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

With Classification, Clustering, Regression and Ensemble Learning, we have walked through the details of algorithms that are used in predicting outcomes of problems. We also observed ways of measuring which algorithm is better suited for what problem and the art of combining results from multiple models and building a best-case result. In this chapter, we will take our exploration of prediction to the next level and work on predicting outcomes that are dependent on time.

Forecasting Python

Here we will continue what we saw in TIME SERIES FORECASTING IN PYTHON - PART 1

Forecasting Python

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

We started Artificial Intelligence with Machine Learning and Data Analysis. In the last six chapters, we moved from basics of Machine Learning to solving a real-world example of predicting house prices based on specified features. AI and ML go hand in hand within projects and it is now time for us to move ahead in this journey. In the coming chapters we will be concentrating on a diverse set of Artificial Intelligence concepts revolving around cognitive science (speech, writing, etc.)

AI Artificial Intelligence Logic Programming Python