<ul>
	<li>Job Profile: Machine Learning Engineer</li>
</ul>

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Are you ready to break into the world of Machine Learning and Data Science? Whether you&#39;re a beginner or someone looking to sharpen your ML skills, this course provides a complete, practical, and beginner-friendly introduction to machine learning using Python and essential libraries.
<h3>Course Content:</h3>
1. Introduction
<ul>
	<li>Welcome</li>
	<li>What is AI, ML, DL, Neural Network</li>
	<li>How Machine Learn</li>
	<li>Supervised vs Unsupervised Learning</li>
	<li>Real World Applications of ML</li>
</ul>
2. Getting Ready
<ul>
	<li>Environment Setup</li>
	<li>Basics of Python</li>
	<li>Jupyter Notebook</li>
	<li>Google Colab</li>
	<li>Pandas Library</li>
	<li>Data Preprocessing</li>
	<li>Statistics</li>
	<li>Matplotlib</li>
	<li>Numpy</li>
	<li>Scikit-Learn</li>
</ul>
3. Concepts
<ul>
	<li>ML Lifecycle</li>
	<li>What is a Model</li>
	<li>Features &amp; Targets</li>
	<li>Train &amp; Test Split</li>
	<li>Feature Engineering</li>
	<li>Label Encoding</li>
	<li>Categorical Encoding</li>
	<li>One Hot Encoding</li>
	<li>Ordinal Encoding</li>
</ul>
4. Datasets
<ul>
	<li>What are datasets</li>
	<li>Some popular datasets</li>
	<li>Scikit-learn Dataset</li>
	<li>kaggle</li>
	<li>HuggingFace</li>
	<li>UCI Repository</li>
	<li>Data from Apis</li>
	<li>Web Scraping</li>
</ul>
5. Model Evaluation
<ul>
	<li>Hyperparameters</li>
	<li>Overfitting &amp; Underfitting</li>
	<li>Accuracy</li>
	<li>Confusion Matrix</li>
	<li>Precision &amp; Recall</li>
	<li>MSE</li>
	<li>Regularisation</li>
</ul>
6. Supervised Learning
<ul>
	<li>Regression vs Classification</li>
	<li>Linear Regression Algo</li>
	<li>Model Save &amp; Reload</li>
	<li>Model Training</li>
	<li>Gradient Descent</li>
	<li>Epochs</li>
	<li>Logistic Regression Algo</li>
	<li>KNN Algo</li>
	<li>Decision Tree</li>
	<li>Random Forest</li>
	<li>Polynomial Regression</li>
	<li>SVM</li>
	<li>Gradient Boosting</li>
</ul>
7. Unsupervised Learning
<ul>
	<li>Clustering vs Association</li>
	<li>K-Means Clustering</li>
	<li>DBSCAN</li>
	<li>PCA (Principal Component Analysis)</li>
	<li>t-SNE</li>
</ul>
8. Model Deployment
<ul>
	<li>Serialization</li>
	<li>Model Rest API</li>
	<li>Deployment</li>
</ul>
9. Projects
<ul>
	<li>Project 1</li>
	<li>Project 2</li>
</ul>
10. Interview Questions
<ul>
	<li>Disucssion on interview questions</li>
</ul>
&nbsp;
<h3>🌟 Course Highlights</h3>
<ul>
	<li>
	🎥 70+ video lessons and 2 hands-on projects included in the course
	</li>
	<li>
	🚀 3 new videos uploaded every week to keep the content fresh and updated
	</li>
	<li>
	📚 48&nbsp;videos already available for instant access after enrollment
	</li>
	<li>
	🗓️ Course completion by March 2026
	</li>
	<li>
	💬 Exclusive Telegram group for enrolled students to discuss and connect
	</li>
	<li>
	🚀&nbsp;Free Resume &amp; LinkedIn Review by me
	</li>
	<li>
	✅ No Maths required &mdash; simple and practical explanations
	</li>
	<li>
	🧠 No prior knowledge of AI, ML, or Python needed &mdash; we start from zero
	</li>
	<li>
	🎯 AI &amp; ML interview preparation covered in the final module
	</li>
	<li>
	☁️ No high-end machine needed &mdash; all coding done on the cloud
	</li>
	<li>
	💻 Code snippets provided for every concept to make learning easier
	</li>
	<li>
	🎥 Doubt-solving support &mdash; if needed, topics will be re-recorded with detailed explanations
	</li>
	<li>
	🏆 Course completion certificate provided
	</li>
	<li>
	📱 Learn anytime, anywhere with our mobile app
	</li>
	<li>
	♾️ Lifetime access to all course content
	</li>
	<li>
	🗣️ Course language: Hinglish (Hindi + English mix)
	</li>
	<li>
	💰 Refund Policy:&nbsp;100% refund within 7&nbsp;days of purchase
	</li>
</ul>
&nbsp;
Connect with me on my free telegram group for more guidance :&nbsp; https://t.me/+QI-dh4JVrrllZGI1
&nbsp;
Happy Learning !!
&nbsp;
<h3>&nbsp;</h3>
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&nbsp;

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