Topic 1 Overview of Machine Learning and Scikit Learn
- Introduction to Machine Learning
- Supervised vs Unsupervised Learnings
- Machine Learning Applications and Case Studies
- What is Scikit Learn
- Installing Scikit-Learn
Topic 2 Classification
- What is Classification
- Classification Algorithms
- Classification Workflow
- Confusion Matrix
- Binary Classification Metrics
- ROC and AUC
Topic 3 Regression
- What is Regression?
- Regression Algorithms
- Regression Workflow
- Regression Metrics
- Overfitting and Regularizations
Topic 4 Clustering
- What is Clustering
- K-Means Clustering
- Silhouette Analysis
- Dendrogram and Hierarchical Clustering
Topic 5 Principal Component Analysis
- Curse of Dimensionality Issue
- What is Principal Component Analysis (PCA)
- Feature Reduction with PCA
Day 1
Topic 1 Overview of Machine Learning & Tensorflow 2.x
- Overview of Machine Learning and Deep Learning
- Introduction to Tensorflow 2.x
- Install Tensorflow 2.x
Topic 2 Basic Tensorflow Operations
- Basic Tensor Data Types
- Constant, Variable & Gradient
- Matrix Operations
- Eagle Mode vs Graph Mode
Topic 3 Datasets
- MNIST Handwritten Digits and Fashion Datasets
- CIFAR Image Dataset
- IMDB Text Dataset
Topic 4 Neural Network for Regression
- Introduction to Neural Network (NN)
- Activation Function
- Loss Function and Optimizer
- Machine Learning Methodology
- Build a NN Predictive Regression Model
- Load and Save Model
Topic 5 Neural Network for Classification
- Softmax
- Cross Entropy Loss Function
- Build a NN Classification Model
Topic 6 Convolutional Neural Network (CNN)
- Introduction to Convolutional Neural Network (CNN)
- Convolution & Pooling
- Build a CNN Model for Image Recognition
- Overfitting and Underfitting Issues
- Methods to Solve Overfitting
- Small Dataset Overfitting Issue
- Data Augmentation & Dropout
Topic 7 Recurrent Neural Network (RNN)
- Introduction to Recurrent Neural Network (RNN)
- Types of RNN Architectures
- LSTM and GRU
- Word Embedding
- Build a RNN Model for Text Classification
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