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Machine Learning Bootcamp – Part 2: Deep Dive Skills Workshop
Description
Machine Learning Bootcamp – Part 2: Deep Dive Skills Introduction
Dive into advanced machine learning techniques with our Machine Learning Bootcamp – Part 2: Deep Dive Skills. This comprehensive course is structured to deepen your knowledge and skills in artificial intelligence and machine learning. Through hands-on training, you’ll master data manipulation, model development, and task simplification with cutting-edge technologies.
The course combines theory with practical sessions, including real-world examples, interactive labs, and detailed case studies, ensuring you can apply your knowledge effectively in any professional setting.
Explore core concepts such as regression analysis, classification, and model optimization, including hyperparameter tuning and feature engineering. Tackle complex challenges such as managing imbalanced datasets and implementing dimensionality reduction. With a focus on practical application, this bootcamp is designed to equip you with the ability to design, train, and optimize sophisticated machine learning models efficiently.
By the end of the course, you’ll possess the confidence and skills to undertake any machine learning project, leveraging the latest tools and best practices to drive results.
Machine Learning Bootcamp – Part 2: Course Objectives
Some of the core topics you’ll explore include:
- Regression Analysis: Understand and predict relationships between variables.
- Binary and Multiclass Classification: Categorize data into distinct classes.
- Hyperparameter Tuning: Optimize machine learning algorithms for peak performance.
- Feature Engineering: Enhance model accuracy through effective variable selection and transformation.
- Handling Imbalanced Datasets: Develop techniques for datasets with uneven class distribution.
- Dimensionality Reduction: Simplify models by reducing the number of variables.
- Ensemble Learning: Boost prediction accuracy by combining multiple models.
- Model Evaluation: Assess machine learning algorithm performance accurately.
- Python Programming for AI: Utilize Python to build AI-driven applications.
- Generalization Techniques: Create models that perform robustly on new, unseen data.
- Data Preprocessing: Prepare data through cleaning, transforming, and normalizing for optimal training.
- Advanced Algorithms: Address complex data challenges with sophisticated machine learning algorithms.
- Ethics in AI and Machine Learning: Address ethical, security, and privacy issues.
- Using ChatGPT and Other Tools: Leverage AI tools to enhance efficiency and productivity.
- Building a Complete AI-Driven Application: Engage in a capstone project to build an AI application.
Prerequisites
To ensure a smooth learning experience and maximize the benefits of attending this course, you should have the following prerequisite skills:
- Python Programming: Students should have a strong understanding of the Python programming language. This includes the syntax of the language, how to define and use functions, and how to work with Python’s built-in data structures like lists and dictionaries.
- Basic Statistics (helpful but not required): A foundational understanding of statistics is crucial for many data science concepts. Students should be familiar with concepts such as mean, median, standard deviation, correlation, and the basics of statistical inference.
- Data Analysis: Experience with exploratory data analysis, including the ability to manipulate and analyze data, is crucial. This includes skills like cleaning data, investigating distributions and correlations, and creating visualizations.
- Basic Machine Learning Knowledge: While the course will likely dive into machine learning in detail, having a basic understanding of what machine learning is and the types of problems it can solve will be useful. This includes familiarity with concepts such as training data, testing data, overfitting, underfitting, and cross-validation.
Audience
- This course is ideally suited for Python developers, data analysts, and aspiring data scientists looking to expand their skills into AI and Machine Learning.
- It is also highly beneficial for product managers and business leaders aiming to acquire a hands-on understanding of AI’s impact on product development and business strategy.
Machine Learning Bootcamp – Part 2: Deep Dive Skills Outline
Introduction and Regression
- Understanding the Python ecosystem for data science
- Review of Python libraries relevant to data science
- Basics of regression analysis
- Linear regression in Python
- Multiple regression analysis
- Hands-on Lab: Regression Analysis with Python
Classification and Cluster Analysis
- Understand and implement binary and multiclass classification.
- Implement and assess the quality of a cluster analysis.
- Logistic regression for binary classification
- Performance metrics for binary classification
- Hands-On Lab: Binary Classification
- Overview of multiclass classification
- Understanding and implementing RandomForest
- Hands-On Lab: Multiclass Classification with RandomForest
- Introduction to cluster analysis
- K-Means clustering in Python
- Assessing cluster quality
- Hands-On Lab: Cluster Analysis
Model Performance, Generalization, and Hyperparameter Tuning
- Evaluate model performance using relevant metrics.
- Understand and implement techniques for model generalization.
- Learn about hyperparameters and methods for tuning them.
- Understanding confusion matrix, precision, recall, F1 score
- ROC and AUC analysis
- Hands-On Lab: Model Performance Assessment
- Understanding overfitting and underfitting
- Cross-validation for model generalization
- Hands-On Lab: Model Generalization Techniques
- Introduction to hyperparameters and their importance
- Grid search and random search for hyperparameter tuning
- Hands-On Lab: Hyperparameter Tuning with Python
Model Interpretation, Dataset Analysis, Data Preparation
- Learn techniques for interpreting model coefficients and understanding feature importance.
- Hands-On Lab: Machine Learning Model Interpretation
- Techniques for data exploration and visualization
- Learn methods for data exploration, visualization, univariate, and multivariate analysis.
- Hands-On Lab: Dataset Analysis with Python
- Dealing with missing values
- Outlier detection and handling
- Encoding categorical variables
- Hands-On Lab: Data Preparation with Python
Feature Engineering, Imbalanced Datasets, Dimensionality Reduction, and Ensemble Learning
- Learn techniques for feature engineering and handling imbalanced datasets.
- Understand and implement dimensionality reduction techniques.
- Hands-On Lab: Feature Engineering and Dimensionality Reduction
- Learn about ensemble learning methods and their implementation.
- Implementing ensemble learning methods
- Hands-On Lab: Ensemble Learning with Python
Capstone Project / Workshop
- Students will build their own AI investor using Python. Students will gain an understanding of the stock market approach from a purely data-driven perspective and will use that to build a stock investor. Students will be able to customize the investor (aggressive or defensive).
- Hands-On Lab: Project Workshop
- Apply learned techniques to a given problem statement.
- Understand how to troubleshoot and improve model performance.
OPTIONAL / Additional Time Required / Project Presentations and Course Wrap-Up
- Present the final project and receive feedback.
- Review the key learning outcomes from the course.
$2295.00
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3 Days Course |