How to become a Machine Learning Engineer?

1. Learn a Programming Language :

Popular Programming Languages for Machine Learning

• R
• Matlab
• SAS
• Python
• WEKA
• Excel

If you go with Python, you must learn sklearn for Machine Learning. Sklearn is a modern machine learning library written in Python.

2. Learn Mathematics for Machine Learning :

Importance of Mathematics topics needed for Machine Learning

• Linear Algebra - 35%
• Probability and Statistics - 25%
• Algorithm and Complexity - 15%
• Calculus - 15%
• Others - 10%

Having a basic understanding of probability and statistics is important when it comes to mastering Machine Learning.

3. Learn Core Machine Learning Algorithms :

Supervised Machine Learning :

• Regression :
• Linear Regression

• Polynomial Regression

• Decision Tree

• Random Forest Model

• Classification :

• KNN

• Trees

• Logistic Regression

• Naive-Bayes

• SVM

Unsupervised Machine Learning :

• Clustering :
• SVD
• PCA

• K-Means

• Association Analysis :

• Apriori

• FP-Growth

• Hidden Markev Model

Reinforcement Machine Learning

4. Learn the basics Libraries for Mathematical and Data Handling :

• Spark
• Pytorch
• Scikit-learn
• Keras
• Pandas
• mxnet
• Numpy
• NLTK
• TensorFlow

5. Learn Deep Learning