The modern era is a data-rich world. Data streams are available from unlimited number of resources. But raw data is not useful till you start getting knowledge from it. Machine learning is nothing but learning from data. So, various machine learning techniques can help you get the inherent knowledge or pattern in the available data set. There are plenty of great applications of machine learning such as

Spam detection, sentiment prediction, web document classification, market prediction, fraud detection, product recommendation, potential customer identification, weather prediction, character recognition, games, medical diagnosis …….the list is endless.

So, As you can see machine learning is a very powerful tool, and if you are also interested to learn and harness the power of machine learning, this blog post will be very helpful to you.

In this blog post, I will provide a learning path to understand various machine learning concepts. I have created several courses on Pluralsight targeting this subject. Based on these course, I have formulated this learning path for you.

Before going to the learning path itself. lets answer one important question.

## Who should follow this path ?

1. Those who want to learn the fundamentals of various machine learning concepts without going into much mathematical details [ So no scary calculus, matrix algebra, probability terms]

2. Those who want to learn to build applications[ in various programming languages such as C#] based on various machine learning techniques

3. Those who want to learn the practical and real world implementations of machine learning techniques

4. Those who are planning to build their career in data analysis field.

5. Those who want to build great products using the open source machine learning framework ENCOG

## Learning Path [ Total duration : Approx 10 Hours]

### Level 1: Build a solid foundation by taking the first course

### Introduction to Machine Learning With ENCOG [ Total Duration : 2 hr 19 min]

**Course Description** :* This course is focused on implementation and applications of various machine learning methods. As machine learning is a very vast area, this course will be targeted more towards one of the machine learning methods which is neural networks. The course will try to make a base foundation first by explaining machine learning through some real world applications and various associated components. In this course, we’ll take one of the open source machine learning framework for .NET, which is ENCOG. The course will explain how ENCOG fits into the picture for machine learning programming. Then we’ll learn to create various neural network components using ENCOG and how to combine these components for real world scenarios. We’ll go in detail of feed forward networks and various propagation training methodologies supported in ENCOG. We’ll also talk about data preparation for neural networks using normalization process. Finally, we will take a few more case studies and will try to implement tasks of classification & regression. In the course I will also give some tips & tricks for effective & quick implementations of neural networks in real world applications.*

### Level 2 : Learn advanced topics by taking the second course

### Advanced Machine Learning With ENCOG [ 4 hr 11 min]

**Course Description :** *Are you worried about your neural network model prediction accuracy? Are you not sure about your neural network model selection for your machine learning problem? This course will introduce you to more advanced topics in machine learning. The previous introductory course, “Introduction to Machine Learning with ENCOG 3,” laid out a solid foundation of machine learning and neural networks. This course will build upon that foundation for more advanced machine learning implementations. In this course, you will learn about various neural network optimization techniques to overcome the problems of underfitting and overfitting and to create more accurate predictive models. This course will also provide an overall picture of various neural network architectures and reasons for their existence. This course will be focused towards implementation of various supervised feed forward and feedback networks. During the whole course, we will be using open source machine learning framework ENCOG to implement various concepts discussed in this course. Although the implementations in this course are ENCOG-based, concepts discussed in this course are widely applicable in other frameworks or even in custom development.*

### Level 3 : Learn more advanced topics by taking the third course

### Advanced Machine Learning With ENCOG – Part 2 [3 hr 41 min]

**Course Description :** *Finding patterns in a multidimensional dataset has always been a challenging task, but self-organizing maps can simplify this process and can help to find interesting patterns and inferences. In this course, you will learn not only the fundamentals of self-organizing maps but also the implementation in a C# application using the ENCOG machine learning framework. In this course, you will also learn to use Hopfield networks in a pattern recall and reconstruction application. This course will also provide a real world case study on time series forecasting, where you will learn to forecast future behavior using historical values. The course also covers another very important aspect of machine learning: optimization. You will learn to solve optimization problems with the help of genetic algorithms. The concepts learned in this course are applicable for developers working in any other framework in any other language.*

This 10 hour learning path will surely help you to understand the power of machine learning. And you will be able to implement various machine learning tasks easily and effectively. In fact, with each step, your confidence level will go up.

Hope this learning path will help you in your machine learning endeavors. Looking forward to your feedback.

Recommended Materials :

1. Programming neural networks with ENCOG 3 in C#, Jeff heaton

2. Artificial Intelligence for Humans , vol 1: Fundamental Algorithms, Jeff heaton

3. Introduction to math of machine learning, Jeff heaton