Machine Learning Specialist Program

Imagine a day where you’re not just solving puzzles, but creating the very algorithms that can predict outcomes, understand patterns, and even drive decisions in industries ranging from healthcare to finance.

As a Machine Learning Specialist, your work life is a thrilling blend of innovation and impact. From refining algorithms to deploying AI models in real-world applications, the life of a Machine Learning Specialist is both dynamic and rewarding. Explore the endless possibilities and become a pivotal part of the technological revolution.

Syllabus:

• Week 1: Introduction

• Introduction to machine learning concepts and applications

• Types of machine learning: supervised learning, unsupervised learning, and reinforcement learning

• Basics of Python programming for machine learning

• Week 3: Supervised Learning - Regression

• Linear regression: simple and multiple regression

• Polynomial regression and regularization techniques (Ridge, Lasso)

• Model evaluation metrics: Mean Squared Error (MSE), R-squared, Adjusted R-squared

• Week 5: Unsupervised Learning - Clustering

• K-means clustering

• Hierarchical clustering

• Model evaluation metrics: silhouette score, Davies-Bouldin index

• Week 7: Neural Networks and Deep Learning

• Introduction to artificial neural networks (ANN)

• Basics of TensorFlow or PyTorch

• Building and training deep neural networks for classification and regression tasks

• Week 9,10,11,12: Final Project

• Apply machine learning techniques learned throughout the program to solve a real-world problem or complete a data science project

• Develop a comprehensive project report and present findings to peers and instructors

• Week 2: Data Preprocessing

• Introduction to machine learning concepts and applications

• Types of machine learning: supervised learning, unsupervised learning, and reinforcement learning

• Basics of Python programming for machine learning

• Week 4: Supervised Learning - Classification

• Logistic regression

• Decision trees and ensemble methods (Random Forest, Gradient Boosting)

• Model evaluation metrics: accuracy, precision, recall, F1-score, ROC-AUC

• and more