Sr No. |
Module Name |
Module Topics |
1
|
Introduction to Machine Learning
|
Origin of ML, History, Features, Career Prospects, Salary Ranges,
Types of Machine Learning (supervised, unsupervised, semi-supervised, reinforcement learning)
|
2
|
Introduction to Data Science and Data Processing
|
Learn about Data Science Process ,Data cleaning ,
Feature scaling ,
Feature engineering and selection,
Dimensionality reduction techniques (PCA, LDA)
|
3
|
Supervised Learning
|
Linear regression, Classification algorithms
(Logistic regression,
k-Nearest Neighbors (k-NN),
Support Vector Machines (SVM),
Decision Trees,
Random Forests,
Naive Bayes), Evaluation metrics for regression and classification
|
4
|
Unsupervised Learning
|
Clustering algorithms
(k-Means clustering,
Hierarchical clustering,
DBSCAN), Dimensionality Reduction
|
5
|
Advanced Supervised Learning Techniques
|
Ensemble learning, Neural Networks and Deep Learning
|
6
|
Model Evaluation and Optimization
|
Cross-validation, Hyperparameter tuning, Regularization techniques
|
7
|
Reinforcement Learning
|
Introduction to reinforcement learning,
Key concepts, Types of reinforcement learning, Policy gradient methods
|
8
|
Natural Language Processing (NLP)
|
Text preprocessing, Text representation, Sentiment analysis,
Named entity recognition,
Sequence-to-sequence models and transformers (BERT, GPT)
|
9
|
Project
|
Recommendation Systems
|