The Machine Learning Algorithm Course 2025 is your step-by-step guide to mastering the foundations and advanced concepts of machine learning. This course is designed for beginners, data science enthusiasts, and professionals who want to understand how different algorithms work and how to apply them in real-world projects.
Starting from the basics of supervised and unsupervised learning, you’ll explore the theory behind algorithms, then implement them with practical coding examples. From regression to classification, clustering to dimensionality reduction, this course ensures you not only learn the math and logic but also build hands-on experience by coding each algorithm from scratch and using popular ML libraries like Scikit-learn, NumPy, and Pandas.
By the end of this course, you will:
✅ Understand the fundamentals of Machine Learning and its applications
✅ Learn the math and intuition behind key ML algorithms
✅ Implement algorithms like Linear Regression, Decision Trees, KNN, SVM, Naïve Bayes, and more
✅ Explore clustering methods like K-Means and Hierarchical Clustering
Course Content
Machine Learning Algorithms: Complete Guide 2025
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Machine Learning Algorithms Introduction
00:35 -
Machine Learning Algorithms 1 : Linear Regression
08:35 -
Machine Learning Algorithms 2 : Logistic Regression
05:25 -
Machine Learning Algorithms 3 : Support Vector Machine
11:08 -
Machine Learning Algorithms 4 : Decision Tree
08:30 -
Machine Learning Algorithms 5 : Random Forest
05:11 -
Machine Learning Algorithms 6 : KNN
07:25 -
Machine Learning Algorithms 7 : K MEANS CLUSTERING
05:52 -
Machine Learning Algorithms 8 : Naive Bayes
09:30 -
Machine Learning Algorithm Project
14:46
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