Machine Learning Algorithms and Models
( 25 Modules )

Module #1
Introduction to Machine Learning
Overview of machine learning, types of machine learning, and importance of machine learning in real-world applications

Module #2
Mathematical Foundations of Machine Learning
Linear algebra, calculus, probability, and statistics for machine learning

Module #3
Supervised Learning Fundamentals
Introduction to supervised learning, regression, and classification

Module #4
Linear Regression
Simple and multiple linear regression, cost function, and regularization techniques

Module #5
Logistic Regression
Binary and multi-class logistic regression, cost function, and regularization techniques

Module #6
Decision Trees
Introduction to decision trees, CART algorithm, and decision tree regression

Module #7
Random Forests
Ensemble learning, random forests, and feature importance

Module #8
Support Vector Machines (SVMs)
Introduction to SVMs, linear and non-linear SVMs, and kernel trick

Module #9
Unsupervised Learning Fundamentals
Introduction to unsupervised learning, clustering, and dimensionality reduction

Module #10
K-Means Clustering
K-means algorithm, cluster evaluation, and hierarchical clustering

Module #11
Hierarchical Clustering
Agglomerative and divisive hierarchical clustering, and dendrogram interpretation

Module #12
Principal Component Analysis (PCA)
Introduction to PCA, dimensionality reduction, and feature extraction

Module #13
t-SNE and Autoencoders
t-SNE for visualization, autoencoders for dimensionality reduction, and anomaly detection

Module #14
Reinforcement Learning Fundamentals
Introduction to reinforcement learning, Markov decision processes, and Q-learning

Module #15
Deep Learning Fundamentals
Introduction to deep learning, neural networks, and backpropagation

Module #16
Convolutional Neural Networks (CNNs)
Introduction to CNNs, image classification, and object detection

Module #17
Recurrent Neural Networks (RNNs)
Introduction to RNNs, LSTM, GRU, and sequence prediction

Module #18
Natural Language Processing (NLP) with Deep Learning
Text preprocessing, word embeddings, and language models

Module #19
Model Evaluation and Selection
Metrics for evaluation, cross-validation, and model selection techniques

Module #20
Hyperparameter Tuning
Grid search, random search, and Bayesian optimization for hyperparameter tuning

Module #21
Model Deployment and Maintenance
Model deployment, model serving, and model maintenance strategies

Module #22
Handling Imbalanced Datasets
Techniques for handling imbalanced datasets, including oversampling, undersampling, and cost-sensitive learning

Module #23
Fairness and Ethics in Machine Learning
Fairness and ethics in machine learning, bias detection, and mitigation techniques

Module #24
Advanced Topics in Machine Learning
Transfer learning, attention mechanisms, and graph neural networks

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Machine Learning Algorithms and Models career

WIZAPE

ONLINE RESOURCES

ABOUT WIZAPE

ONLINE PORTAL

Our priority is to cultivate a vibrant community before considering the release of a token. By focusing on engagement and support, we can create a solid foundation for sustainable growth. Let’s build this together!

We're giving our website a fresh new look and feel! 🎉 Stay tuned as we work behind the scenes to enhance your experience.
Get ready for a revamped site that’s sleeker, and packed with new features. Thank you for your patience. Great things are coming!

CONTACT-US PRIVACY POLICY

Machine Learning Algorithms and Models ( 25 Modules )

Machine Learning Algorithms and Models
( 25 Modules )