Machine Learning

Categories: AI & ML

About Course

This course covers the foundational concepts and techniques in machine learning. It focuses on both supervised and unsupervised learning, as well as advanced topics like deep learning and reinforcement learning. You’ll learn how to build, train, and evaluate machine learning models, apply them to real-world problems, and gain insights from data.

Introduction to Machine Learning:
Understand the basics of machine learning and its applications in real-world problems.
Learn the types of machine learning: Supervised, Unsupervised, and Reinforcement Learning.
Supervised Learning:
Master classification and regression techniques.
Study algorithms like Linear Regression, Logistic Regression, Decision Trees, and Support Vector Machines (SVM).
Unsupervised Learning:
Learn about clustering and association algorithms such as K-Means, Hierarchical Clustering, and Apriori.
Explore dimensionality reduction methods like Principal Component Analysis (PCA).
Deep Learning and Neural Networks:
Dive into deep learning concepts with Neural Networks, Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs).
Learn to implement deep learning algorithms for complex tasks like image recognition and natural language processing.
Advanced Machine Learning Techniques:
Understand Ensemble Learning, including Random Forests, Boosting, and Stacking.
Explore Transfer Learning and AutoML to apply pre-trained models to new tasks.
Reinforcement Learning:
Learn how machines can make decisions and improve performance over time with Reinforcement Learning algorithms like Q-Learning.
Generative Models:
Gain insight into advanced topics like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).
Machine Learning Applications:
Explore real-world applications, such as image classification, natural language processing, autonomous vehicles, and robotics.

Course Content

Introduction to Machine Learning
overview of machine learning (ML), explaining its basic concepts, types, and the core algorithms used in ML. This foundational module will help you understand the difference between supervised, unsupervised, and reinforcement learning, as well as the kinds of problems each can solve. You'll also learn about the essential components that make up an ML system, such as data, models, and algorithms.

Overview of Machine Learning
Types of Machine Learning
Key Concepts in Machine Learning

Data Preprocessing and Feature Engineering
focuses on the essential steps of preparing data before feeding it into a machine learning model. This includes data preprocessing (cleaning, transforming, and normalizing data) and feature engineering (creating new features to improve model performance). The goal of this module is to ensure that the data used for training is ready and optimized for accurate predictions.

Supervised Learning Algorithms
focuses on supervised learning, a type of machine learning where models are trained using labeled data. In this module, we’ll explore some of the most common supervised learning algorithms, understand how they work, and discuss their strengths and weaknesses. The main goal is to help you learn how to implement and use these algorithms for classification and regression tasks.

Unsupervised Learning Algorithms
dives into unsupervised learning, where the machine learning model is trained using unlabeled data. The goal of unsupervised learning is to discover hidden patterns, groupings, or structures within the data. This module focuses on key unsupervised learning algorithms, including clustering and dimensionality reduction techniques, that help identify and analyze these patterns.

Reinforcement Learning and Advanced Topics
introduces Reinforcement Learning (RL), a powerful paradigm in machine learning where an agent learns how to make decisions by interacting with an environment. Unlike supervised or unsupervised learning, in reinforcement learning, the model learns through trial and error, receiving feedback in the form of rewards or penalties. This module will also touch on advanced topics such as Q-learning, Deep Q Networks (DQN), and policy gradient methods, which are used to tackle more complex decision-making tasks.

Neural Networks and Deep Learning
introduces Neural Networks (NN) and Deep Learning (DL), key technologies that have revolutionized the field of machine learning. These models are inspired by the structure of the human brain and are capable of learning from vast amounts of data. Deep learning specifically refers to neural networks with many layers (hence the term "deep"). This module will cover the fundamental principles of neural networks, including basic architectures, forward and backward propagation, and deep learning concepts like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN).

Advanced Machine Learning Techniques and Emerging Trends
focuses on some of the advanced techniques and emerging trends in the field of machine learning that are pushing the boundaries of what’s possible. These include ensemble learning, transfer learning, and reinforcement learning advancements. Additionally, the module introduces AutoML (Automated Machine Learning), which aims to make machine learning accessible to non-experts by automating the model-building process. We will also explore the latest advancements in unsupervised learning, generative models, and the integration of machine learning with edge computing.

ASSESSMENT AND QUIZ

Student Ratings & Reviews

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Machine Learning

Machine Learning

About Course

What Will You Learn?

Course Content

Overview of Machine Learning

Types of Machine Learning

Key Concepts in Machine Learning

Data Preprocessing

Feature Engineering

Handling Categorical Data

Linear Regression

Logistic Regression

Decision Trees

K-Means Clustering

Hierarchical Clustering

Principal Component Analysis (PCA)

Introduction to Reinforcement Learning (RL)

Q-Learning

Deep Q Networks (DQN)

Introduction to Neural Networks

Deep Learning and Advanced Neural Networks

Applications of Neural Networks and Deep Learning

Ensemble Learning

Transfer Learning

Automated Machine Learning (AutoML)

ASSESSMENT AND QUIZ

ASSESSMENT

Your Instructor

VividPrep

Machine Learning

About Course

What Will You Learn?

Course Content

Overview of Machine Learning

Types of Machine Learning

Key Concepts in Machine Learning

Data Preprocessing

Feature Engineering

Handling Categorical Data

Linear Regression

Logistic Regression

Decision Trees

K-Means Clustering

Hierarchical Clustering

Principal Component Analysis (PCA)

Introduction to Reinforcement Learning (RL)

Q-Learning

Deep Q Networks (DQN)

Introduction to Neural Networks

Deep Learning and Advanced Neural Networks

Applications of Neural Networks and Deep Learning

Ensemble Learning

Transfer Learning

Automated Machine Learning (AutoML)

ASSESSMENT AND QUIZ

ASSESSMENT

Student Ratings & Reviews

Your Instructor

VividPrep

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