Machine Learning using Python Programming

Learn the core concepts of Machine Learning and its algorithms and how to implement them in Python 3

‘Machine Learning is all about how a machine with an artificial intelligence learns like a human being’

What you’ll learn

  • Machine Learning Algorithms & Terminologies.
  • Artificial Intelligence.
  • Python Libraries – Numpy, Pandas, Scikit-learn, Matplotlib, Seaborn.

Course Content

  • Introduction to Machine Learning –> 3 lectures • 13min.
  • Types of Machine Learning –> 2 lectures • 10min.
  • The Machine Learning Pipeline –> 6 lectures • 35min.
  • Numpy Library –> 6 lectures • 54min.
  • Pandas Library –> 9 lectures • 36min.
  • Analysis of Datasets using Pandas and Matplotlib Library –> 3 lectures • 25min.
  • The Scikit-learn Library and Preprocessing Techniques –> 3 lectures • 42min.
  • Supervised Learning – Linear Regression –> 5 lectures • 49min.
  • Logistic Regression for Classification Problems –> 5 lectures • 43min.
  • Support Vector Machines –> 3 lectures • 21min.

Machine Learning using Python Programming

Requirements

  • Yes, A Basic Knowledge in Python is preferred.

‘Machine Learning is all about how a machine with an artificial intelligence learns like a human being’

Welcome to the course on Machine Learning and Implementing it using Python 3. As the title says, this course recommends to have a basic knowledge in Python 3 to grasp the implementation part easily but it is not compulsory.

This course has strong content on the core concepts of ML such as it’s features, the steps involved in building a ML Model – Data Preprocessing, Finetuning the Model, Overfitting, Underfitting, Bias, Variance, Confusion Matrix and performance measures of a ML Model. We’ll understand the importance of many preprocessing techniques such as Binarization, MinMaxScaler, Standard Scaler

We can implement many ML Algorithms in Python using scikit-learn library in a few lines. Can’t we? Yet, that won’t help us to understand the algorithms. Hence, in this course, we’ll first look into understanding the mathematics and concepts behind the algorithms and then, we’ll implement the same in Python. We’ll also visualize the algorithms in order to make it more interesting. The algorithms that we’ll be discussing in this course are:

1. Linear Regression

2. Logistic Regression

3. Support Vector Machines

4. KNN Classifier

5. KNN Regressor

6. Decision Tree

7. Random Forest Classifier

8. Naive Bayes’ Classifier

9. Clustering

And so on. We’ll be comparing the results of all the algorithms and making a good analytical approach. What are you waiting for?