Machine Learning

Machine Learning

Overview

I started my machine learning journey by enrolling in comprehensive online course (Machine Learning A-Z), highly endorsed by esteemed machine learning engineers. This course places a strong emphasis on the fundamental principles and industry-leading practices. Through it, I specialized in developing and training models to analyze data, identify patterns, and build predictive models that drive automation and improve decision-making.

Key Skills & Technologies

• Programming: Python, R
• Frameworks/Libraries: Scikit-Learn, Pandas, Matplotlib, Numpy
• Tools: 
• IDEs: Jupyter Notebook, Google Colab
• Concepts: 
  • Data Pre-processing
  • Regression

Notable Project

1. Real Estate Price Forecasting Model
   • Predictive Modeling for Real Estate Investment: Developed an advanced machine learning model to predict housing prices, providing investors with accurate forecasts to estimate potential revenue and make informed investment decisions.
   • Feature Engineering & Data Preprocessing: Employed techniques such as feature engineering, data cleaning, and normalization to ensure the model's predictions were based on high-quality, relevant data.
   • Algorithm Selection & Optimization: Applied various regression and time-series forecasting algorithms, fine-tuning them to maximize prediction accuracy and reliability for dynamic real estate markets.

Achievements & Impact

• Enhanced Decision-Making: Enabled real estate investors to make data-driven investment decisions by predicting market trends and optimizing pricing strategies.
• Streamlined Data Processing: Reduced manual data analysis time by 40% through the development and implementation of automated ML pipelines, improving efficiency and scalability in model training and prediction.
• Improved Revenue Estimation: The model helped stakeholders make better-informed decisions, potentially increasing revenue by offering precise forecasts on housing price trends.

Future Goals & Learning (in progress)

Completed 20% of the course; remaining topics include:

• Classification 
• Clustering 
• Association Rule Learning
• Reinforcement Learning
• Natural Language Processing
• Deep Learning
• Dimensionality Reduction
• Model Selection & Boosting

I have no doubt that this course will equip me with the essential knowledge required to overcome any machine learning challenge that comes my way.

A detailed overview of the course concepts

Data Pre-processing

• Data pre-processing
  • Machine learning process
  • Splitting data
  • Feature scaling
• Data pre-processing
  • Import library
  • Import dataset
  • Handling missing data
  • Encoding categorical data
  • Splitting dataset
  • Feature scaling

Regression

• Simple linear regression
  • Equation
  • Ordinary Least Squares
  • Import library
• Multiple linear regression
  • Equation
  • Assumptions of linear regression
  • Dummy variables 
  • P-value
  • Building a model
  • Import library
• Polynomial regression
  • Equation
  • Import library
• Support vector regression (SVR)
  • Equation
  • Import library
• Decision tree regression
• Random forest regression

Classification 

Clustering 

Association Rule Learning

Reinforcement Learning

Natural Language Processing

Deep Learning

Dimensionality Reduction

Model Selection & Boosting