In this blog let's discuss what are the topics to be covered to become a Machine Learning engineer
What is Artificial Intelligence?
Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems.
AI vs Machine Learning vs Deep Learning
Types of Machine Learning (ML)
- Supervised
- Unsupervised
- Reinforcement
What is Supervised Machine Learning ?
Supervised learning is similar to a Supervisor or a Teacher, teaching or training the model with labelled data.
What is Unsupervised Machine Learning?
Unlike supervised learning, no teacher is provided that means no training will be given to the machine. Therefore the machine is restricted to find the hidden structure in unlabeled data by itself.
What is Reinforcement Machine Learning?
Area of ML concerned with low intelligent agents take actions in an environment to maximize its rewards
Deep Learning - Basics
Deep learning utilizes both structured and unstructured data for training.
After exploring these basics cover python topics
Python Basics
- Explore Jupyter notebook or Google colaboratory
- Python Basics
- Python Basics - Data types - int,float,string,complex,boolean
- Python Special Data types - List, tuple,dictinoary,set
- Operators
- Conditional statement
- Loops in python
- Functions
Python libraries
- Pandas
- Numpy
- Scikit learn
- Matplot lib
- Seaborn
Data Collection
You can collect data from
- Kaggle
- Direct import of data
- Via API
- UCI
- Google search Data
Pre processing
- Handle Missing values
- Data standardization
Mathematics Basics
- Algebra
- Statistics
- Probability
- Calculus
Training models
- What is machine learning model?
- How to select model for training?
- Model Optimization
- Model Evaluation
Types of models
Classification models:
- Logistic Regression
- Support Vector machine
- Decision Tree Classification
- Random Forest Classifier
- Nave Bayes
- K - Nearest Neighbors
Regression models:
- Linear Regression
- Lasso Regression
- Logistic Regression
- SVM Regression
- Random Forest Regression
- Decision Tree Regression Clustering models:
- K means clustering
- Hierarchical clustering
Association Models:
- Apriori
- Eclat
Bravo! Now you are known about what topics to be covered in Machine Learning, Then why are you waiting start explore, and practice more models and showcase your projects in you resume.