Roadmap For Machine Learning Engineer

Roadmap For Machine Learning Engineer

Hello Learners…

Welcome to my blog.

Table Of Contents

  • Introduction
  • Roadmap for machine learning
  • Python Programming
  • Exploratory Data Analysis(EDA)
  • Feature Engineering
  • Feature Selection
  • Machine Learning Algorithms
  • Hyper Parameter Tuning
  • Model Deployment
  • Summary
  • References

Introduction

In this post, We discuss a general roadmap for machine learning. If we want to start machine learning then this is the way we can follow.

Roadmap For Machine Learning Engineer


Here is a general roadmap for learning machine learning engineer

Python Programming

  • Start by getting a good understanding of basic programming concepts, and practice by building small projects.
  • Libraries That we have to learn
    • Seaborn
    • Pandas
    • Matplotlib
    • NumPy
    • Sklearn

Exploratory Data Analysis(EDA)

  •  Exploratory Data Analysis(EDA) is unavoidable and one of the major steps to fine-tune the given data set(s) in a different form of analysis to understand the insights of the key characteristics of various entities of the data set like column(s), row(s) by applying Pandas, NumPy, Statistical Methods, and Data visualization packages. 

Feature Engineering

  • Normalization
  • Standardization
  • Categorical Encoding
  • Handling Outliers
  • Handling Missing Values

Feature Selection

  • Correlation
  • Forward Elimination
  • Handling Missing Values
  • Univariate Selection
  • Feature Selection With Decision Tree

Machine Learning Algorithms

  • Learn about the fundamental concepts of machine learning, such as supervised and unsupervised learning, and different types of algorithms.
  • Here are some machine-learning algorithms that we can learn
    • Linear Regression
    • Logistic Regression
    • Decision Tree
    • Random Forest
    • XGBoost
    • KMeans
    • DBSCAN
  • Learn about common machine learning libraries and frameworks, such as TensorFlow, PyTorch, and scikit-learn.
  • Practice implementing and training machine learning models on real-world datasets. This can be done through online courses, Kaggle competitions, or your own personal projects.
  • As you gain more experience, consider specializing in a particular area of machine learning, such as natural language processing, computer vision, or reinforcement learning.

Hyper Parameter Tuning

  • Hyperparameter optimization or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm.
  • A hyperparameter is a parameter whose value is used to control the learning process. By contrast, the values of other parameters are learned.

Model Deployment

  • Machine learning model deployment is the process of placing a finished machine learning model into a live environment where it can be used for its intended purpose. Models can be deployed in a wide range of environments, and they are often integrated with apps through an API so they can be accessed by end users.

Stay up to date with the latest research and developments in the field by reading papers and blogs, and attending conferences and meetups.

Summary

This is the general roadmap for machine learning, by following these all steps we get an idea of how machine learning works and how to implement end-to-end machine learning projects from scratch.

Also refer to the other post related to this,

Happy Learning And Keep Learning…

Thank You…

References

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