Python with Machine Learning Course

Python with Machine Learning Course Outline

Duration: 40–50 Hours
Level: Beginner to Advanced
Delivery Mode: Online / Offline
Target Audience: Aspiring Data Scientists, Machine Learning Engineers, Software Developers, IT Professionals
Prerequisites: Basic programming knowledge is recommended but not mandatory

Module 1: Introduction to Python for Machine Learning

• Introduction to Python and its Application in Machine Learning
• Python Setup and IDEs (Jupyter Notebooks, VS Code, PyCharm)
• Python Data Structures: Lists, Tuples, Dictionaries, and Sets
• Functions and Lambda Functions in Python
• Numpy and Pandas for Data Handling
• Control Flow (if-else, loops), Functions, and Object-Oriented Programming

Module 2: Data Preprocessing and Feature Engineering

• Understanding the Importance of Data Preprocessing
• Data Cleaning: Handling Missing Values, Duplicates, Outliers
• Feature Scaling: Normalization and Standardization
• Encoding Categorical Data: One-Hot Encoding, Label Encoding
• Feature Selection and Dimensionality Reduction
• Handling Imbalanced Data: Techniques like SMOTE

Module 3: Data Visualization and Analysis

• Introduction to Data Visualization Libraries: Matplotlib, Seaborn, Plotly
• Creating Basic Plots: Line Plots, Bar Charts, Scatter Plots
• Visualizing Distributions and Relationships between Variables
• Correlation Analysis and Heatmaps
• Advanced Visualizations: Pair Plots, Violin Plots, and Box Plots
• Customizing Plots: Titles, Legends, and Labels

Module 4: Introduction to Machine Learning Concepts

• Overview of Machine Learning: Supervised vs Unsupervised Learning
• Understanding the Machine Learning Lifecycle
• Types of Machine Learning Algorithms
• Evaluation Metrics for Machine Learning Models: Accuracy, Precision, Recall, F1 Score
• Cross-Validation and Model Selection

Module 5: Supervised Learning Algorithms

• Linear Regression: Simple and Multiple Linear Regression
• Logistic Regression: Understanding Logistic Regression for Classification
• Decision Trees: Concept and Building Decision Trees
• K-Nearest Neighbors (KNN): Working with KNN for Classification and Regression
• Support Vector Machines (SVM): Building and Tuning SVM Models
• Random Forest: Building and Tuning Random Forests for Classification and Regression
• Hyperparameter Tuning and Model Optimization

Module 6: Unsupervised Learning Algorithms

• Introduction to Unsupervised Learning
• K-Means Clustering: Working with K-Means and Elbow Method
• Hierarchical Clustering: Agglomerative and Divisive Clustering
• Principal Component Analysis (PCA): Dimensionality Reduction and Visualization
• DBSCAN: Density-Based Spatial Clustering of Applications with Noise
• Anomaly Detection: Techniques for Identifying Anomalies in Data

Module 7: Model Evaluation and Improvement

• Model Evaluation Metrics: Precision, Recall, ROC-AUC, and Confusion Matrix
• Model Validation Techniques: K-Fold Cross-Validation, Stratified Sampling
• Tuning Models using GridSearchCV and RandomizedSearchCV
• Bias-Variance Tradeoff and Overfitting vs Underfitting
• Regularization Techniques: Lasso, Ridge, and ElasticNet

Module 8: Ensemble Learning Techniques

• Introduction to Ensemble Learning
• Bagging: Random Forest
• Boosting: AdaBoost, Gradient Boosting, and XGBoost
• Stacking: Combining Multiple Models for Better Accuracy
• Model Blending and Voting Classifiers
• Evaluating Ensemble Models

Module 9: Neural Networks and Deep Learning (Optional)

• Introduction to Neural Networks and Perceptrons
• Building a Simple Neural Network with Keras and TensorFlow
• Understanding the Backpropagation Algorithm
• Introduction to Deep Learning Architectures
• Building a Convolutional Neural Network (CNN) for Image Classification
• Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) Networks

Module 10: Working with Real-World Data and Projects

• End-to-End Machine Learning Project: From Data Collection to Model Deployment
• Real-World Case Studies: Applications in Finance, Healthcare, and E-commerce
• Working with Datasets: Kaggle Competitions and Challenges
• Model Deployment: Introduction to Flask for Deploying Models
• Monitoring and Maintaining Models in Production

Module 11: Capstone Project and Industry Use Cases

• Design, Develop, and Deploy a Machine Learning Solution
• Presenting Results, Insights, and Business Applications
• Preparing for Machine Learning Job Roles and Interviews
• Overview of Career Paths: Data Scientist, Machine Learning Engineer, AI Specialist