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
Course Content
Module 1
-
Introduction to ML
56:11 -
Python Programming Language Basics
52:08 -
Python Data types, Operations
01:03:10 -
Python Operators and conditional Statements
01:05:18 -
Data Structures
59:00 -
Python Loops (While, for)
01:00:08 -
Python Library – Numpy
52:46 -
Python Library – Pandas
52:48 -
Data Manipulation
58:47