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How to learn au with machine learning?

I want to work in MNC like Google, meta, Amazon, IBM, BLACK ROCK, microsoft etc I want make expertise in this field and grab opportunity.

technology careeradvice careerpath datascience artificialintelligence machine-learning deeplearning faang
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Roger’s Answer

Hello! Here's an exciting path to becoming an AI and machine learning expert. It's perfect for high school students, and no previous experience is needed.

AI & Machine Learning Learning Path (High School)

Stage 1: Build the Basics (1–2 months)

Start with some essential foundations.

1. Math (important!)

Focus on:
- Algebra (equations, variables)
- Basic statistics (mean, median, probability)
- Intro to linear algebra (vectors, matrices)

Resources:
- Khan Academy (free)
- School math courses are a great start

2. Learn Python (main ML language)

Begin coding with:
- Variables, loops, functions
- Lists, dictionaries
- Basic problem solving

Beginner resources:
- freeCodeCamp (Python course)
- CS50 (intro programming)
- Codecademy or W3Schools

Stage 2: Intro to Machine Learning (2–3 months)

Once you're comfortable with Python:

3. Learn ML concepts

Understand:
- What is Machine Learning?
- Types:
- Supervised learning
- Unsupervised learning
- Common models:
- Linear regression
- Decision trees
- KNN

Beginner-friendly courses:
- Andrew Ng’s Machine Learning course (Coursera)
- Google’s Machine Learning Crash Course

4. Learn libraries used in ML

Start using real tools:
- numpy → math
- pandas → data handling
- matplotlib → visualization
- scikit-learn → ML models

Stage 3: Build Projects (Most important step)

Start small:

Easy projects:
- Predict house prices
- Spam email classifier
- Movie recommendation system

Tools:
- Jupyter Notebook / Google Colab (free)

Tip: Projects are what colleges and jobs care about most.

Stage 4: Level Up to AI (3–6 months)

5. Learn Deep Learning

- Neural networks
- How models learn (training)

Tools:
- TensorFlow or PyTorch

Courses:
- DeepLearning.AI (beginner tracks)
- YouTube: 3Blue1Brown (great visuals)

6. Specialize (pick something fun)

Choose a direction:
- Computer Vision (images)
- Natural Language Processing (chatbots)
- AI for games
- Data science

Stage 5: Build a Portfolio

Create 3–5 projects like:
- Chatbot
- Image classifier
- Stock price predictor (simple)
- AI game bot

Upload to:
- GitHub (very important)
- Personal website (optional)

Weekly Study Plan (Example)

- 30–60 minutes/day:
- 3 days coding
- 2 days theory
- 1 day project
- 1 day review/rest

Pro Tips

- Start SIMPLE — don’t rush into deep learning
- Practice coding regularly (consistency beats intensity)
- Don’t just watch tutorials → build things
- Join communities:
- Reddit (r/learnmachinelearning)
- Discord groups
- Kaggle (competitions)

Best Beginner Stack (What to actually use)

- Python
- Google Colab
- scikit-learn
- Later: PyTorch

Final Goal (1 year)

By the end, you should be able to:
- Understand ML concepts
- Build real projects
- Explain your work
- Have a small portfolio

Good luck on your journey! You can do it!
Thank you comment icon Thank you for giving me advice. RAVI
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Sarah’s Answer

The resources described above are great! I would add that the field of data science and especially generative AI is changing at an extremely rapid pace. I would try to spend a little time every week seeing what is new in the field -- maybe just following AI news reddit threads or AI newsletters to see what is new can help you stay informed.
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Gulcan’s Answer

Hi Ravi, I would advise taking one of the AWS/Google Cloud courses to gain certifications within this field, such as the Google Cloud Digital Leader one.
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Suresh’s Answer

Start by mastering one programming language. Then, dive into data structures, algorithms, and competitive programming. Build projects and seek internships to gain practical experience. Learn about system design, and develop networking skills along with cultural intelligence. Apply strategically for opportunities and prepare thoroughly for both technical and behavioral interviews. Keep learning and stay active on platforms like GitHub and LinkedIn to increase your visibility.
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Yusufali’s Answer

Ravi, while building technical skills is crucial, remember that joining companies like Google, Meta, Amazon, Microsoft, IBM, or BlackRock involves more than just learning AI/ML. It's about being noticed, forming connections, and showing real passion for the field.

To network effectively, start by creating a strong LinkedIn profile. Keep it simple yet impactful with:

- a clear headline
- a brief summary of your interests in AI/ML
- details of projects, certifications, hackathons, or Kaggle work
- links to your resume and GitHub, if you have them

Also, consider connecting with:

- alumni from your school or college
- people working in data, AI, or software roles
- recruiters and interns at these companies
- professionals you meet at events, webinars, or hackathons

When you reach out, do so respectfully. Instead of immediately asking for a job, seek advice on how they started, which skills are most important, or which projects helped them stand out.
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PARTH’s Answer

Assuming you mean AI/ML: the best path is to learn foundations first, then build real projects, then target company-specific skills. Python, SQL, statistics, linear algebra, ML basics, and deep learning are the core stack almost every early-career AI/ML role expects. Strong candidates also now need some exposure to LLMs/GenAI and MLOps because companies want people who can deploy and maintain models, not just train them in notebooks.
A smart roadmap:

Step 1: Learn Python, SQL, DSA, probability, statistics, and linear algebra well.
Step 2: Learn ML properly: regression, classification, clustering, feature engineering, model evaluation, overfitting, and regularization.
Step 3: Learn deep learning with PyTorch or TensorFlow.
Step 4: Add GenAI skills: prompting, RAG, LangChain/LlamaIndex, agents.
Step 5: Learn deployment/MLOps: APIs, Docker, CI/CD, MLflow, cloud basics. This is a big differentiator.
Step 6: Build 3 strong projects and put them on GitHub with clean README, demo, and metrics. That matters more than just certificates.
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Sandeep’s Answer

Hello Ravi,

Start by building strong fundamentals in programming, mathematics, and data analysis. Learn Python first, then move into machine learning concepts like data preprocessing, model training, and neural networks.

After that, work on real projects and share them on GitHub/Bitbucket. Companies like Google, Meta, and Microsoft usually look for strong problem-solving skills, practical experience, and good understanding of computer science fundamentals.
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Ashar’s Answer

It's awesome that you're interested in AI and machine learning! To get started, it's important to build a strong base in programming, data analysis, math, and problem-solving. These are the key skills needed for many tech jobs.

From my experience at IBM and now at Deloitte, I've learned that AI and machine learning are most useful when they solve real business problems. Companies use these technologies to make better decisions, work more efficiently, lower risks, and improve customer experiences.

So, while it's important to learn the technology, also take time to understand how businesses use it to create value. The most successful people are those who can connect their technical skills with real-world needs.

Instead of focusing only on landing a job at one company, work on building your skills, creating projects, staying curious, and always learning. This mix of technical know-how and business sense can help you find opportunities at companies like IBM, Microsoft, Amazon, Google, and many others.
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Terry’s Answer

Here's something important to think about: if you dive into GenAI too quickly without knowing the basics, you might seem cool but not very strong. On the other hand, if you only focus on theory and never work on real projects, you might seem smart but not ready for a job. The best candidates are well-rounded, with skills in engineering, machine learning, and deployment.

A good next step is to start learning Python and SQL, and try completing a simple machine learning project this month. If you like, I can help create a day-by-day plan for the next three months or a specific plan for working at Google, Meta, or Amazon.
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Vidhya’s Answer

Learning AI and machine learning is absolutely achievable if you approach it gradually. Here’s a great way to begin and develop valuable skills:

1. Improve your Python skills.
2. Understand the math behind machine learning, such as algebra, probability, basic statistics, graphing, and functions.
3. Explore beginner-friendly courses like Harvard's CS50 Introduction to AI, Google's Machine Learning Crash Course, and Kaggle's free "Intro to ML" lessons.
4. Create small projects using tools like NumPy and Pandas.
5. Participate in ML communities or competitions.
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Olga’s Answer

Hello,
Apart from the resources for study I would've recommended to checkout competitions on Kaggle. This websites offers different machine-learning (many of them are close to real life) challenges in a competition form.
It is also widely recognized in the Machine Learning world and you will be able to share your profile with interviewers.
Good luck!
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Diana’s Answer

Use AI daily to keep up with its fast changes. Stay informed by reading articles, listening to podcasts, or watching YouTube videos about new trends and technology. Attend seminars on LinkedIn and follow industry leaders on social media, especially LinkedIn.
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Aquie’s Answer

Hi Ravi,

I recommend starting with Python and exploring how AI and ML are used in different business scenarios. I really like the Anthropic Claude platform, so I suggest trying it out and learning about Claude and Claude code.
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Disha’s Answer

Dive into learning Python, math, statistics, machine learning, and deep learning basics. Work on real-world projects, join competitions, and boost your coding and problem-solving skills. Building a strong portfolio and preparing well for interviews can open doors to AI/ML roles at top companies like Google, Meta, Amazon, Microsoft, IBM, and BlackRock.
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