Machine Learning technology gives machines ability to learn using data, and improve with time. All this happens with minimal programming. There are multiple machine learning algorithms are available. We need to select the best ML algorithm and input data to ensure we get a good performance. This article covers a list of important machine learning topics you need to master to develop best machine learning applications.
Here is the list of important machine-learning topics you can master to solve complex ML problems.
- Math for Machine Learning
- Programming Language
- Insights on Business Applications
- Hypothesis Testing
- Exploratory Data Analysis
- Machine Learning Algorithms and their applications
- ML model evaluation techniques
- ML model deployment
Math for Machine Learning: Machine Learning Topic to Understand Basics
ML Algorithms work on the concepts of mathematics. We can’t understand the ML concepts without understanding the math behind them. Here is the list of important math topics for Machine learning.
- Linear Algebra
- Matrix
- Probability and Distribution
- Statistics
- Geometry
- Calculus
- Dimensionality Reduction
- Regression
Expert recommends to master the mathematical concepts before starting the Machine Learning journey.
Important Resources Links for Math for Machine Learning
Programing Language: Important Machine Learning Topics
Multiple programming languages are available for machine learning. But “Python” and “R” are extensively used. Here is the list of machine learning programming languages.
- Python
- R
- Java
- Julia
- C++
- MATLAB
You can select the programming language as per your experience. If you are new to programming, Python is easy to learn and has all the required libraries for Machine Learning.
Important Resources to learn python for Machine Learning
Insight on the business problem: Prior Requirement to Build ML Models
Machine Learning is not only about data wrangling, developing ML algorithms, and deploying them. Its end goal is to solve complex business problems that are very difficult to solve using traditional programming.
Therefore, best practice is to:
- Understand the business problem in depth before starting any ML project.
- How your competitors are solving the problem.
- What additional value Machine Learning implementation will add?
- Explore other options (such as rule-based programming) to solve the problem.
You cannot build this understanding just by reading books. You should practice to solve real-life problems.
Hypothesis testing is an important machine learning topics. We can use hypothesis testing to get inferences on the population from the sample data. Its application in machine learning is to select features and check assumptions for machine learning algorithms.
Data Wrangling and EDA: Important Machine Learning Topics
Data is fuel for machine learning algorithms. Understanding and analysis of data helps to select the optimized ML Algorithms and improve input data quality.
The better the quality of the input data, the more reliable and accurate will be the ML Model. Real-time data is scattered and has defects such as missing data, outlines, etc. Therefore, we need to process the data before we input it into ML algorithms.
The ultimate goal for data wrangling and exploratory data analysis is to:
- Build an understanding of data.
- Handle missing values and outliners.
- Feature Selection
- ML Model Selection etc.
Machine Learning Models and their Applications
Multiple machine learning algorithms are available that work on different mathematical concepts. As a result, different ML algorithms give different performances for the same input data.
Therefore, we need to select the best ML Algorithms according to requirements. Here is the list of factors that affect the selection of the best ML algorithm:
- Business Need
- Availability of the output in the training data (Supervised or un-supervised problem).
- Number of observations in training and test data.
- Type and number of input features.
- Comparison of evaluation results
ML Model Evaluation Techniques
Evaluation of ML algorithms is an important machine learning topic. It is critical to evaluate the ML Model performance against the business needs. Multiple ML model evaluation techniques are available.
It is not straightforward to verify the ML model on any evaluation criteria. Here is the list of factors that affect the selection of evaluation techniques.
- Business Need
- Type of Output: Continuous or Categorical
ML Model Deployment: Most untapped Potential Topic in ML
The Last step is to deploy the ML model for use. You need to continuously monitor the ML model even after deployment to ensure ML system is working as per plan.
Key Takaways
Before jumping onto ML algorithms, we suggest you to clear your basic concepts on mathematics, statistics and python. This will help you in understanding the machine learning concepts to solve the real life problem.
There are lot of parameter selection we do during ML model development to ensure ML model works perfectly. These parameter selection required in-depth knowledge of basic concepts working behind the ML model.