Machine Learning with Python at Softlogic, the premier institute providing a robust Machine Learning with Python Syllabus for aspiring data professionals. Our syllabus is designed to build solid machine learning fundamentals with the strength of Python. Study important topics like supervised and unsupervised learning, data preprocessing, model evaluation, feature engineering, regression, classification, clustering, and real-time model deployment.
Get hands-on experience with actual data sets, end-to-end machine learning projects, and hands-on coding exercises. Softlogic provides expert instruction and one-on-one career coaching to succeed in AI and data science professions. Download our Machine Learning with Python Syllabus PDF to view the complete course structure and topics. Enroll in our Machine Learning with Python Course with 100% Placement Support and take your first step toward a successful career in data science. Begin your journey with Softlogic today!
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
1
- 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
2
- 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
3
- 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
4
- 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
5
- K-Means Clustering
- Hierarchical Clustering
- Principal Component Analysis (PCA)
- Dimensionality Reduction Techniques
- Association Rule Mining (Apriori, FP-Growth)
Module 6: Model Tuning and Optimization
6
- 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
7
- 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
8
- Model Serialization: Pickle and Joblib
- Flask for API Development
- Deploying ML Models with Streamlit
- Cloud Deployment Overview (AWS, Azure)
Breakdown of Machine Learning With Python Syllabus Course Fee and Batches
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June 2025
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