Machine Learning With Python

Course Description

This course dives into the basics of machine learning using an approachable, and well-known programming language, Python.
In this course, we will be reviewing two main components: First, you will be learning about the purpose of Machine Learning and where it applies to the real world.
Second, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms.

Course Instructor

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Bikash Nandan Bora

Pursuing his M.Tech at IIIT specialising in IoT and Machine Learning

TOPICS



1

Introduction to Machine Learning

In this week, you will learn about applications of Machine Learning in different fields such as health care, banking, telecommunication, and so on. You'll get a general overview of Machine Learning topics such as supervised vs unsupervised learning, and the usage of each algorithm. Also, you understand the advantage of using Python libraries for implementing Machine Learning models.

  • Welcome
  • Introduction to Machine Learning
  • Python for Machine Learning
  • Supervised vs Unsupervised

2

Regression

In this week, you will get a brief intro to regression. You learn about Linear, Non-linear, Simple and Multiple regression, and their applications. You apply all these methods on two different datasets, in the lab part. Also, you learn how to evaluate your regression model, and calculate its accuracy.

  • Introduction to Regression
  • Simple Linear Regression
  • Model Evaluation in Regression Models
  • Evaluation Metrics in Regression Models
  • Multiple Linear Regression
  • Non-Linear Regression

3

Classification

In this week, you will learn about classification technique. You practice with different classification algorithms, such as KNN, Decision Trees, Logistic Regression and SVM. Also, you learn about pros and cons of each method, and different classification accuracy metrics.

  • Introduction to Classification
  • K-Nearest Neighbours
  • Evaluation Metrics in Classification
  • Introduction to Decision Trees
  • Building Decision Trees
  • Intro to Logistic Regression
  • Logistic regression vs Linear regression
  • Logistic Regression Training
  • Support Vector Machine

4

Clustering

In this section, you will learn about different clustering approaches. You learn how to use clustering for customer segmentation, grouping same vehicles, and also clustering of weather stations. You understand 3 main types of clustering, including Partitioned-based Clustering, Hierarchical Clustering, and Density-based Clustering.

  • Intro to Clustering
  • Intro to k-Means
  • More on k-Means
  • Intro to Hierarchical Clustering
  • More on Hierarchical Clustering
  • DBSCAN

5

Recommender Systems

In this module, you will learn about recommender systems. First, you will get introduced with main idea behind recommendation engines, then you understand two main types of recommendation engines, namely, content-based and collaborative filtering.

  • Intro to Recommender Systems
  • Content-based Recommender Systems
  • Collaborative Filtering

6

Final Project

In this module, you will do a project based of what you have learned so far. You will submit a report of your project for peer evaluation.