Algorithm Alchemy: Unlocking The Secrets Of Machine Learning
Published 12/2024
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Master Key Machine Learning Algorithms: From Basics to Real-World Applications
What you’ll learn
Understand key machine learning algorithms and their applications in real-world scenarios.
Build predictive models using supervised and unsupervised techniques.
Analyze and preprocess data for optimal algorithm performance.
Implement machine learning solutions using Python and popular libraries.
Master core concepts of supervised and unsupervised learning.
Apply decision trees, SVM, and neural networks in practical projects.
Evaluate model performance using accuracy, precision, and recall.
Build and optimize clustering models like K-Means and Hierarchical Clustering.
Understand ensemble techniques like Random Forest and Gradient Boosting.
Requirements
Basic Programming Knowledge: Familiarity with Python will be helpful but not mandatory.
Foundational Math Skills: Understanding of algebra and basic statistics is beneficial.
Computer with Internet Access: A reliable device for coding and accessing course materials.
Computer with Internet Access: A reliable device for coding and accessing course materials.
Description
Overview
Section 1: Introduction to Machine Learning Algorithms and Implementation in Python
Lecture 1 Introduction to Machine Learning Algorithms and Implementation in Python
Section 2: Supervised Learning Algorithms
Lecture 2 1. Linear Regression Implementation in Python
Lecture 3 2. Ridge and Lasso Regression Implementation in Python
Lecture 4 3. Polynomial Regression Implementation in Python
Lecture 5 4. Logistic Regression Implementation in Python
Lecture 6 5. K-Nearest Neighbors (KNN) Implementation in Python
Lecture 7 6. Support Vector Machines (SVM) Implementation in Python
Lecture 8 7. Decision Trees Implementation in Python
Lecture 9 8. Random Forests Implementation in Python
Lecture 10 9. Gradient Boosting Implementation in Python
Lecture 11 10. Naive Bayes Implementation in Python
Section 3: Unsupervised Learning Algorithms
Lecture 12 1. K-Means Clustering Implementation in Python
Lecture 13 2. Hierarchical Clustering Implementation in Python
Lecture 14 3. DBSCAN (Density-Based Spatial Clustering of Applications w Noise)
Lecture 15 4. Gaussian Mixture Models(GMM) Implementation in Python
Lecture 16 5. Principal Component Analysis (PCA) Implementation in Python
Lecture 17 6. t-Distributed Stochastic Neighbor Embedding (t-SNE) Implementation in Python
Lecture 18 7. Autoencoders Implementation in Python
Section 4: Other Specialized Categories
Lecture 19 1. Self-Training Implementation in Python
Lecture 20 2. Q-Learning Implementation in Python
Lecture 21 3. Deep Q-Networks (DQN) Implementation in Python
Lecture 22 4. Policy Gradient Methods Implementation in Python
Lecture 23 5. One-Class SVM Implementation in Python
Lecture 24 6. Isolation Forest Implementation in Python
Lecture 25 7. Convolutional Neural Networks (CNNs) Implementation in Python
Lecture 26 8. Recurrent Neural Networks (RNNs) Implementation in Python
Lecture 27 9. Long Short-Term Memory (LSTM) Implementation in Python
Lecture 28 10. Transformers Implementation in Python
