AI and Machine Learning for Data Analysis
( 25 Modules )

Module #1
Introduction to AI and Machine Learning
Overview of AI and Machine Learning, importance in data analysis, and brief history

Module #2
Types of Machine Learning
Supervised, Unsupervised, and Reinforcement Learning, and their applications

Module #3
Mathematical Foundations of Machine Learning
Linear Algebra, Calculus, Probability, and Statistics refresher

Module #4
Python for Machine Learning
Introduction to Python, NumPy, Pandas, and Scikit-learn libraries

Module #5
Data Preprocessing and Feature Engineering
Handling missing values, feature scaling, encoding categorical variables, and feature selection

Module #6
Supervised Learning:Linear Regression
Simple and Multiple Linear Regression, cost function, and regularization techniques

Module #7
Supervised Learning:Logistic Regression
Logistic Regression, sigmoid function, and classification metrics

Module #8
Decision Trees and Random Forests
Decision Trees, Random Forests, and Ensemble Methods

Module #9
Unsupervised Learning:Clustering
K-Means, Hierarchical Clustering, and DBSCAN algorithms

Module #10
Unsupervised Learning:Dimensionality Reduction
PCA, t-SNE, and Autoencoders for dimensionality reduction

Module #11
Model Evaluation and Selection
Metrics for regression and classification, cross-validation, and hyperparameter tuning

Module #12
Deep Learning Fundamentals
Introduction to Neural Networks, activation functions, and backpropagation

Module #13
Convolutional Neural Networks (CNNs)
CNNs for image classification, object detection, and image segmentation

Module #14
Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) Networks
RNNs, LSTMs, and GRUs for sequential data and time series forecasting

Module #15
Natural Language Processing (NLP) with Deep Learning
Text preprocessing, word embeddings, and language models for NLP tasks

Module #16
Transfer Learning and Fine-Tuning
Using pre-trained models and fine-tuning for custom datasets

Module #17
Model Interpretability and Explainability
LIME, SHAP, and TreeExplainer for model explainability

Module #18
AI and Machine Learning in Data Analysis
Real-world applications of AI and Machine Learning in data analysis

Module #19
Case Study:Predictive Modeling
Implementing AI and Machine Learning algorithms for predictive modeling

Module #20
Case Study:Natural Language Processing
Applying AI and Machine Learning to NLP tasks

Module #21
Case Study:Computer Vision
Using AI and Machine Learning for computer vision tasks

Module #22
Ethics in AI and Machine Learning
Bias, fairness, and transparency in AI and Machine Learning systems

Module #23
Deploying Machine Learning Models
Model deployment, API integration, and containerization

Module #24
Advanced Topics in AI and Machine Learning
GANs, Reinforcement Learning, and Transfer Learning

Module #25
Course Wrap-Up & Conclusion
Planning next steps in AI and Machine Learning for Data Analysis 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

AI and Machine Learning for Data Analysis ( 25 Modules )

AI and Machine Learning for Data Analysis
( 25 Modules )