Top 25 things you should know in order to start machine learning:

1. Understand the basics of machine learning: Machine learning is a subgroup of artificial intelligence that focuses on algorithms and data to replicate how a human learns a task and improves skill as the machine is given more data¹.

There are various types of machine learning algorithms, including supervised learning (in which regression and classification techniques are used on labeled datasets), unsupervised learning (in which dimensionality reduction and clustering techniques are used on unlabeled datasets), and reinforcement learning (algorithm in which the model learns from its every action).

2. Learn programming for machine learning: Before you start with machine learning, it's important to have a good understanding of programming concepts and be comfortable with coding¹.

A number of programming languages, including Python, Java, JavaScript, C++, R, Julia, and MATLAB, can be used for machine learning. One of the most used programming languages for machine learning professionals is Python. It includes a large machine learning library, well-known ML algorithms, machine learning systems, advanced data structures, and analysis.

3. Collect and pre-process data: Machine learning algorithms require data to learn from. You'll need to know how to collect, clean, and pre-process data¹.

Pre-processing includes a number of techniques and actions:

Data cleaning. These techniques, manual and automated, remove data incorrectly added or classified.

Data imputations. Most ML frameworks include methods and APIs for balancing or filling in missing data. Techniques generally include imputing missing values with standard deviation, mean, median and k-nearest neighbors (k-NN) of the data in the given field.

Oversampling. Bias or imbalance in the dataset can be corrected by generating more observations/samples with methods like repetition, bootstrapping or Synthetic Minority Over-Sampling Technique (SMOTE), and then adding them to the under-represented classes.

Data integration. Combining multiple datasets to get a large corpus can overcome incompleteness in a single dataset.

Data normalization. The size of a dataset affects the memory and processing required for iterations during training. Normalization reduces the size by reducing the order and magnitude of data.

4. Analyze data: Data analysis is an important step in the machine learning process. You'll need to know how to explore and analyze data¹.

For data analysis, there are numerous AI technologies accessible. Top AI data analysis tools include Tableau, which offers dynamic dashboards and simple data visualization, RapidMiner, which has an intuitive user interface and a drag-and-drop framework generator to streamline data analysis for people with different skill sets, Microsoft Azure Machine Learning, KNIME, Google Cloud AutoML, PyTorch, DataRobot, and Talend.

5. Learn machine learning algorithms in depth: There are many machine learning algorithms to choose from. It's important to have a good understanding of the different algorithms and how they work¹.

One of the most widely used supervised learning methods for predicting and forecasting values that lie within a continuous range, such as sales figures or home prices, is linear regression . Other widely used algorithms include decision trees, naive Bayes, k-nearest neighbors, support vector machines, artificial neural networks, and logistic regression, which is mostly used for binary classification applications.

6. Learn deep learning: To learn deep learning, you can start by taking online courses or reading books on the subject. Some popular online courses include the Deep Learning Specialization on Coursera and the Introduction to Deep Learning course on edX. Some popular books on the subject include "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, and "Hands-On Machine Learning with Scikit-Learn and TensorFlow" by Aurélien Géron.

 

7. Work on projects: To work on machine learning projects, you can start by finding datasets that interest you and building models to solve problems using those datasets. Kaggle is a great resource for finding datasets and project ideas. You can also participate in machine learning competitions to gain experience and improve your skills.

 

8. Discover the ecosystem for machine learning: To discover the ecosystem for machine learning, you can start by exploring popular libraries and tools such as scikit-learn, TensorFlow, Keras, PyTorch, and XGBoost. You can also read blogs and follow machine learning experts on social media to stay up-to-date with the latest developments in the field.

 

9. Use tools for big data analysis: To use tools for big data analysis, you can start by learning about Apache Spark and Hadoop. These are popular tools for processing large amounts of data. You can take online courses or read books to learn how to use these tools.

 

10. Use libraries like NumPy, Pandas, Matplotlib, and Seaborn: To use these libraries, you can start by installing them on your computer and reading their documentation to learn how to use them. You can also find tutorials and examples online to help you get started.

 

11. Understand ML Algorithms: To understand machine learning algorithms, you can start by taking online courses or reading books on the subject. Some popular online courses include the Machine Learning course on Coursera and the Introduction to Machine Learning with Python course on edX. Some popular books on the subject include "The Hundred-Page Machine Learning Book" by Andriy Burkov and "Machine Learning: A Probabilistic Perspective" by Kevin P. Murphy.

 

12. Learn ML with Weka (no code): To learn machine learning with Weka, you can start by downloading Weka and reading its documentation to learn how to use it. You can also find tutorials and examples online to help you get started.

 

13. Learn ML with Python (scikit-learn): To learn machine learning with Python and scikit-learn, you can start by installing Python and scikit-learn on your computer and reading their documentation to learn how to use them. You can also find tutorials and examples online to help you get started.

 

14. Learn ML with R (caret): To learn machine learning with R and caret, you can start by installing R and caret on your computer and reading their documentation to learn how to use them. You can also find tutorials and examples online to help you get started.

 

15. Learn Time Series Forecasting: To learn time series forecasting, you can start by taking online courses or reading books on the subject. Some popular online courses include the Time Series Forecasting course on Coursera and the Practical Time Series Analysis course on edX. Some popular books on the subject include "Forecasting: Principles and Practice" by Rob J Hyndman and George Athanasopoulos, and "Time Series Analysis" by James D Hamilton.

 

16. Learn Data Preparation: To learn data preparation, you can start by taking online courses or reading books on the subject. Some popular online courses include the Data Wrangling with Pandas course on Coursera and the Data Cleaning in R course on edX. Some popular books on the subject include "Python for Data Analysis" by Wes McKinney, and "R for Data Science" by Hadley Wickham.

 

17. Learn Intermediate Code ML Algorithms: To learn intermediate code machine learning algorithms, you can continue taking online courses or reading books that cover more advanced topics in machine learning.

 

18. Learn XGBoost Algorithm: To learn the XGBoost algorithm, you can start by installing XGBoost on your computer and reading its documentation to learn how to use it. You can also find tutorials and examples online to help you get started.

 

19. Learn Imbalanced Classification: To learn imbalanced classification, you can start by taking online courses or reading books that cover this topic in detail.

 

20. Learn Deep Learning (Keras): To learn deep learning with Keras, you can start by installing Keras on your computer and reading its documentation to learn how to use it. You can also find tutorials and examples online to help you get started.

 

21. Learn Deep Learning (PyTorch): To learn deep learning with PyTorch, you can start by installing PyTorch on your computer and reading its documentation to learn how to use it. You can also find tutorials and examples online to help you get started.

 

22. Learn Better Deep Learning: To learn better deep learning, you can continue taking online courses or reading books that cover more advanced topics in deep learning.

 

23. Learn Ensemble Learning: To learn ensemble learning, you can start by taking online courses or reading books that cover this topic in detail.

 

24. Learn Long Short-Term Memory: To learn Long Short-Term Memory (LSTM), you can start by taking online courses or reading books that cover this topic in detail.

 

25. Learn Natural Language (Text): To learn Natural Language Processing (NLP), you can start by taking online courses or reading books on the subject. Some popular online courses include the Natural Language Processing with Classification and Vector Spaces course on Coursera and the Natural Language Processing Fundamentals in Python course on DataCamp. Some popular books on the subject include "Speech and Language Processing" by Dan Jurafsky and James H Martin, and "Natural Language Processing with Python" by Steven Bird, Ewan Klein, and Edward Loper.

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References:

(1) Machine Learning Skills: Your Guide to Getting Started. https://www.coursera.org/articles/machine-learning-skills.
(2) How To Learn Machine Learning From Scratch [2023 Guide] - Springboard. https://www.springboard.com/blog/data-science/how-to-learn-machine-learning/.
(3) Start Here with Machine Learning. https://machinelearningmastery.com/start-here/.
(4) Getty Images. https://www.gettyimages.com/detail/photo/robot-with-education-hud-royalty-free-image/966248982.