Introduction To Machine Learning
- What is M.L?
- Why M.L?
- Introduction to Supervised M.L
- Introduction to Unsupervised M.L
- Bias- Variance Dichotomy
- Cross-Validation, Area Under Curve
- Data Split and Hyper Parameter Tuning
- Feature Engineering
- Data Preprocessing
Supervised Machine learning Algorithms
- What is Regression?
- Different Types of Regression
- Linear Regression
- Goodness of Fit
- Multiple Linear regression
- Regularization- L1 and L2.
- Real-Time Mini Project on Regression
- Logistic Regression
- K-Nearest Neighbor Algorithm
- Decision- Trees
- Random Forest
- Naive Bayes for text classification
- Real-Time Mini Project on Classification
Unsupervised Machine Learning Algorithms
- What is Clustering?
- Types of Clustering-Hierarchical and Agglomerative
- K-Means Clustering Algorithm
- Real – Time Problem
Some Advanced M.l
- Curse of Dimensionality- PCA
- Support Vector Machines
- Gradient Boosting
- XGBoost Algorithm
- Ensemble Methods
Introduction To Deep Learning
- What is Deep Learning?
- Why Deep Learning?
- How Deep Learning works?
- DrawBacks of Machine Learning
- Applications of Deep Learning
- What is a neural network?
- Structure of Artificial Neuron
- Training of Perceptron
- Important Parameters of Perceptrons
- Activation Functions-sigmoid,Relu,SoftMax, Hyperbolic Fns
- Feed-forward Neural Network
- Introduction to Back-Propogation