Course Content
Introduction to Machine Learning
- What is ML?
- Applications of ML
- Why ML?
- Uses of ML
- Machine learning methods
- Machine learning algorithms(Regression, Classification, Clustering, Association)
- A brief introduction python libraries
Creating a Machine Learning Model
- Types of ML algorithms
- Labelled Dataset
- Training and Testing Data
- Importing the Libraries
- Importing the Dataset
- Demo: Creating a machine model
Data Preparation and Exploration
- What is data?
- What is information?
- Analyzing data to fetch the information
- Entropy, Information gain
- Data exploration and preparation
- Univariate, bivariate, and multivariate analysis
- Correlation
- Chi-Square, Z-test, T-test, ANOVA
- Categorical Data
- Feature Scaling
- Dimensionality Reduction
- outliers
Regression
- What is regression?
- Applications of regression
- Types of regression
- Fitting the regression line
- Simple linear regression
- Simple linear regression in python
- Polynomial regression
- Polynomial regression in python
- Gradiant Descent
- Cost function
- Regularization
- Demo: Perform regression on a real world dataset
- Ridge and lasso Regression
Classification
- How is classification used?
- Applications of classification
- Logistic Regression, Sigmoid function
- Decision tree
- K-Nearest Neighbors (K-NN)
- SVM
- Naive Bayes
- Understand limitations of linear classifer and evaluate abilities of non-linear classifiers using a data set
Evaluation of Classification Models
- Confusion Matrix
- Precision, Recall
- F1-score
- RoC, AuC
- n-fold cross validation
- Measuring classifier performance
- Overfitting
- Ensemble Learning
- Bagging and Boosting
Unsupervised Learning - Clustering
- Application of Unsupervised learning, examples, and applications
- Clustering
- Hierarchical Clustering in Python, Agglomerative and Divisive techniques
- Measuring the distanvce between two clusters
- k-means algorithm
- Limitations of K-means clustering
- SSE and Distortion measurements
- Demo: Agglomerative Hierarchical clustering
Dimensionality Reduction
- What is dimensionality reduction?
- Applications of dimensionality reduction
- Feature selection
- Feature extraction
- Dimensionality reduction via Principal component analysis
- Eigenvalue and Eigenvectors
- Hands on PCA on MNSIT data
Reinforcement Learning
- What is reinforcement learning
- Applications of reinforcement learning
- An Example use case
- Components of RL
- Approachs to RL
- RL algorithms
- Deep reinforcement learning
Introduction to Natural Language Processing (NLP)
- What is NLP?
- Why NLP
- Applications of NLP
- Components of NLP
- NLP techniques
Introduction to Deep Learning
- Why deep learning?
- Neural networks
- Applications of neural networks
- Biological Neuron vs Artificial Neuron
- Artificial Neural networks, layers
