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Advanced Machine Learning

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40 hours

About Course

“Advanced Machine Learning encompasses a sophisticated realm of techniques and methodologies that surpass the foundational principles of traditional algorithms. Beyond basic supervised learning, advanced topics delve into the intricacies of ensemble learning, where predictions from multiple models are combined to enhance performance. Semi-supervised and unsupervised learning tackle challenges presented by partially labeled or unlabeled datasets, offering insights into self-training, co-training, and clustering. Transfer learning enables the application of knowledge gained in solving one problem to related tasks, especially beneficial when data for the target task is limited. Other facets include multi-instance learning, Bayesian methods incorporating statistical uncertainty, online and incremental learning for dynamically evolving data, and the automation of the machine learning process through AutoML. Advanced machine learning also addresses critical issues such as model interpretability (Explainable AI), anomaly detection, robustness against adversarial attacks, and ethical considerations regarding biases and fairness in model predictions. The field continuously evolves, with researchers exploring cutting-edge methodologies to tackle real-world challenges in various domains.”

In an advanced machine learning course or self-study program, you can expect to learn a comprehensive set of skills and concepts that go beyond the basics. The exact curriculum may vary depending on the specific course or learning resource, but here's a general outline of what you might learn:
Ensemble Learning:
Understand and implement ensemble methods such as Random Forests, AdaBoost, and Gradient Boosting to improve model performance through the combination of multiple learners.
Semi-Supervised and Unsupervised Learning:
Explore techniques for learning from partially labeled or unlabeled datasets, including self-training, co-training, and clustering methods.
Transfer Learning:
Learn how to leverage pre-trained models and adapt them to new tasks, especially in situations where labeled data for the target task is limited.
Multi-Instance Learning:
Understand the principles behind learning from bags of instances rather than individual instances, with applications in scenarios where labels are assigned to sets of instances.
Online and Incremental Learning:
Acquire skills in updating machine learning models continuously as new data becomes available, suitable for dynamic environments.
Bayesian Methods:
Explore Bayesian statistics and methods to model uncertainty and incorporate prior knowledge into the learning process.
AutoML (Automated Machine Learning):
Gain hands-on experience with tools and algorithms that automate aspects of the machine learning process, including hyperparameter tuning, feature engineering, and model selection.

"Advanced Machine Learning" refers to the study, development, and application of sophisticated and complex machine learning techniques beyond the fundamentals. As of my last knowledge update in January 2022, the field of advanced machine learning includes a range of topics and methodologies that go beyond traditional algorithms. Here are some key aspects of advanced machine learning:
Ensemble Learning:
Techniques that involve combining the predictions of multiple machine learning models to improve overall performance. Ensemble methods include bagging (e.g., Random Forests) and boosting (e.g., AdaBoost, Gradient Boosting).
Semi-Supervised and Unsupervised Learning:
Advanced methods for learning from partially labeled or unlabeled datasets. This includes approaches like self-training, co-training, and clustering.
Transfer Learning:
The practice of leveraging knowledge gained while solving one problem and applying it to a different, but related, problem. This is especially useful when labeled data for the target task is limited.
Multi-Instance Learning:
Learning from datasets where each example is a bag of instances rather than a single instance. This is applicable in scenarios where the labels are assigned to sets of instances.
Online and Incremental Learning:
Techniques for updating machine learning models continuously as new data becomes available. This is crucial in dynamic environments where the data distribution may change over time.
Bayesian Methods:
Approaches that use Bayesian statistics to model uncertainty and incorporate prior knowledge into the learning process. Bayesian methods are particularly useful in situations with limited data.
AutoML (Automated Machine Learning):
The use of automated tools and algorithms to streamline the machine learning process, including hyperparameter tuning, feature engineering, and model selection.
Explainable AI (XAI):
Techniques and models designed to make machine learning models more interpretable and understandable. This is especially important in applications where transparency and accountability are critical.
Anomaly Detection:
Identifying patterns in data that do not conform to expected behavior. Anomaly detection is crucial in various domains, including fraud detection, network security, and predictive maintenance.
Reinforcement Learning:
Going beyond the basics of reinforcement learning, advanced topics include deep reinforcement learning, policy optimization methods, and addressing challenges like exploration-exploitation trade-offs.
Time Series Analysis and Forecasting:
Advanced methods for handling time-dependent data, such as recurrent neural networks (RNNs), long short-term memory networks (LSTMs), and attention mechanisms.
Robust Machine Learning:
Techniques to improve the resilience of machine learning models against adversarial attacks and noisy data.
Ethics and Fairness in Machine Learning:
Considering the ethical implications of machine learning algorithms, addressing biases, and ensuring fairness in model predictions.
Causal Inference:
Understanding and modeling causal relationships in data to make more informed and reliable predictions.
Continued advancements in machine learning research contribute to the development of these advanced techniques. As the field evolves, practitioners and researchers often engage with interdisciplinary approaches and stay abreast of the latest developments in both academia and industry to apply advanced machine learning effectively to real-world problems.

Course Content

Introduction to Machine Learning

Types of Human Learning
Problems Not to Be Solved Using Machine Learning
Applications of Machine Learning
State-of-The-Art Languages/Tools in Machine Learning
Issues in Machine Learning
Intruduction To Machine Learning (MOCK TEST)

Preparing to Model

Modelling and Evaluation

Basics of Supervised Learning

Bayesian Concept Learning

Supervised Learning – Classification

Supervised Learning Regression

Introduction to Supervised Learning – Regression
Example of Regression
Common Regression Algorithms
Assumptions in Regression Analysis
Main Problems in Regression Analysis
Improving Accuracy of the Linear Regression Model
Polynomial Regression Model
Logistic Regression
Maximum Likelihood Estimation (MLE)
Supervised Learning – Regression (MOCK TEST)

Unsupervised Learning

Free

Enrollment validity: Lifetime

Level Expert
Duration 40 hours
Certificate Certificate of completion

Advanced Machine Learning

About Course

What Will You Learn?

Material Includes

Course Content

Introduction to Machine Learning

Types of Human Learning

Problems Not to Be Solved Using Machine Learning

Applications of Machine Learning

State-of-The-Art Languages/Tools in Machine Learning

Issues in Machine Learning

Intruduction To Machine Learning (MOCK TEST)

Preparing to Model

Machine Learning Activities

Basic Types of Data in Machine Learning

Exploring Structure of Data

Data Quality and Remediation

Data Pre-Processing

Preparing to model (MOCK TEST)

Modelling and Evaluation

Selecting a Model

Training a Model (for Supervised Learning)

Evaluating Performance of a Model

Improving Performance of a Model

MODELLING AND EVALUATION

Basics of Supervised Learning

Introduction to Feature Engineering

Feature Transformation

Feature Subset Selection

Additional Considerations in Feature Engineering

BASIC OF SUPERVISED LEARNING (MOCK TEST)

Bayesian Concept Learning

Why Bayesian Methods are Important

Bayes’ Theorem

Components of a BBN

Applications of Bayesian Belief Networks

Bayesian concept learning (MOCK TEST)

Supervised Learning – Classification

Introduction to Supervised Learning – Classification

Example of Supervised Learning

Classification Model

k-Nearest Neighbors (k-NN)

Decision Tree

Random Forest

Support Vector Machines (SVM)

Supervised Learning Classification (MOCK TEST)

Supervised Learning Regression

Introduction to Supervised Learning – Regression

Example of Regression

Common Regression Algorithms

Assumptions in Regression Analysis

Main Problems in Regression Analysis

Improving Accuracy of the Linear Regression Model

Polynomial Regression Model

Logistic Regression

Maximum Likelihood Estimation (MLE)

Supervised Learning – Regression (MOCK TEST)

Unsupervised Learning

Unsupervised vs Supervised Learning

Application of Unsupervised Learning

Clustering as a Machine Learning Task

Different Types of Clustering Techniques

Finding Patterns Using Association Rule

The Apriori Algorithm for Association Rule Learning

Build the Apriori Principle Rules

Unsupervised Learning (MOCK TEST)

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