How to become a Machine Learning Engineer?

Step 1: Learn a Programming Language

• Popular Programming Language for Machine Learning

1. Python

   • Python is one of the leading programming languages for its simple syntax and readability
   • Python libraries for machine learning include:
     • sci-kit image
     • OpenCV
     • TensorFlow
     • PyTorch
     • Keras
     • NumPy
     • NLTK
     • SciPy
     • sci-kit learn
     • Seaborn
     • Matplotlib

2. R Language

   • The R programming language focuses primarily on numbers and has a wide range of data sampling, model evaluation, and data visualization techniques.
   • R comes with its own supply of packages for engineers to utilize to get their work done efficiently, such as:
     • Dplyr
     • tidyr
     • CARET
     • Ggplot2
     • MICE
     • PARTY
     • part
     • Shiny
     • Rmarkdown
     • random forest

3. C++

   • C++ is another popular programming language widely used for performance-critical applications that need memory management and speed at the forefront.
   • This top favorite has many machine learning and artificial intelligence libraries, such as:
     • Caffe
     • LightGBM
     • DyNet
     • Turi Create

4. Java

   • Java is some of the most widely used and multipurpose programming languages out there.
   • Most websites are created using these languages, so using them in machine learning makes the integration process much simpler.
   • Java machine learning libraries:
     • JavaML
     • Arbiter
     • Neuroph
     • Weka

5. JavaScript

   • JavaScript is some of the most widely used and multipurpose programming languages out there.
   • Most websites are created using these languages, so using them in machine learning makes the integration process much simpler.
   • JavaScript machine learning libraries:
     • Math.js
     • TensorFlow.js
     • OpnCV.js
     • Synaptic

6. Shell

   • Shell can be used to develop algorithms, machine learning models, and applications. It uses mathematical models to collect and prepare data.
   • Shell supplies you with an easy and simple way to process data with its powerful, quick, and text-based interface.
   • Shell machine learning libraries:
     • Ml-notebook
     • Dl-machine
     • Docker-prediction

7. Go

   • Go (Golang) is an open-sourced programming language that was created by Google.
   • This intuitive language is used in a variety of applications and is considered one of the fastest-growing programming languages.
   • Go machine learning libraries:
     • GoLearn
     • Gorgonia
     • eaopt

Step 2: Learn Mathematics for Machine Learning

• Importance of Mathematics topics needed for Machine Learning

• (15%) Algorithm and Complexity

• (15%) Multivariate Calculus

• (35%) Linear Algebra

• (25%) Probability and Statistics

• (10%) Others

• Having a basic understanding of Probability and Statistics is important when it comes to mastering Machine Learning.

Step 3: Learn Core Machine Learning Algorithms

• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning

1. Supervised Learning Algorithm :

• Regression
• Decision Tree
• Random Forest
• Classification

2. Unsupervised Learning Algorithm :

• Clustering
• Association Analysis
• Hidden Markov Model

3. Reinforcement Learning Algorithm :

• Supervised & Unsupervised

  • Regression
  • Decision Tree
  • Random Forest
  • Clustering

• Categorical

  • Classification
  • Association Analysis
  • Hidden Markov Model

Step 4: Learn the basic Libraries for Mathematics & Data Handling

• TensorFlow
• Keras
• PyTorch
• Scikit-learn
• Pandas
• NumPy
• Spark
• NLTK
• Theano
• mxnet

Step 5: Learn Deep Learning

• Deep learning is a subset of machine learning, which is essentially a neural network with three or more layers.

• These neural networks attempt to simulate the behavior of the human brain albeit far from matching its ability allowing it to “learn” from large amounts of data.

• While a neural network with a single layer can still make approximate predictions, additional hidden layers can help to optimize and refine for accuracy.

• Deep Learning Applications :

  • Law enforcement
  • Image Recognition
  • Financial services
  • Customer service
  • Healthcare
  • Automatic Machine Translation

Step 6: Start Working on Projects

• Machine Learning Projects Ideas :

  • Amazon Product Review Sentiment Analysis
  • Hate Speech Detection
  • COVID-19 Vaccine Analysis
  • WhatsApp Chats Analysis
  • Dogecoin Price Prediction
  • Social Media Ads Classification
  • Spotify Recommendation System
  • Bankruptcy Prediction Model
  • Instagram Algorithm
  • Netflix Data Analysis