Overview: This comprehensive course is designed for individuals with no prior coding experience, providing a complete learning journey from Python fundamentals to machine learning with Python. Over six months, participants will acquire proficiency in Python programming, learn the basics of data science, and gain hands-on experience in applying machine learning algorithms using Python libraries such as scikit-learn. From data preprocessing and exploratory data analysis to supervised and unsupervised learning techniques, students will develop the skills necessary to build and evaluate machine learning models for real-world applications.

Syllabus:

Month 1: Python Fundamentals

Week 1:

• Introduction to Python programming
• Variables, data types, and basic operations
• Control structures: loops and conditionals

Week 2:

• Functions and modules
• File handling and input/output operations
• Error handling and exceptions

Week 3:

• Introduction to Python libraries for data science (NumPy, Pandas, Matplotlib)
• Working with arrays and matrices using NumPy
• Data manipulation and analysis with Pandas

Week 4:

• Data visualization with Matplotlib
• Plotting basic charts and graphs
• Customizing visualizations and adding labels

Month 2: Basics of Data Science

Week 5:

• Introduction to data preprocessing
• Handling missing values and outliers
• Data cleaning and transformation techniques

Week 6:

• Data normalization and scaling
• Feature encoding and categorical data handling
• Introduction to feature selection and dimensionality reduction

Week 7:

• Exploratory data analysis (EDA)
• Descriptive statistics and data summarization
• Visualizing relationships and distributions in data

Month 3: Supervised Learning Algorithms

Week 8:

• Introduction to supervised learning
• Linear regression and logistic regression
• Implementation and interpretation of regression models

Week 9:

• Decision trees and random forests
• Ensemble methods for classification and regression
• Hyperparameter tuning and model optimization

Week 10:

• Support vector machines (SVM)
• Understanding the kernel trick
• Non-linear classification and regression

Month 4: Unsupervised Learning Algorithms

Week 11:

• Introduction to unsupervised learning
• Clustering techniques: K-means and hierarchical clustering
• Evaluating cluster quality and cohesion

Week 12:

• Dimensionality reduction: Principal Component Analysis (PCA)
• Anomaly detection and outlier identification
• Recommender systems

Month 5: Advanced Topics in Machine Learning

Week 13:

• Evaluation metrics and model selection
• Cross-validation and bias-variance tradeoff
• Introduction to ensemble learning

Week 14:

• Neural networks and deep learning
• Building and training neural network models
• Deep learning frameworks (e.g., TensorFlow, Keras)

Month 6: Applied Machine Learning and Project

Week 15:

• Natural Language Processing (NLP)
• Text preprocessing and feature extraction
• Sentiment analysis and text classification

Week 16:

• Time series analysis and forecasting
• Feature engineering for time-dependent data
• ARIMA and other time series models

Week 17:

• Project: Applying machine learning to a real-world dataset
• End-to-end machine learning workflow: data preprocessing, model training, evaluation, and deployment

Week 18:

• Project presentation and wrap-up
• Recap of key concepts and further learning resources

Note: This syllabus provides a general outline for the Machine Learning with Python course, including Python fundamentals and basics of data science. The duration and content can be adjusted based on the pace of the learners and the depth of coverage desired. Additional topics such as advanced neural networks, reinforcement learning, or specific applications of machine learning can be included if time permits