About Course

MODULE 1: Introduction to Machine Learning

Overview of machine learning concepts and applications
Types of machine learning: supervised, unsupervised, reinforcement learning
Introduction to Python programming language and libraries (NumPy, Pandas, Matplotlib)

MODULE 2: Supervised Learning

Linear regression: theory and implementation
Logistic regression: theory and implementation
Model evaluation metrics: MSE, RMSE, MAE, confusion matrix, ROC curve

MODULE 3: Supervised Learning (cont.)

Decision trees and ensemble methods (bagging, boosting)
Random forests: theory and implementation
Gradient boosting machines (GBM)

MODULE 4: Unsupervised Learning

Clustering algorithms: K-means, hierarchical clustering
Dimensionality reduction techniques: PCA (Principal Component Analysis), t-SNE (t-distributed Stochastic Neighbor Embedding)

MODULE 5: Neural Networks and Deep Learning

Introduction to artificial neural networks (ANNs)
Deep learning fundamentals: activation functions, backpropagation
Building and training deep neural networks using TensorFlow/Keras

MODULE 6: Convolutional Neural Networks (CNNs)

Introduction to CNNs
CNN architecture and layers
Image classification and object detection using CNNs

MODULE 7: Recurrent Neural Networks (RNNs) and Natural Language Processing (NLP)

Introduction to RNNs
Long Short-Term Memory (LSTM) networks
Text classification and sentiment analysis using RNNs

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Course Content

Module 1

  • Introduction to ML
  • Python Programming Language Basics
  • Python Data types, Operations
  • Python Operators and conditional Statements
  • Data Structures
  • Python Loops (While, for)
  • Python Library – Numpy
  • Python Library – Pandas
  • Data Manipulation

Module 2

Module 3

Module 4

Module 5

Module 6

Module 7