Table of Content
Machine Learning Foundations
- What is Machine Learning
- Types of Machine Learning
- How does a Machine Learning Algorithm Works
- Parametric and Non-Parametric Algorithms
- Regression and Classification
- Clustering
- Preparing Your Data
- Outliers
- Problem of Underfitting and Overfitting
- The Bias-Variance trade-off
- Intro to jupyter notebooks
- Data Science packages
Regression Analysis
- What is Regression Analysis
- Linear Regression
- Cost Function
- Gradient Descent
- Polynomial Regression
- Logistic Regression
- Cost Function for Logistic Regression
- Regularization
- Evaluating a Machine Learning Model
Bayesian Statistics
- Introduction to Conditional Probability
- Bayes Rule
- Bayesian Learning
- Naïve Bayes Algorithm
- Test your understanding of Bayes Theorem
- Solution – Test Your Understanding of Bayes Theorem
- Bayes Net
- Markov Chains
Tree-Based Learning
- Decision Trees
- Gini Index
- ID3 Algorithm – Entropy
- ID3 Algorithm – Information Gain
- Practice Example – Information Gain
Project – 1
House Price Predictions
Project – 2
SMS Spam Classifier
Ensemble Learning
- What is Ensemble Learning
- Bagging
- Random Forest Algorithm
- Boosting
Support Vector Machines
- Introduction to Support Vector Machines
- Support Vectors
- Kernel
- Hyperparameters in SVMs
Instance Based Learning & Feature Engineering
- What is Instance Based Learning
- K-Nearest Neighbours Algorithm
- Dimensionality Reduction
- Principle Component Analysis
- Feature Scaling
- K-Means Algorithm
Project – 3
Breast Cancer Prediction
Deep Learning
- Introduction to Deep Learning
- Perceptron
- Perceptron Exercise
- Solution – Perceptron Exercise
- Deep Neural Networks
- Deep Neural Networks – 2
- Activation Functions - 1
- Activation Functions - 2
- Backpropagation Algorithm
- Convolutional Neural Nets
Project – 4
Image Classification using Deep Learning
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