• views: 209
• date: 10 September 2025
• posted by: 0nelove

What you'll learn
Understand the intuition behind algorithms like Logistic Regression, KNN, SVM, and Decision Trees.
Apply Gradient Descent methods to optimize models effectively.
Evaluate models with metrics such as accuracy, precision, recall, and F1-score.
Compare different algorithms and choose the right one for a given problem.
Requirements
Basic knowledge of Python programming (variables, loops, functions).
A computer with Python installed (Anaconda or Google Colab works fine).
Description
Machine Learning is one of the most in-demand skills in today's world, and the best way to learn it is through practical projects. This beginner-friendly course will guide you step by step through the essential algorithms of Machine Learning using Python, while working on real-world datasets like Titanic, Wine Data, and Bank Marketing.We'll start from the basics — understanding how models work, how to evaluate their performance, and how to improve them. Then, we'll move into hands-on projects covering a wide range of algorithms:Logistic Regression with the Titanic datasetNaive Bayes with Wine classificationDecision Trees with Bank Marketing dataRandom Forests for ensemble learningGradient Descent methods (Batch, Stochastic, Mini-Batch)Linear Regression projects with real datasetsK-Nearest Neighbors (KNN) and Support Vector Machines (SVM)Boosting techniques and Unsupervised Learning (Clustering, PCA)By the end of this course, you will:Understand the intuition behind core ML algorithmsBe able to implement them from scratch and with Python librariesKnow how to choose the right model and evaluate its performanceGain confidence through practical, project-based learningNo advanced math is required — just basic Python knowledge and a willingness to learn. Whether you're a student, programmer, or professional looking to upskill, this course will give you a solid foundation to start your Machine Learning journey.
Who this course is for
Beginners who want to start learning machine learning through hands-on projects.
Students and programmers curious about data science and AI.
Career changers looking to add machine learning to their skill set.




   
   
   

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