Introduction to the main Machine Learning methods of supervised or unsupervised learning and the most common algorithmic approaches, including the k-nearest neighbor algorithm, decision tree-based learning and deep learning.

Development IoT Machine Learning

The journey of learning Python programming and implementing Artificial Intelligence started with this series of tutorials and case studies. This is now the final project of the series and we will be covering all the key topics we learnt so far. Starting from Data Preprocessing, to turning text into features, performing NLP on real news articles, and then building a Machine Learning model to predict classes of output. The primary aim of this series has been to practice Python programming, learn concepts within Python that help in performing AI tasks like Machine Learning, Speech Processing, NLP, Cognitive Science, etc. From starting with statistical concepts to building ML models, we have covered all these bases in the last twelve chapters. In this chapter, we will implement a final project using all the concepts we have learnt throughout this series.  

Machine Learning Natural Language Processing Python and AI Sentiment Analysis

Learning is defined as the acquisition of knowledge and skills through practice, learning or experience. Based on the idea of learning, machine learning implements this through studying given input and understanding the data.

Classification Clustering Machine Learning ML Python

Here we will continue what we saw in APPLIED MACHINE LEARNING USING PYTHON – CLASSIFICATION & CLUSTERING - PART 1

Classification Clustering Machine Learning ML Python

Regression, both as a statistical tool and an algorithm for machine learning is widely used. It could be described as a parametric procedure that permits us to produce results based on data by studying the association between input and output variables. The output variables here are generally continuous-valued real numbers. Regression is often used for prediction of values or patterns.

Machine Learning ML Python Regression

Here we will continue what we saw in APPLIED MACHINE LEARNING USING PYTHON – REGRESSION - PART 1

Machine Learning ML Python Regression

We have now reached the concluding chapter of our machine learning journey. This is also the beginning of the first project and will focus on Machine Learning for Problem Solving. We will take a real-life problem and try solving it using ML algorithms. To compute the best-case solution, we will also perform significant amount of data preprocessing and cleaning to make it ready for analysis.

AI Artificial Intelligence Machine Learning ML Python

We started our first case of study in machine learning. We worked with the Ames Housing dataset and we applied the EDA and some statistics tests on the data. Let's now continue our analysis.

AI Artificial Intelligence Machine Learning ML Python