Complete Machine Learning With Python Hands-On Masterclass

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Complete Machine Learning With Python: Hands-On Masterclass
Published 8/2026
Created by Abdel Thomas
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 44 Lectures ( 6h 16m ) | Size: 3.4 GB
Machine Learning with Algorithmics & Python: Pandas, Numpy, Scikit-learn, FastAPI, Docker & Real-World Projects
What you'll learn
⚡ Build a complete machine learning workflow in Python, from data preparation and feature engineering to model training and evaluation.
⚡ Apply statistical analysis, data cleaning, scaling, encoding, feature selection, and PCA to prepare reliable datasets.
⚡ Train and compare classification and regression models, including Decision Trees, SVM, LR, Random Forest, Boosting, Clustering, and ensemble methods.
⚡ Complete real-world machine learning Labs & projects.
⚡ Build an end-to-end machine learning project, track experiments with MLflow, and deploy the model as a FastAPI API and Dockerized web app.
⚡ Master Python libraries for Machine Learning, including NumPy, Pandas, Matplotlib, Seaborn, and Scikit-learn.
⚡ Train, evaluate, and optimize classification, regression, clustering, and ensemble learning models.
⚡ Tune machine learning models with Grid Search, Random Search, and hyperparameter optimization.
Requirements
❗ Basic knowledge of Python is helpful, but the essential concepts will be explained during the course.
❗ No advanced mathematics or statistics knowledge is required.
❗ Basic computer skills and a willingness to learn are enough to get started.
Description
Ready to master Data Science, Artificial Intelligence, and Machine Learning? You're in the right place!
Welcome to one of the most comprehensiveMachine Learning with Python courses designed to take you from beginner to job-ready. Whether you're starting from scratch or looking to strengthen your skills, this course combines clear explanations with extensive hands-on practice to help you masterData Science, Artificial Intelligence, and Machine Learning.
You'll learn the complete machine learning workflow, including data preprocessing, exploratory data analysis, feature engineering, feature selection, dimensionality reduction (PCA), model training, evaluation, hyperparameter tuning, and deployment. Throughout the course, you'll complete15+ practical labs using industry-standard tools such asNumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, MLflow, FastAPI, and Docker.
To reinforce your learning, you'll build areal-world end-to-end Machine Learning project where you'll apply everything you've learned-from preparing raw data to training, optimizing, tracking, and deploying a production-ready model as a Dockerized REST API.
The course also covers the essential theory behind machine learning algorithms, helping you understand not only how to use them, but why they work and when to apply them.
Created byAbdel, a PhD researcher in Artificial Intelligence and a Top Rated Data Scientist, this course reflects real industry practices and professional workflows. By the end, you'll have practical experience, a portfolio-ready project, and the confidence to pursue opportunities inData Science, Machine Learning, and AI.
Who this course is for
⭐ Students, graduates, and researchers who want to strengthen their practical Data sciences and machine learning skills.
⭐ Data analysts, developers, engineers, and IT professionals who want to transition into data science.
⭐ Python users who want to learn how to build, evaluate, and improve machine learning models.
⭐ Anyone interested in learning data science and machine learning through clear explanations and hands-on exercises.
Homepage

Code:

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