Machine Learning

6 Courses · 49 Lessons
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📖 C1 Exploratory Data Analysis for ML 4 Modules · 8 Lessons
▶ M1 EDA Fundamentals 2
1L1 Introduction to EDA→ 2L2 Data Understanding→
▶ M2 Data Cleaning 2
1L1 Handling Missing Values→ 2L2 Outliers and Data Types→
▶ M3 Feature Engineering 2
1L1 Encoding and Scaling→ 2L2 Feature Creation and Selection→
▶ M4 Stats and Visualization 2
1L1 Statistical Testing→ 2L2 Advanced Visualization→
📖 C2 Supervised ML Regression 4 Modules · 8 Lessons
▶ M1 Intro to Regression 2
1L1 Supervised Learning Overview→ 2L2 Regression Basics→
▶ M2 Linear Regression 2
1L1 Simple Linear Regression→ 2L2 Multiple Linear Regression→
▶ M3 Polynomial Regularized 2
1L1 Polynomial Regression→ 2L2 Regularization→
▶ M4 Evaluation and Project 2
1L1 Evaluation Metrics→ 2L2 Regression Project→
📖 C3 Supervised ML Classification 4 Modules · 8 Lessons
▶ M1 Intro to Classification 2
1L1 Classification Concepts→ 2L2 Classification Metrics→
▶ M2 Logistic Regression KNN 2
1L1 Logistic Regression→ 2L2 KNearest Neighbors→
▶ M3 Trees Forests SVM 2
1L1 Decision Trees→ 2L2 Random Forests and SVM→
▶ M4 Ensemble and Evaluation 2
1L1 Ensemble Methods→ 2L2 Model Selection and Evaluation→
📖 C4 Unsupervised ML 4 Modules · 8 Lessons
▶ M1 Intro to Unsupervised 2
1L1 Unsupervised Learning Overview→ 2L2 Distance and Similarity→
▶ M2 Clustering Algorithms 2
1L1 KMeans Clustering→ 2L2 Hierarchical and DBSCAN→
▶ M3 Dimensionality Reduction 2
1L1 PCA Principal Component Analysis→ 2L2 tSNE and UMAP→
▶ M4 Anomaly Detection 2
1L1 Anomaly Detection Methods→ 2L2 RealWorld Applications Project→
📖 C5 Deep Learning and Reinforcement Learning 5 Modules · 9 Lessons
▶ M1 Neural Networks 2
1L1 Neural Network Basics→ 2L2 Training Neural Networks→
▶ M2 CNNs 2
1L1 CNN Architecture→ 2L2 Transfer Learning and Applications→
▶ M3 RNNs 2
1L1 RNN Architecture→ 2L2 Sequence Applications→
▶ M4 Reinforcement Learning 2
1L1 RL Fundamentals→ 2L2 Deep Reinforcement Learning→
▶ M5 Final Project 1
1L1 Deep Learning Project→
📖 C6 Machine Learning Capstone 4 Modules · 8 Lessons
▶ M1 Project and Data 2
1L1 Problem Definition→ 2L2 Data Preparation→
▶ M2 Model Development 2
1L1 Model Training→ 2L2 Hyperparameter Tuning→
▶ M3 Evaluation and Optimization 2
1L1 Comprehensive Evaluation→ 2L2 Optimization and Interpretation→
▶ M4 Deployment and Submission 2
1L1 Model Deployment→ 2L2 Final Submission→