Bc

As well...

Fake.

please continue

I have a question

I am straight out of a software engineering bootcamp, but I applied for a scholarship to learn AI programming with python which will provide some foundational concepts and libraries used, with all the expectations that as software engineers we will still need AI ans ML skills, but my question is on the timing, cause I am still quite early in my career and haven't mastered software engineering well yet, I am still trying to learn and build projects to get to a point where I am confident that I can apply for roles....so is it wise to take up AI this early or should I first of all have some experience with software engineering, I would like to explore the field of AI and ML for curiosity but considering I am at a place where I need a job soon I really need to account for every second I use and be accurate how invest myself, I love the industry but the expectations sometimes are just draining😵‍💫😵‍💫

That Great video in India. I am now student I wana start semiconductor and ai machine learning can be much better to learn and start in India. Semiconductor is growing Hope all people with idea succeed and make their nation strong 💪 love from India ❤

This is awesome and amazing! Smitha is very clear, and explains things well. Finally someone that can teach a boring subject with enthusiasm! Keep the videos coming!

💖

You aren't even a ML Engineer though

What about matrices? Considering most calculations are on matrices.

😢 in Iran the ai developer salary is 530$ per month.

thank you

OpenAI 1000K

Self taught but you have a masters in computer science.

Please use external mic next time. It was hard to understand

❤

Thanks for series! Loving it. One way to use the kaggke dataset is to use the notebook that comes with kaggle. This will eliminate the need to download the datasets and connect to google-colabs.

Quiz answers 1) B - At optimal value loss is minimum. 2) B - To handle negative values.

Right has higher loss. The points that aren't on the line are farther away which makes the loss bigger,

Why did you stopped 100 days of machine learning series ?

I got some value error with this method. The updated method is def salary_model(y_new): # Create the training data xs = np.array([0, 1, 2, 3, 4, 5, 6], dtype=float) ys = np.array([0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1], dtype=float) # Define the model model = keras.Sequential([keras.layers.Dense(units=1, input_shape=[1])]) # Compile the model model.compile(optimizer='sgd', loss='mean_squared_error') # Train the model model.fit(xs, ys, epochs=50) # Ensure input for prediction is a 2D array y_new = np.array([[y_new]]) # Convert to a 2D array # Predict for the reshaped input return model.predict(y_new)[0][0]

Yes, but doesn't becoming an AI engineer require first getting a bachelor's in data science and then a minor in software engineering?

Where to search job

Omg your beautiful. Its distracting lol.

She’s back! 🎉

🐍

I am new to machine learning. I have a question, how did you get to know that this is an NLP problem?

He'll ma'am I'm Tabish ,I am pursuing diploma in chemical engineering from Delhi skill and entrepreneurship University in 2025. I want to learn Machine learning mastery ( Tutorial) . I saw your KZbin channel, I hope I understand, please help me a little more.🙂

Could you please suggest me a book for NLP ?

It's a BS regarding the availability of jobs for entry-level engineers. You will struggle hard to get your first job and be ready to spend more than a year in search

Pls make a video for masters programme in ai and ml in canada and usa

Is it still relevant to learn ML and its core mathematical concepts, given that LLMs are just an API call away?

Build a solid portfolio, network to attract clients, and keep up with market changes. To achieve success, deliver high-quality work and communicate well.

To enter the job market in machine learning, it is essential to understand the fundamentals of artificial intelligence and models like GPT. There are 4-month courses and bootcamps that cover these concepts. To transition into machine learning operations roles, is it necessary to delve deeply into machine learning, or is a basic understanding sufficient to quickly move to projects involving generative AI and cloud-based machine learning operations? What projects can provide the most learning and attract recruiters' attention? Additionally, do you think the ability to reproduce and share results with data interpretation, using tools like Quarto with R and Python, will hold significant weight in the future? What machine learning projects would be most beneficial for a beginner to advance their learning and achieve employment or professional growth?

Yesss😂 keep this coming...love there information....

Where can I get the github link ?

can we find remote ML jobs from India?.

Indians always make the hard stuff easy and simple 😂

Finally she remembered that she has a YT channel😶‍🌫️

I was just about to message you on LinkedIn to express how much I love your content and to encourage you to keep creating video lectures on KZbin. And then I saw your new video-perfect timing!👍

Good to see you back after a long gap of around one year. 😊

Thank you 🙏🏼

🎓 Start learning machine learning with the 'Introduction to Machine Learning with Python for Beginners' course, co-designed by me to give you practical, hands-on experience from day one. 🚀www.schoolofmachinelearning.com/introduction-to-machine-learning-with-python-for-beginners/

To be a successful freelance machine learning engineer, start by building a strong portfolio with diverse, high-impact projects that showcase your skills. Network actively to connect with potential clients and peers, and continually update your expertise to stay ahead in a rapidly evolving field. With Smythos, you'll gain access to cutting-edge resources and a supportive community to enhance your journey. With a blend of technical prowess, effective communication, and adaptability, you'll thrive in the freelance world and unlock exciting opportunities!

The fastest way to become a machine learning engineer is to combine focused learning with practical experience. Start with foundational courses on platforms like Coursera or edX, such as Andrew Ng’s Machine Learning course. Simultaneously, work on real-world projects and Kaggle competitions to build a strong portfolio. Networking with professionals and applying for internships will also accelerate your journey into the field.

Keep in mind that there is no such thing as an "entry level" ML engineer. You need massive ML experience for any ML role, nobody is trusting you with their data otherwise. Refer to "the data janitor" for more.

Some people are so poor all they have is money. Working on something that will ultimately be the end of human civilization really isn't something to brag about. But I understand, your love of money is all that brings you comfort in this world.

In usa not in italy!😂

super helpful! Thank you