<h2>About this course</h2>
<p><strong>100 Days of ML</strong> is India's most comprehensive free machine learning series, created by Nitish Singh of CampusX. Taught in Hindi and English, this 80-hour journey covers everything from foundational statistics and Python to <a href="/topics/neural-networks">neural networks</a> and real-world ML projects—no paywalls, no shortcuts.</p>
<p>CampusX has built a reputation for making complex tech accessible to Indian learners. This playlist condenses what might take months of scattered tutorials into a structured, step-by-step path that thousands have followed to land their first ML roles.</p>

<h2>What you'll learn</h2>
<ul>
<li>Master Python for data science, including NumPy, Pandas, and data manipulation techniques</li>
<li>Build intuition in statistics, probability, and linear algebra—the math behind ML</li>
<li>Train, evaluate, and tune classification and regression models (logistic regression, decision trees, random forests, SVM)</li>
<li>Understand and implement neural networks and <a href="/topics/deep-learning">deep learning</a> fundamentals</li>
<li>Handle real datasets: cleaning, exploratory analysis, feature engineering, and dealing with imbalanced data</li>
<li>Deploy models and understand the ML pipeline from raw data to production insights</li>
<li>Apply your knowledge through hands-on projects that mirror actual industry problems</li>
</ul>

<h2>Who this is for</h2>
<p>If you're curious about machine learning but intimidated by the jargon and cost, this course is built for you. It works whether you're learning alone, preparing for a job switch, or building a portfolio to land your first ML internship.</p>
<ul>
<li><strong>College <a href="/for/students">students</a></strong> — build ML expertise while in school, stand out in campus placements, and start freelancing early</li>
<li><strong>Early-career professionals</strong> — transition into data science or ML roles without expensive bootcamps; learn at your own pace alongside your current job</li>
</ul>

<h2>Prerequisites</h2>
<p>None—this course is beginner-friendly. You'll pick up Python as you go; basic comfort with math (high school algebra) helps but isn't essential. Curiosity and patience matter more than prior coding experience.</p>

<h2>Why this matters for Indian learners</h2>
<p>Machine learning jobs in India are growing fast. Companies across fintech (Razorpay, Paytm), e-commerce (Flipkart, Amazon India), and startups actively hire ML engineers—and salaries range from ₹6–12 lakhs annually for junior roles, scaling to ₹20+ lakhs with experience.</p>
<p>This course gives you the foundational skills employers expect without requiring you to spend ₹2–5 lakhs on bootcamps. Hindi-English instruction matters too: you can learn in whichever language clarifies concepts best, a luxury not always available in English-only courses.</p>

<h2>Frequently asked questions</h2>
<h3>Is this course really free?</h3>
<p>Yes. It's hosted on YouTube and entirely free—no hidden charges, no premium tier. All 100 days of content, all videos, all notebooks are accessible at zero cost.</p>
<h3>How long will it take to complete?</h3>
<p>The course is 80 hours of video. If you dedicate 10–12 hours per week (roughly 2 hours on weekdays, 3 hours on weekends), you'll finish in 6–8 weeks. Many learners take longer and that's fine—ML is dense, and taking time to practice is better than rushing.</p>
<h3>Will I get a certificate?</h3>
<p>No formal certificate is issued. However, the projects and hands-on work you complete build a portfolio that matters far more to employers than a certificate. Share your GitHub repos and project writeups—that's your real proof.</p>

Nitish Singh's Hindi-English ML/DL YouTube playlists.

CampusX

Nitish Singh's epic 100-day machine learning playlist in Hindi-English — most comprehensive free ML series in India.

100 Days of ML (CampusX)

<h2>About this course</h2>
<p>CampusX's <a href="/topics/deep-learning">Deep Learning</a> Playlist is a comprehensive journey through the foundations and frontiers of deep learning, taught in a mix of Hindi and English for maximum accessibility. From the basics of perceptrons to cutting-edge transformer architectures, this playlist builds your understanding methodically, with real code examples and intuitive explanations. CampusX has earned a strong reputation in India's self-taught tech community for making complex concepts tangible and practical.</p>

<h2>What you'll learn</h2>
<ul>
  <li>How perceptrons and <a href="/topics/neural-networks">neural networks</a> form the backbone of deep learning, and how to implement them from scratch</li>
  <li>Convolutional Neural Networks (CNNs) for image recognition and <a href="/topics/computer-vision">computer vision</a> tasks</li>
  <li>Recurrent Neural Networks (RNNs), LSTMs, and GRUs for sequential data and time-series problems</li>
  <li>Attention mechanisms and Transformer architectures that power modern <a href="/topics/llm">large language models</a></li>
  <li>Practical training techniques: backpropagation, optimization (SGD, Adam), and regularization methods</li>
  <li>How to preprocess data, build models in popular frameworks, and evaluate performance rigorously</li>
  <li>Real-world applications of deep learning in <a href="/topics/nlp">NLP</a>, computer vision, and beyond</li>
</ul>

<h2>Who this is for</h2>
<p>This course suits anyone serious about moving beyond machine learning basics into the depth required for AI roles. You should be comfortable with Python and linear algebra fundamentals—if those feel rusty, a quick review beforehand will help.</p>
<ul>
  <li><strong>Engineering <a href="/for/students">students</a></strong> — master deep learning before internship season, and stand out in campus placements at tech companies</li>
  <li><strong>Self-taught programmers and career switchers</strong> — acquire the skills employers expect for junior ML engineer and AI researcher roles</li>
</ul>

<h2>Prerequisites</h2>
<p>Solid grasp of Python programming, basic linear algebra (vectors, matrices, multiplication), and familiarity with NumPy or similar libraries. If you're shaky on math fundamentals, budget extra time for those sections—the instructors explain well, but prior exposure helps.</p>

<h2>Why this matters for Indian learners</h2>
<p>Deep learning expertise opens doors across India's booming AI sector. Startups in Bangalore, Delhi, and Mumbai are hiring ML engineers aggressively, and roles at companies like Flipkart, Amazon India, and Google India actively seek candidates with transformer and CNN knowledge. Salaries for mid-level deep learning engineers in India start around ₹10–15 LPA and climb steeply with experience. Learning in Hindi and English also removes a barrier many Indian learners face with purely English-medium content, so you can absorb concepts faster.</p>

<h2>Frequently asked questions</h2>
<h3>Is this course really free?</h3>
<p>Yes, completely free. CampusX hosts the entire playlist on YouTube with no paywalls or hidden fees.</p>
<h3>How long will it take to complete?</h3>
<p>The course is designed as 60 hours of content. If you dedicate 8–10 hours per week, you'll finish in about 6–8 weeks. Learners often spend extra time on projects and coding exercises, which we recommend—that's where deep learning truly sinks in.</p>
<h3>Will I get a certificate?</h3>
<p>This course does not offer an official certificate. However, completing the projects and building a portfolio of deep learning work (shared on GitHub) carries far more weight with employers than a course certificate.</p>

CampusX's deep learning playlist — perceptrons to transformers, in Hindi-English.

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