Machine Learning using Python Training Courses

Lesson 1：Course Introduction
1.1 Course Introduction
1.2 What You Will Learn
Lesson 2：Introduction to Machine Learning
2.1 Introduction
2.2 What is Machine Learning?
2.3 Types of Machine Learning
2.4 Machine Learning Pipeline and MLOP's
2.5 Introduction to Python Packages Used in Machine Learning
2.6 Recap
Lesson 3：Supervised Learning
3.1 Introduction
3.2 Supervised Learning
3.3 Applications of Supervised Learning
3.4 Preparing and Shaping Data
3.5 What is overfiting and underfiting?
3.6 Detecting and Preventing Overfitting and Underfitting
3.7 Regularization
3.8 Recap
Lesson 4：Decision Trees and Random Forest
4.1 Introduction
4.2 What is Regression?
4.3 Regression Types Introduction
4.4 Linear Regression
4.5 Working with Linear Regression
4.6 Critical Assumptions for Linear Regression
4.7 Logistic Regression
4.8 Data Exploration Using SMOTE
4.9 Over Sampling Using SMOTE
4.10 Polynomial Regression
4.11 Data Preparation Model Building and Performance Evaluation Part A
4.12 Ridge Regression
4.13 Data Preparation Model Building Part B
4.14 LASSO Regression
4.15 Data Preparation Model Building Part C
4.16 Recap
4.17 Spotlight
Lesson 5：Classification and Applications
5.1 Introduction
5.2 What are Classification Algorithms?
5.3 Types of Classification
5.4 Types and selection of performance parameters
5.5 Naive Bayes
5.6 Applying Naive Bayes Classifier
5.7 Stochastic Gradient Descent
5.8 Applying Stochastic Gradient Descent
5.9 K Nearest Neighbors
5.10 Applying K Nearest Neighbors
5.11 Decision Tree
5.12 Applying Decision Tree
5.13 Random Forest
5.14 Applying Random Forest
5.15 Boruta Explained
5.16 Automatic Feature Selection with Boruta
5.17 Support Vector Machine
5.18 Applying Support Vector Machine
5.19 Cohens Kappa Measure
5.20 Recap
Lesson 6：Unsupervised Algorithms
6.1 Introduction
6.2 What are Unsupervised Algorithms?
6.3 Types of Unsupervised Algorithms Clustering and Associative
6.4 When to Use Unsupervised Algorithms?
6.5 Visualizing Outputs
6.6 Performance Parameters
6.7 Clustering Types
6.8 Hierarchical Clustering
6.9 Applying Hierarchical Clustering
6.10 K means Clustering Part 1
6.11 K means Clustering Part 2
6.12 Applying K Means Clustering
6.13 KNN K Nearest Neighbors
6.14 Outlier Detection
6.15 Outlier Detection Algorithms in PyOD
6.16 Demo KNN for Anomaly Detection
6.17 Principal Component Analysis
6.18 Applying Princiipal Component Analysis PCA
6.19 Correspondence Analysis  Multiple correspondence analysis MCA
6.20 Singular Value Decomposition
6.21 Applying Singular Value Decomposition
6.22 Independent Component Analysis
6.23 Applying Independent Component Analysis
6.24 BIRCH
6.25 Applying BIRCH
6.26 Recap
6.27 Spotlight
Lesson 7：Ensemble Learning
7.1 Introduction
7.2 What is Ensemble Learning?
7.3 Categories in Ensemble Learning
7.4 Sequential Ensemble Technique
7.5 Parallel Ensemble Technique
7.6 Types of Ensemble Methods
7.7 Bagging
7.8 Demo Bagging
7.9 Boosting
7.10 Demo Boosting
7.11 Stacking
7.12 Demo Stacking
7.13 Reducing Errors with Ensembles
7.14 Applying Averaging and Max Voting
7.15 Hello World Tensorflow
7.16 Hands on with TensorFlow Part A
7.17 Keras
7.18 Hands on with TensorFlow Part B
7.19 Recap
Lesson 8：Recommender Systems
8.1 Introduction
8.2 How do recommendation engines work
8.3 Recommendation Engine Use Cases
8.4 Examples of Recommender System and Their Designs
8.5 Leveraging PyTorch to Build a Recommendation Engine
8.6 Collaborative Filtering and Memory Based Modeling
8.7 Item Based Collaborative Filtering
8.8 User Based Collaborative Filtering
8.9 Model Based Collaborative Filtering
8.10 Dimensionality Reduction and Matrix Factorization
8.11 Accuracy Matrices in ML
8.12 Recap
8.13 Spotlight