Overview
Course Description
A hands-on, comprehensive journey into modern Data Science and Machine Learning. Learn how to clean and analyze complex datasets, visualize findings with Matplotlib and Seaborn, train regression and classification models using Scikit-Learn, and build neural networks with TensorFlow and Keras.
What you'll learn
- Clean, filter, and aggregate multi-gigabyte datasets with Pandas & NumPy
- Build and evaluate regression, decision tree, and random forest models
- Implement convolutional neural networks for computer vision with TensorFlow
- Deploy machine learning models as production REST APIs using FastAPI
Requirements
- Basic mathematics and logical thinking
- No previous Python experience required - covered from scratch
Course Content
6 Lectures 145 min
Module 1: Data Wrangling with NumPy & Pandas 3 lessons
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NumPy Multi-Dimensional Arrays & Vectorization
Preview18:10
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Pandas DataFrames: Cleaning, Joins & GroupBy
29:45
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Exploratory Data Analysis with Seaborn & Plotly
21:30
Module 2: Machine Learning Algorithms with Scikit-Learn 3 lessons
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Linear & Logistic Regression with Regularization
24:20
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Tree Models: Decision Trees, Random Forests & XGBoost
31:10
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Model Evaluation: ROC-AUC, Confusion Matrix & Cross-Validation
19:50
About the Instructor
Full Stack Developer & Educator
0 Courses
Experienced instructor teaching web development for 8+ years.
Frequently Asked Questions
Do I need a GPU to run the code in this course?
No, all code can be executed on Google Colab with free cloud GPUs provided.
Will I learn how to deploy ML models?
Yes, we include a project showing how to wrap models with FastAPI and Docker.
Reviews & Comments (1)
harshita saini
4 days ago[Subject: for testing]
Very good just average becuase video quality is not good .