Machine Learning wit Softlogic Systems Machine Learning With Python Course Syllabus is specifically designed for College Students, Freshers, and Job Seekers. Our Machine Learning with Python syllabus covers data preprocessing, supervised and unsupervised learning, model evaluation, feature engineering, and deployment techniques. Our Machine Learning with Python Course Content helps you learn Machine Learning with Python step by Step with real-time projects and Interview Preparations.
Machine Learning With Python Syllabus
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Syllabus for The Machine Learning With Python Syllabus Course
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Module 1: Introduction to Machine Learning
- Overview of Machine Learning and AI
- Applications and Use Cases in Industry
- Types of Machine Learning: Supervised, Unsupervised, Reinforcement
- Python for Machine Learning: Environment Setup and Tools
- Introduction to Jupyter Notebook and Anaconda
Module 2: Python Programming for ML
- Python Basics: Variables, Data Types, Operators
- Control Flow Statements, Loops, and Functions
- Working with Libraries: NumPy, Pandas, Matplotlib, Seaborn
- File Handling and Data Manipulation
- Exploratory Data Analysis (EDA)
Module 3: Data Preprocessing and Cleaning
- Handling Missing Data and Outliers
- Data Scaling and Normalization
- Encoding Categorical Variables (Label, One-Hot)
- Feature Selection and Extraction
- Data Splitting: Training, Validation, and Test Sets
Module 4: Supervised Learning Algorithms
- Linear Regression and Multiple Regression
- Logistic Regression
- Decision Trees and Random Forest
- Support Vector Machines (SVM)
- K-Nearest Neighbors (KNN)
- Model Evaluation Metrics (Accuracy, Precision, Recall, F1-Score)
Module 5: Unsupervised Learning Algorithms
- K-Means Clustering
- Hierarchical Clustering
- Principal Component Analysis (PCA)
- Dimensionality Reduction Techniques
- Association Rule Mining (Apriori, FP-Growth)
Module 6: Model Tuning and Optimization
- Cross-Validation Techniques
- Bias-Variance Tradeoff
- Hyperparameter Tuning using Grid Search & Random Search
- Pipeline Creation for ML Workflows
Module 7: Advanced Topics in Machine Learning
- Introduction to Ensemble Methods: Bagging, Boosting
- Introduction to Neural Networks
- Time Series Forecasting Basics
- Introduction to Deep Learning Frameworks (TensorFlow, Keras)
Module 8: Model Deployment & Real-Time Applications
- Model Serialization: Pickle and Joblib
- Flask for API Development
- Deploying ML Models with Streamlit
- Cloud Deployment Overview (AWS, Azure)
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