GitHub - faizanxmulla/CS2008-machine-learning-practice: Repository containing introduction to scikit-learn to provide hands-on problem solving experience for all the methods and models learnt in MLT.

Machine-Learning Practice

WEEK 1 - End-to-end machine learning project on scikit-learn

WEEK 2 - End-to-end machine learning project on scikit-learn (continued)

WEEK 3 - Regression on scikit-learn - Linear regression Gradient-descent- Batch (MBGD) and Stochastic (SGD).

WEEK 4 - Polynomial regression, Regularized models

WEEK 5 - Logistic regression

WEEK 6 - Classification on scikit-learn - Binary classifier

WEEK 7 - Classification on scikit-learn - Multiclass classifier

WEEK 8 - Support Vector Machines using scikit-learn

WEEK 9 - Decision Trees using scikit-learn

WEEK 10 - Ensemble Learning and Random Forests using scikit-learn

WEEK 11 - Clustering using scikit-learn

WEEK 12 - Neural networks models in scikit-learn

Understand the life cycle of a machine learning project - typical steps involved and tools that can be used in each step.
Using machine learning algorithms to solve practical problems using libraries like scikit-learn and tensorflow.
Fine tuning the algorithms through regularization, feature selection, and better models.
Develop an understanding of evaluation of machine learning algorithms and decide the next steps based on the analysis.

Name		Name	Last commit message	Last commit date
Latest commit History 16 Commits
W1 - Introduction		W1 - Introduction
W10 - Ensemble-RandomForests		W10 - Ensemble-RandomForests
W11 - Clustering		W11 - Clustering
W12 - Neural Networks		W12 - Neural Networks
W2 - Data Preprocessing		W2 - Data Preprocessing
W3 - Regression		W3 - Regression
W4 - Regression (cont.)		W4 - Regression (cont.)
W5 - Classification		W5 - Classification
W6 - Classification (cont.)		W6 - Classification (cont.)
W7 - Softmax Regression-KNN		W7 - Softmax Regression-KNN
W8 - SVM		W8 - SVM
W9 - Decision Trees		W9 - Decision Trees
README.md		README.md