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Foundations of Machine Learning: Concepts, Models & Applications
Instructor
admin-Thinkit
Category
Members Only
Description
Curriculum
Reviews
Week 1: Introduction to Machine Learning
1
What is Machine Learning?
9:49
2
Types of Machine Learning: Supervised, Unsupervised, Semi-supervised, Reinforcement Learning
11:05
3
ML vs AI vs DL
5:53
4
Real-world Applications
7:15
5
Tools: Python, Jupyter, Scikit-learn
7:29
Week 2: Data & Preprocessing
6
Understanding Features and Labels ,Dataset types: structured, unstructured
10:01
7
Feature Engineering & Encoding Categorical Data
8:51
8
Feature Engineering & Encoding Categorical Data
Video lesson
9
Feature Scaling: Normalization and standardization
Video lesson
10
Train-Test Split and Cross-Validation
Video lesson
Week 3: Supervised Learning
11
Linear Regression (with code)
Video lesson
12
Logistic Regression (binary classification)
Video lesson
13
Hands-on Mini Project: Predicting Titanic Survival
Video lesson
Week 4: Unsupervised Learning
14
Clustering: K-Means
Video lesson
15
Dimensionality Reduction: PCA
Video lesson
16
Hands-on: Clustering Iris Dataset
Video lesson
Week 5: Decision Trees & Ensemble Models
17
Decision Trees, Overfitting & Pruning , Ensemble Methods: Random Forest
Video lesson
18
Gradient Boosting (intro to XGBoost)
Video lesson
19
Feature Importance & Interpretability
Video lesson
Week 6: Model Evaluation
20
ROC, AUC, Confusion Matrix
Video lesson
21
Model Tuning: Grid Search & Hyperparameter Optimization
Video lesson
22
Final Mini Project End-to-End ML Pipeline
Video lesson
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Course details
Lectures
22
Level
Advanced
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