Session 1 Introduction:–
What is Machine Learning?
Applications of Machine Learning
ML Types of Learning- Supervised vs Unsupervised Learning
Python libraries suitable for Machine Learning
Session 2 Data Pre-processing:–
Missing Value
Categorical Data
Splitting Dataset
Feature Scaling
Session 3 Regression:–
Introduction to Regression
Types of Regression
Simple Linear Regression
Multiple Linear Regression
Polynomial Regression
Decision Tree Regression
Random Forest Regression
Session 4 Classification:–
Basic Concept of Classification
Classification vs Regression
Logistic Regression
K-NN
SVM
Kernel SVM
Naive Bayes
Decision Tree Classification
Random Forest Classification
Session 5 Clustering: –
K-Means
Hierarchical Clustering
Session 6 Natural Language Processing: –
NLTK installation
Tokenization
Case Study: Document Classification
Session 7 Deep Learning:–
Artificial Neural Networks
Convolutional Neural Networks
Session 8 Dimensionality Reduction:–
Introduction to Dimensionality Reduction
Principal Component Analysis (PCA)
LDA
Kernel PCA
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