Machine Learning

Machine Learning

Machine Learning is one of the fastest evolving fields, it involves different algorithms which the businesses can utilize for developing their projects. Machine learning algorithms are implemented using various languages such as C, C++, JavaScript, R, and Python.

Machine Learning (ML) is expected to bring heavy changes to the world of technology. Machine learning is a subfield of artificial intelligence and computer science that allows software applications to be more accurate in predicting results.

Advantages of Machine Learning are:

- It is used in so many industries of applications such as banking and financial sector, healthcare, retail, publishing and social media, etc.

- It is used by Google and Facebook to push relevant advertisements based on users search history.

- It allows time cycle reduction and efficient utilization of resources.

- Due to machine learning there are tools available to provide continuous quality improvement in large and complex process environments.


Machine Learning

Python Programming Language for Machine Learning

Python programming language is gaining more popularity in Machine Learning projects due to its various features. It is a high-level, general purpose and dynamic programming language which is not new in the market.

Python programming language can be found practically at everyplace, like web and desktop apps, machine learning, network servers and many more. It is one of the fastest growing programming languages. Python programming language has several advantages which enable Machine Learning developers to use it for developing their Machine Learning projects.

Python is the tool of choice for people who want to explore and apply machine learning for their research projects - data analysis, pattern recognition, etc. Python comes with a number of very attractive machine learning libraries which can be applied out of the box.

Python is one of the most popular programming languages in the world. Favoured for applications ranging from web development to scripting and process automation, Python is quickly becoming the top choice among developers for artificial intelligence (AI), Machine learning, and deep learning projects.

Machine Learning - Contents


  1. Introduction to Machine Learning :

  2. - Introduction to Machine Learning
    - Types of Machine learning: Supervised, Unsupervised and Reinforcement Learning
    - Discussion on different packages used for ML
    - Working on Linear regression: Understanding the regression technique
    - Related concepts: Splitting the dataset into training and validation
    - Case study based practical application of the technique on R and Python

  3. Supervised Machine Learning :

  4. - Linear Regression Technique
    - Logistic Regression Technique
    - Hierarchical Cluster Analysis

  5. Decision Tree :

  6. - Decision Tree
    - Introduction to Decision tree
    - Significance of using Decision Tree
    - Different kinds of Decision Tree
    - Procedure and technique of Decision Tree
    - Practical application of Decision Tree on R and Python

  7. Support Vector Machine :

  8. - Support Vector machine
    - Introduction to Support Vector machine
    - Mathematical Approach
    - Theory on hyperplane and kernels
    - Kernel function
    - Different kinds of kernels
    - Practical application on R and Python

  9. Random Forest :

  10. - Random Forest
    - Theory and mathematical concepts
    - Entropy and Decision Tree Classification using random forest on Python and R

  11. Naïve Bayes :

  12. - Naïve Bayes
    - Theory of classification
    - Concept of probability: prior and posterior
    - Bayes Theorem
    - Mathematical concepts
    - Limitation of Naïve Bayes
    - Practical application on Python and R

  13. K- Nearest Neighbours :

  14. - K-Nearest Neighbours
    - Concept and theory
    - Distance functions: Euclidean, Hamming, Minkowski
    - Why should we use KNN?
    - Mathematical approach
    - Practical application on Python and R

  15. Gradient Boosting :

  16. - Gradient boosting
    - Bootstrapping
    - Types of boosting
    - Gradient descent
    - Practical application on Python and R

  17. Information Retrieval :

  18. - Information Retrieval
    - Concepts and how to deal with humungous information
    - Natural Language Processing
    - Related concepts and theory

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