Machine Learning is the science of creating algorithms and program which learn on their own.Once designed, no need for human being to become better. Common ML approaches include but not limited to Web Search, spam filters, recommender system, ad placement,credit scoring,fraud detection,stock,trading,computer vision etc.
You are here to either learn ML or quick view the ML approaches used.I got to ML after rigorous data analysis using python and R.
As I approached different challlenges of data analysis, It was inevitable to apply ML in the kind of datasets, so I had to learn ML. ML seems to be complex but as far as you got passion, you can learn it.
Prior to understand ML, one should know atleast some of the following mathematical topics:
- linear algebra
- probability theory
- calculus
- statistics
- calculus of variations
- graph theory
- optimization methods (Lagrange multipliers)
Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program.
- Familiarity with the basic probability theory.
- Familiarity with the basic linear algebra will also do.
This covers a wide range of ML algorithms to solve problem, however,
By the end, you will be able to:
-Identify potential applications of machine learning in practice of real problems.
-Describe the core differences in analyses enabled by regression , classification, and clustering.
-Atleast choose the appropriate machine learning task for a potential application.
-Apply regression, classification, clustering, retrieval, recommender systems, and deep learning.
-Represent your data as features to serve as input to machine learning models.
-Assess the model quality in terms of relevant error metrics for each task.
-Utilize a dataset to fit a model to analyze new data.
-Build an end-to-end application that uses machine learning at its core.
-Implement these techniques in Python.