C# Corner Launches New Learn Series - Machine Learning with Python

C# Corner launches its new learn series "Machine Learning with Python"

Adding a new feather to its hat, C# Corner has launched a new learning series, "Machine Learning with Python".
 
 
 
Learn series is C# Corner's way to teach you cutting edge technologies.
 
Machine learning is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence. 
 
Machine Learning is a topic that has gained a lot of buzz in recent years, after tech giants like Google, Amazon, etc. started to implement search engines and recommendation systems. 
 
Python is widely employed to implement machine learning algorithms as Python has a wide variety of libraries with pre-defined functions and also since the number of code lines needed to be written is comparatively less as compared to other languages. Hence, the resulting code is crisp and takes less time to run.
 
 
 
This series is intended to introduce the learner to applied machine learning, focusing on the techniques and methods along with the theory and the statistics behind. You will get hands-on ML concepts using Python environment. The series will provide you everything required to getting started in Machine learning from scratch. 
 
The USP of the learn series is that:
 
1. For each machine learning algorithms, there are 3 Python implementations provided, i.e.:
  1. From Scratch (using basic functions)
  2. Using Scikit-Learn
  3. Using Tensorflow 
2. Provides an introduction to Python 
 
Python provides us with a lot of pre-defined machine learning libraries. Some of them discussed in the series are:
  1. NumPy
  2. Pandas
  3. Scikit-Learn
  4. Matplotlib
  5. Seaborn
  6. Tensorflow
The series provides you with hands-on knowledge of the following machine learning algorithms: 
  1. Linear Regression
  2. Logistic Regression
  3. Multiple Linear Regression
  4. Decision Tree
  5. Naive Bayes
  6. K-Means Clustering
  7. K-Nearest Neighbors
  8. Support Vector Machine 
At the end of the series, you will get a chance to test what you have learned by implementing the following:
  1. Housing Price Prediction
  2. IRIS Dataset
  3. Tweet Classifier
  4. Recommendation System
To learn more, please visit Machine learning with Python.