Machine Learning – Immersive Bootcamp
- Created By ebrahim khaja
- Posted on July 2nd, 2024
- Overview
- Prerequisites
- Audience
- Audience
- Curriculum
Description:
Join our intensive 5-day Machine Learning Bootcamp designed to equip you with the essential skills and knowledge to excel in the field of data science. Starting with an introduction to Python and foundational statistical methods, you'll learn to visualize and analyze data effectively. We'll then delve into the core principles of machine learning, covering both theoretical concepts and practical applications. You'll explore key algorithms, including PCA, k-Nearest Neighbors, linear regression, decision trees, and ensemble methods like random forests. By the end of the bootcamp, you'll have a solid understanding of how to formulate and solve real-world problems using advanced machine learning techniques. This bootcamp is perfect for anyone looking to deepen their expertise in data science and machine learning.
Duration: 5 Days
Course Code: BDT347
Learning Objectives
- Gain a solid foundation in Python programming and its applications in data science
- Develop skills to perform EDA to uncover patterns and insights from data.
- Comprehend the significance of machine learning, formulate machine learning problems, and explore supervised and unsupervised learning.
- Learn and apply essential machine learning algorithms such as PCA, KNN, linear regression, decision trees, ensemble methods (like Random Forests), SVM, logistic regression, and Naive Bayes.
- Understand the concepts of generalization and overfitting, and master the use of training, validation, and testing datasets to develop robust machine learning models.
- Basic Programming Knowledge: Familiarity with any programming language like Python is preferred but not mandatory.
- Understanding of Mathematics: Basic knowledge of linear algebra, calculus, and probability.
- Statistical Concepts: Fundamental understanding of descriptive and inferential statistics.
- Eagerness to learn and apply new concepts in data science and machine learning.
This course is suitable for:
- Software Developers
- Data Scientists
- AI/ML Engineers
- Tech Enthusiasts
This course is suitable for:
- Software Developers
- Data Scientists
- AI/ML Engineers
- Tech Enthusiasts
Course Outline:
Data Science Toolkits, Statistical & Exploratory Data Analytics
- Introduction to Python
- Python for Data science
- Math for Machine Learning
- Data Visualization in Python
- CRISP-DM Framework
- Inferential Statistics
- Hypothesis Testing
- Exploratory Data Analytics
Introduction to Machine Learning
- Motivation & Role of Machine learning in computer science & problem solving
- Problem Formulation (Classification and Regression)
- Paradigms of learning
- Supervised Learning
- Unsupervised Learning
Fundamentals to Machine Learning
- PCA and Dimensionality Reduction
- Nearest Neighbours and KNN
- Linear Regression
- Decision Tree Classifiers
- Notion of Generalization and concern of Overfitting
- Notion of Training, Validation and Testing
Machine Learning Algorithms
- Linear SVM
- Logistic Regression
- Naive Bayes
- Decision Trees
- Ensemble Techniques
- Random Forests
