<h2>About this course</h2>
<p>This Hindi-language machine learning playlist from CodeBasics breaks down ML concepts in a way that actually makes sense—no English jargon walls, no leaps in logic. Dhaval Patel is known in the Indian developer community for clear explanations paired with hands-on coding examples, making this course ideal if you learn better in Hindi and want to avoid the friction of English-medium technical content.</p>

<h2>What you'll learn</h2>
<ul>
<li>Foundational ML concepts: what machine learning is, how it differs from traditional programming, and where it fits in real-world projects</li>
<li>Supervised learning algorithms: linear regression, logistic regression, and decision trees—with practical coding walkthroughs</li>
<li>Data preprocessing: cleaning, normalizing, and preparing datasets so your models actually work</li>
<li>Model evaluation: how to measure accuracy, prevent overfitting, and choose between competing models</li>
<li>Hands-on Python coding: writing ML pipelines using libraries like scikit-learn and pandas</li>
<li>End-to-end project approach: taking a raw problem from idea to a trained, tested model</li>
<li>Common pitfalls and debugging: troubleshooting that goes beyond theory, grounded in real mistakes</li>
</ul>

<h2>Who this is for</h2>
<p>You're a learner who's comfortable with basic Python and wants to level up into machine learning—but you've been intimidated by English-heavy courses or textbooks. This course meets you where you are, in Hindi, with practical examples you can immediately code along with.</p>
<ul>
<li><strong><a href="/for/students">Students</a> and freshers</strong> — build a concrete ML portfolio project before your first job interview, giving you a real edge in competitive hiring</li>
<li><strong>Self-taught <a href="/for/developers">developers</a></strong> — bridge the gap from coding tutorials to applied machine learning with step-by-step guidance in your native language</li>
</ul>

<h2>Prerequisites</h2>
<p>Basic Python knowledge (loops, functions, libraries like NumPy). No prior ML or statistics background needed—this course builds from the ground up.</p>

<h2>Why this matters for Indian learners</h2>
<p>Machine learning skills are in high demand across Indian tech hubs—from Bangalore startups to FAANG offices in Pune and Hyderabad. Companies like Flipkart, Swiggy, and Microsoft India actively hire ML engineers, and salary expectations for ML-competent developers are significantly higher than general software engineers. Learning in Hindi removes a language barrier that often discourages talented Indian programmers from entering the field, letting you compete faster and with more confidence.</p>

<h2>Frequently asked questions</h2>
<h3>Is this course really free?</h3>
<p>Yes. It's a free YouTube playlist. No hidden paywalls, no "premium" version you need to unlock.</p>
<h3>How long will it take to complete?</h3>
<p>The full course is about 35 hours. If you dedicate 5–7 hours per week and code along (don't just watch), you'll finish in 5–7 weeks. Learning sticks better when you type the code yourself, so pace yourself accordingly.</p>
<h3>Will I get a certificate?</h3>
<p>No formal certificate from this course. But you will have real projects in your portfolio—code on GitHub, models you've trained—which matter far more to employers than a certificate.</p>

Dhaval Patel's Hindi-English ML tutorials.

CodeBasics

Dhaval Patel's Hindi-only ML playlist — clear explanations, hands-on coding.

Machine Learning in Hindi (CodeBasics)

<h2>About this course</h2>
<p><a href="/topics/deep-learning">Deep learning</a> has transformed everything from image recognition to natural language processing—and this CodeBasics playlist breaks it down into digestible pieces. You'll move from neural network fundamentals through CNNs, RNNs, and GANs, all using <a href="/topics/tensorflow">TensorFlow</a>. CodeBasics brings real clarity to concepts that often feel abstract, making this an ideal bridge between basic machine learning and production-level deep learning work.</p>

<h2>What you'll learn</h2>
<ul>
  <li>Build and train feedforward <a href="/topics/neural-networks">neural networks</a> from scratch using TensorFlow</li>
  <li>Understand convolutional neural networks (CNNs) and apply them to image classification tasks</li>
  <li>Work with recurrent neural networks (RNNs) for sequence data and time-series problems</li>
  <li>Implement generative adversarial networks (GANs) and understand their architecture</li>
  <li>Optimize model performance through backpropagation, loss functions, and hyperparameter tuning</li>
  <li>Debug common deep learning pitfalls: overfitting, vanishing gradients, and training instability</li>
  <li>Deploy models and understand when to use each architecture for real-world problems</li>
</ul>

<h2>Who this is for</h2>
<p>You're ready for this if you've got Python basics and understand machine learning fundamentals. This course works best for people who want to move beyond shallow learning techniques into the deep learning tools that power modern AI applications.</p>
<ul>
  <li><strong>ML engineers building career depth</strong> — master the architectures you'll encounter in production systems and job interviews</li>
  <li><strong><a href="/for/data-scientists">Data scientists</a> expanding skill scope</strong> — add deep learning to your toolkit for <a href="/topics/computer-vision">computer vision</a> and sequential data problems</li>
</ul>

<h2>Prerequisites</h2>
<p>Solid Python programming skills and familiarity with basic machine learning concepts (training/test splits, overfitting, gradient descent). Linear algebra basics help but aren't essential—CodeBasics explains what you need as you go.</p>

<h2>Why this matters for Indian learners</h2>
<p>Deep learning roles in India are among the highest-paying AI positions, with companies like TCS, Infosys, Flipkart, and startups like Instadeep actively hiring. Whether you're aiming for roles in computer vision (autonomous systems, medical imaging), <a href="/topics/nlp">NLP</a> (text analytics, chatbots), or <a href="/topics/generative-ai">generative AI</a>, deep learning is now table stakes. This free course gives you the foundation without the ₹50,000–₹2,00,000 price tag of premium bootcamps.</p>

<h2>Frequently asked questions</h2>
<h3>Is this course really free?</h3>
<p>Yes, completely free. CodeBasics publishes on YouTube with no paywalls or hidden costs.</p>
<h3>How long will it take to complete?</h3>
<p>The playlist is around 30 hours of video. At a comfortable pace of 5–6 hours per week, you're looking at 5–6 weeks. Don't rush—deep learning clicks better when you pause and tinker with code.</p>
<h3>Will I get a certificate?</h3>
<p>No official certificate is offered, but the real credential is what you can build and explain in interviews. Many learners create a GitHub portfolio with projects based on what they learn here.</p>

CodeBasics' deep learning playlist — neural nets, CNNs, RNNs, GANs in TensorFlow.

Deep Learning Tutorials (CodeBasics)

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9 free AI courses from Google — covering generative AI, LLMs, prompt engineering, ML basics, and more.

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