Learn Machine Learning with Python
Our Machine Learning with Python course provides hands-on training in implementing ML algorithms using Python’s scientific stack. Using scikit-learn, TensorFlow, and other Python libraries, you’ll learn how to build, evaluate, and deploy machine learning models.
Skills You’ll Gain:
- Statistical analysis and probability
- Data preprocessing with Pandas
- Supervised and unsupervised learning
- Model optimization techniques
- Ensemble learning methods
- Recommendation systems
- Practical case studies
Module 1 - Statistical Learning
- Descriptive statistics
- Probability and Bayes theorem
- Hypothesis testing
- Probability distributions
Module 2 - Python for Machine Learning
- Pandas for EDA
- NumPy for analysis
- Matplotlib & Seaborn
- Scikit-Learn
Module 3 - Introduction to Machine Learning
- ML Modelling Flow
- Bias-Variance Trade-Off
- Overfitting & Underfitting
- Performance Measures
Module 4 - Optimization
- Cost Function
- Learning Rate
- Optimization Techniques
Module 5 - Supervised Learning
- Linear/Logistic Regression
- KNN Classification
- SVM
- Case Studies
Module 6 - UnSupervised Learning
- K-Means Clustering
- Hierarchical Clustering
- Case Study
Module 7 - Ensemble Techniques
- Decision Trees
- Bagging/Boosting
- Random Forests
- PCA Applications
Module 8 - Recommendation System
- Collaborative Filtering
- Content-based Filtering
- Hybrid Systems
- Performance Measurement
Earn a recognized credential that validates your technical expertise and opens doors to new career opportunities.
Why Learn Machine Learning with Python?
- Python’s simplicity for ML algorithms
- Rich ecosystem of ML libraries
- Good for both research and production
- Strong visualization capabilities
- Integration with data processing tools
- Active community support
- Cross-platform compatibility
- Good documentation and resources
- Continuous framework improvements
- Industry adoption
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