The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyse past sales data to predict customer behaviour, optimise robot behaviour so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data.
Below are some of the resources that will get you started on learning concepts of Machine Learning along with its practical applications.
Blogs / Resources
Here’s an ideal resource for you to master machine learning. This learning path provides you with a step by step method of becoming a master at machine learning. Once you’ve covered the basics of machine learning, you can then proceed to higher level concepts such as deep learning , neural network.
With this, you can dive deep into the essential components of machine learning which includes algorithms / techniques used in machine learning. In this article, the algorithms have been explained in the simplest possible manner using real life interesting examples.
More than reading, sometimes video tutorials can help you learn concepts quickly. Here’s a large collection of best youtube videos available in machine learning, deep learning and neural networks. These videos include talks and complete tutorials teaching various aspects of machine learning.
Resources by Machine Learning Category
Data exploration/ Pre-Processing:
Machine Learning Algorithms:
- Linear Regression (Resource1, Resource2, Resource3)
- Logistic Regression (Resource1, Resource2)
- Ridge and Lasso Regression (Resource1, Resource2)
- Naive Bayes (Resource)
- k-NN (Resource)
- k-Means (Resource1, Resource2, Resource3)
- Support Vector Machine (Resource1, Resource2)
- Tree Based Algorithms (Resource)
Boosting and Ensemble Methods:
Improve Model Performance:
This book is best suited for beginners who have no prior knowledge of machine learning and pattern recognition. It provides a comprehensive introduction to the field of pattern recognition and machine learning.
This book is highly recommended by data science experts. It covers all the necessary algorithms that you require to master the concepts of machine learning. This book describes the important ideas covering a wide range of topics from supervised to unsupervised learning.
This is another book which covers important aspects of bayesian reasoning with the elementary to advanced level of machine learning concepts.
This book is a great starting point for beginners in data science. This book manifests intuitive examples which are fun to read and help understand complex concepts in a simplistic manner.The books cover a wide range of topics beginning with fundamentals of probability & statistics to advanced concepts of machine learning.
This book is meant for folks interested to master the concepts of advanced machine learning which include data compression, noisy channel coding, probabilities and inference, neural networks, sparse graph codes etc.