Machine learning – ezyintern

About Course

This course provides a comprehensive introduction to the field of Machine Learning (ML). Students will gain a solid foundation in ML concepts, algorithms, and applications. The course covers key topics such as supervised and unsupervised learning, neural networks, and deep learning. Practical hands-on experience is emphasized through coding exercises and a capstone project. By the end of the course, students will be equipped with the skills to apply ML techniques to real-world problems and understand the ethical considerations in the field.

Learning Format:
– Live interactive classes
– Recorded tutorials for flexible learning
– Hands-on exercises and projects to concepts
– 24/7 doubt clearing facility
Format:

Self-paced online course with video lectures and practical exercises.
Real-world case studies and projects for hands-on application.
Capstone project to showcase mastery of Machine learning Courses skills.

Assignments:
– Daily hands-on exercises to Machine learning Courses
– Project-based assessments to apply knowledge in practical scenarios

Final Project:

Final Project Overview
- Planning and executing a Machine learning Courses project
- Showcasing learned skills
Course Recap and Next Steps
- Reviewing key concepts
- Resources for ongoing learning
Certification:
- Comprehensive assessment covering the entire course
- Practical problem-solving and application of knowledge

Foundational Concepts: Understand key principles of Machine Learning, including supervised and unsupervised learning.
Practical Skills: Gain hands-on experience with Python, NumPy, and Pandas for data manipulation and analysis.
Data Preprocessing: Learn essential techniques for data cleaning, handling missing values, and feature scaling.
Ethical Considerations: Grasp ethical implications in ML, emphasizing responsible AI practices and addressing bias.
Reinforcement Learning: Understand fundamentals, including Q-learning and Deep Q Networks (DQN).

Course Content

Introduction to Machine Learning

Overview of Machine Learning

00:00
Historical context and evolution

00:00
Types of Machine Learning: Supervised, Unsupervised, Reinforcement Learning

00:00
Real-world applications and case studies

00:00
Python and Jupyter Notebooks for ML

00:00

Fundamentals of Python and NumPy

Data Preprocessing and Exploratory Data Analysis (EDA)

Supervised Learning – Regression

Supervised Learning – Classification

Unsupervised Learning – Clustering

Dimensionality Reduction

Model Evaluation and Hyperparameter Tuning

Neural Networks and Deep Learning

Natural Language Processing (NLP) and Text Mining

Reinforcement Learning

Special Topics in Machine Learning

Capstone Project

Ethical Considerations and Future Directions

Quiz:

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About Course

What Will You Learn?

Course Content

Introduction to Machine Learning

Overview of Machine Learning

Historical context and evolution

Types of Machine Learning: Supervised, Unsupervised, Reinforcement Learning

Real-world applications and case studies

Python and Jupyter Notebooks for ML

Fundamentals of Python and NumPy

Python basics: variables, data types, loops, and functions

Introduction to NumPy for numerical operations

Handling data with Pandas

Data Preprocessing and Exploratory Data Analysis (EDA)

Data cleaning and handling missing values

Feature scaling and normalization

Exploratory Data Analysis techniques

Visualization with Matplotlib and Seaborn

Supervised Learning – Regression

Linear Regression

Multiple Regression

Polynomial Regression

Model evaluation metrics: MSE, RMSE, R-squared

Supervised Learning – Classification

Logistic Regression

Decision Trees

Random Forests

Support Vector Machines (SVM)

k-Nearest Neighbors (KNN)

Model evaluation metrics: Accuracy, Precision, Recall, F1 Score, ROC-AUC

Unsupervised Learning – Clustering

K-Means Clustering

Hierarchical Clustering

DBSCAN

Gaussian Mixture Models (GMM)

Evaluation metrics for clustering

Dimensionality Reduction

Principal Component Analysis (PCA)

t-Distributed Stochastic Neighbor Embedding (t-SNE)

Feature selection techniques

Model Evaluation and Hyperparameter Tuning

Cross-validation

Grid Search and Random Search

Hyperparameter tuning strategies

Model deployment considerations

Neural Networks and Deep Learning

Introduction to Neural Networks

Building a basic neural network with TensorFlow/Keras

Convolutional Neural Networks (CNN) for image data

Recurrent Neural Networks (RNN) for sequential data

Transfer learning

Natural Language Processing (NLP) and Text Mining

Basics of NLP

Text preprocessing techniques

Word embeddings (Word2Vec, GloVe)

Sentiment analysis

Named Entity Recognition (NER)

Reinforcement Learning

Basics of reinforcement learning

Markov Decision Processes (MDP)

Q-learning

Deep Q Networks (DQN)

Special Topics in Machine Learning

Explainable AI (XAI)

Bias and fairness in Machine Learning

Handling imbalanced datasets

Current trends and advancements in ML

Capstone Project

Applying Machine Learning on a real-world problem

Designing and implementing a complete ML solution

Presentation and documentation of the project

Ethical Considerations and Future Directions

Ethics in Machine Learning

Responsible AI practices

Future directions and emerging trends in ML

Quiz:

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