25 Key Machine Learning Algorithms - Math, Intuition, Python
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Description
Do you want to understand machine learning algorithms and how artificial intelligence works but don’t know where to start? Or perhaps you already have some knowledge and want to deepen your understanding of AI-driven algorithms?
- This course is exactly what you need!
In this course, you’ll master 25 key machine learning algorithms:
Simple Linear Regression
Multiple Linear Regression
Logistic Regression
Decision Trees
K-means
Model Evaluation
Naive Bayes
Ridge Regression
Bagging
Random Forest
Boosting
LASSO
KNN
Gradient Boosting
PCA - Principal Component Analysis
XGBoost
LDA - Linear discriminant analysis
QDA - Quadratic discriminant analysis
Agglomerative Hierarchical Clustering
Hard-Margin SVM
SVM
DBSCAN
t-SNE
Isolation Forest
Perceptron
Each lesson is designed to provide clear, structured learning with three essential components:
Theory – A deep dive into the mathematical concepts behind each algorithm
Examples – Simple scenarios to illustrate how each algorithm works
Implementation – Step-by-step Python coding to bring each algorithm to life
Why This Course Stands Out:
No long videos – Just focused learning! This course is perfect for those who prefer reading over passive video watching.
Math made simple – Algorithms are explained in an accessible way, with intuitive examples to help you understand their logic.
Hands-on coding – You’ll implement every algorithm from scratch, ensuring you truly understand the process.
Ready to start your journey in Machine Learning?
Who this course is for:
- Aspiring Data Scientists and Machine Learning Engineers
- Beginners in Machine Learning who don’t know where to start
- Those looking for a balance between simple explanations and mathematical formalism
- People who prefer reading and analyzing rather than watching long lectures
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