error can
arise due to various factors, including the complexity of the model, the amount
of available data, and the inherent limitations of the model's representation.
For example, if you're using a polynomial regression model to
fit a curve to data points, the approximation error would be the difference
between the actual data points and the points on the polynomial curve generated
by the model.
Estimation Error: Estimation error is
closely related to the idea of measuring something, such as estimating a
parameter or quantity of interest from a sample of data. It refers to the
difference between the estimated value and the true value of the parameter
you're trying to measure.
For example, let's say you're estimating the average height of a
certain population by measuring the heights of a sample of individuals. The
estimation error would be the difference between the estimated average height
based on the sample and the actual average height of the entire population.
In the context of statistical inference, estimation error is
often discussed in terms of confidence intervals. A confidence interval
provides a range within which the true value of a parameter is likely to lie.
The width of the confidence interval reflects the estimation error – a wider
interval indicates higher uncertainty in the estimate.
Relationship: The relationship
between approximation error and estimation error depends on the context. In
some cases, they can be closely related. For instance, if you're using a
complex machine learning model to estimate a parameter, the estimation error
might be influenced by the model's approximation capabilities.
Both errors highlight the fact that no model or measurement
process is perfect, and there will always be some discrepancy between the
estimated or predicted values and the true values. Minimizing these errors is a
central goal in various fields, including machine learning, statistics, and
scientific research.
Supervised learning: Linear separability and decision regions, Linear discriminants, Bayes optimal classifier, Linear regression, Standard and stochastic gradient descent, Lasso and Ridge Regression, Logistic regression, Support Vector Machines, Perceptron, Back propogation, Artificial Neural Networks, Decision Tree Induction, Over fitting, Pruning of decision trees, Bagging and Boosting, Dimensionality reduction and Feature selection for skill development and employability.
Monday, December 12, 2022
How to Join a NPTEL Course For June - December 2023 Session
Hello Students
Here I will tell you how you can find, select, and enroll yourself for any
NPTEL course for June - December 2023 Session
Very First
Go to www.onlinecourses.nptel.ac.in and click on the Final Course List. You can find the course list by clicking the following link:
https://docs.google.com/spreadsheets/d/e/2PACX-1vQCbGU35MAoqfECfSQCj22Kj-272L_xGjsxjgNCJWlhYn3yA25jKhX8v_NKQYffH0dSS0LquHhzhTnM/pubhtml?urp=gmail_link
Now choose the Appropriate Course from the Course Name considering your Branch shown as Discipline.
Here you
can find the Detail such as who is the Faculty for that course, the course duration, the course start date, the ending date, exam date with a course joining link.
Click on
the concerned course joining link from the Column Click here to join the course.
A new Web Page Opens click on Join
Button Now.
Now if you don’t have an NPTEL account, Just Sign Up and log in and click on the course joining Link.
When you join any Course, you have to fill out or Update Your Profile.
Don’t forget
to fill in the Name of the Local Chapter State and College/School Name.
Now at Last
Click on both checkboxes and Click on UPDATE PROFILE AND JOIN COURSE.
A confirmation page will open Now.
Thanks for
Reading.
Thursday, May 28, 2020
A journey from Python to Web scraping --> machine Learning --> Artificial Intelligence
Hello, guys, here I am going to tell you all my steps that pushing me towards data science. Here in this post, I am going to tell you the way that I choose and you can follow as well.
So very first it is important that you must have a technical background(at least BCA) and must have a keen interest to learn new technology or programming language. I want to learn python for a long time and when I get time started my own.
Now moved to how to start.
When we start any language course, we always start with how to install that language package. In the same manner when start learning python start right from installation. Next after installation to write code you we need a Python integrated development environment (IDE). Pycharm is best because it provides all builtin functions and saves time in code typing. Best video for installation of python and pycharm is given here:
After successful installation start any free course online that learn you python. I found it through www.educative.io. This website not only providing free python course but also giving program run environment without any installation. Anyone can write code when and where studying on this website and free of cost on any device. The interface of the website is also very user friendly. What else you need? Just start studying and be a python learner. Give at least 20 days to python otherwise, you will dump yourself.
More ways to Run Python Code
Hello once again guys, till now we have seen that by using pycharm python code can be run, It is good to use pycharm for a professional developer. But there are two more ways to run python code.
Without installing python, install anaconda navigator than all the tools are available towards data science and one from the list is Jupyter Notebook. So after installing Anaconda Navigation from the bellow given URL Jupiter is ready to run python code.
It is a web-based interface that is extremely easy.
Now there is one more way to run python code on your machine that is through the Sublime text editor. So first install python and then install sublime text editor from the bellow given URL.
This is a lightweight way to run python code. And working is nearly the same as pycharm.
So summarizing my today's post if going for anaconda Navigator don't install python while going for Pycharm OR Sublime Text 3 install python as per operating system.
Free Python Ebooks To Download for Reading
Books play a significant role in our life. They say that “When you open a book, you open a new world”. Books are packed with knowledge, insights into a happy life. I have observed that by reading a book, you can dive much deeper into whatever you want to learn. In the case of video, the learning is a bit quicker but not that in-depth because books allow room for imagination.
It all is also true about python programming so I am here giving some links to download python books free to read. These books are not large in size so easy to read on any device.
The appeal of Python is in its simplicity and beauty, as well as the convenience of the large ecosystem of domain-specific tools that have been built on top of it.
The Python ecosystem is vast and far-reaching in both scope and
depth. Starting out in this crazy, open-source forest is daunting, and
even with years of experience, it still requires continual effort to
keep up-to-date with the best libraries and techniques.
There are two major versions of the Python programming language:
the Python 2.x series, and the newer Python 3.x series.
Python is most definitely not a “pure functional programming lan‐
guage”; side effects are widespread in most Python programs. That
is, variables are frequently rebound, mutable data collections often
change contents, and I/O is freely interleaved with computation.
Subscribe to:
Posts (Atom)