IIRF Online > Development > Data Science > Machine Learning > Applied Machine Learning with Python by PwC Academy
Applied Machine Learning with Python by PwC Academy by Edureka
Skills you will learn!
Curriculum
9 Topics
Introduction to Python for Data Handling (Pandas NumPy)
Data Cleaning and Preprocessing Techniques
Handling Missing and Duplicated Data
Data Types and Type Conversion
Exploratory Data Analysis (EDA)
Aggregation and Grouping Operations
Merging and Joining Datasets
Working with Date and Time Data
Introduction to Data Science Concepts
9 Topics
Overview of Supervised Learning (Classification)
Algorithms: Logistic Regression Decision Trees Random Forests
Feature Engineering Techniques
Handling Missing Data and Encoding Categorical Variables
Scaling and Normalization of Features
Dimensionality Reduction Techniques (PCA t-SNE)
Training and Evaluating Supervised Models
Cross-validation and Hyperparameter Tuning
Model Interpretation and Feature Importance
9 Topics
Introduction to Unsupervised Learning
Overview of Clustering Techniques (K-Means Hierarchical DBSCAN)
Data Preprocessing for Clustering (Scaling Normalization)
Clustering Algorithms: Theory and Application
Visualizing Clusters (PCA t-SNE)
Evaluating Clustering Results (Silhouette Score Elbow Method)
Handling Outliers and Anomalies in Clustering
Business Applications of Customer Segmentation
Interpreting and Presenting Clustering Insights
9 Topics
Introduction to Reinforcement Learning (RL) Concepts
Overview of RL Algorithms
Defining the RL Environment
Inventory Management Challenges and Strategies
Stock Replenishment and Demand Forecasting
Formulating the RL Problem for Inventory Management
Training and Tuning RL Models for Optimization
Evaluating RL Model Performance
Comparing RL Models with Traditional Inventory Methods
Applied Machine Learning with Python by PwC Academy
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