Master the Art of Machine Learning to Develop Intelligent Systems (6-Week Intensive)
Course Duration: 6 Weeks
Mentorship: Industry experts from top tech companies will guide you throughout the course.
Mode of Learning: Online, Self-paced + Weekly Live Sessions
Week 1: Introduction to Machine Learning & Python
- Introduction to Machine Learning: Overview of ML concepts, types of ML (supervised, unsupervised, reinforcement learning), applications, and the ML workflow.
- Python for Machine Learning: Setting up the Python environment, data types, variables, control flow, functions, and introduction to key libraries: NumPy, Pandas, and Scikit-learn.
- Data Preprocessing: Data cleaning, handling missing values, feature scaling, and data transformation techniques.
- Project 1: Exploratory Data Analysis and Preprocessing – Perform EDA and preprocessing on a dataset relevant to a chosen ML task.
Outcome: Foundational knowledge of ML and Python, ability to prepare data for ML algorithms.
Week 2: Supervised Learning – Regression
- Linear Regression: Simple and multiple linear regression, model evaluation metrics (R-squared, MSE).
- Polynomial Regression: Introduction to polynomial regression and feature engineering for non-linear relationships.
- Regularization: Techniques for preventing overfitting (L1 and L2 regularization).
- Project 2: Building a Regression Model – Train and evaluate a regression model to predict a continuous variable.
Outcome: Understanding of regression algorithms and model evaluation, ability to build and evaluate regression models.
Week 3: Supervised Learning – Classification
- Logistic Regression: Introduction to logistic regression for binary classification.
- Support Vector Machines (SVMs): Understanding SVMs and their application to classification.
- Decision Trees: Building and interpreting decision tree classifiers.
- Project 3: Building a Classification Model – Train and evaluate a classification model to predict a categorical variable.
Outcome: Understanding of classification algorithms and model evaluation, ability to build and evaluate classification models.
Week 4: Model Selection, Evaluation, and Tuning
- Model Selection: Techniques for choosing the best model among different algorithms (cross-validation, hold-out sets).
- Hyperparameter Tuning: Optimizing model performance by tuning hyperparameters (Grid Search, Random Search, Bayesian Optimization).
- Performance Metrics: Advanced metrics for evaluating classification models (precision, recall, F1-score, ROC curves, AUC).
- Project 4: Model Optimization and Tuning – Optimize a chosen model using appropriate techniques and evaluate its performance using suitable metrics.
Outcome: Proficiency in model selection, evaluation, and tuning techniques.
Week 5: Unsupervised Learning & Dimensionality Reduction
- Clustering: Introduction to clustering algorithms (k-means, hierarchical clustering, DBSCAN).
- Dimensionality Reduction: Techniques for reducing the number of features (PCA, t-SNE).
- Association Rule Mining: Discovering relationships between variables (Apriori algorithm).
- Project 5: Applying Unsupervised Learning – Apply clustering or dimensionality reduction techniques to a dataset and interpret the results.
Outcome: Understanding of unsupervised learning concepts and algorithms.
Week 6: Final Project & Career Prep
- Project 6: Final Machine Learning Project – Develop a complete ML project, from data collection and preprocessing to model building, evaluation, and deployment (if possible). Students can choose a project based on their interests.
- Career Preparation:
- Portfolio Building: Refine and complete your portfolio with a professional presentation of your projects.
- Interview Prep & Resume Tips: Learn how to showcase your skills, work on technical interview questions, and perfect your resume.
- Industry Insights: Get advice on the latest industry trends, frameworks, and technologies.
Outcome: By the end of the course, you’ll have a strong portfolio and the confidence to apply for jobs in machine learning.
Major Projects:
- Exploratory Data Analysis and Preprocessing (Week 1) – Core Skills: Python, Pandas, data cleaning, feature engineering.
- Building a Regression Model (Week 2) – Core Skills: Regression algorithms, model training, evaluation.
- Building a Classification Model (Week 3) – Core Skills: Classification algorithms, model training, evaluation.
- Model Optimization and Tuning (Week 4) – Core Skills: Model selection, hyperparameter tuning, performance metrics.
- Applying Unsupervised Learning (Week 5) – Core Skills: Clustering, dimensionality reduction.
- Final Machine Learning Project (Week 6) – Core Skills: Full ML lifecycle, project planning, and execution.
Mentorship at Eduveda Academy:
- Industry Mentor Assignment: You’ll be paired with a mentor from a top tech company who will provide personalized guidance on projects, career advice, and best practices.
- 1-on-1 sessions for project reviews and troubleshooting.
- Live Q&A Sessions weekly with mentors and instructors.
Final Notes:
- Weekly Live Sessions: These will cover key topics, provide updates, and allow you to ask questions.
- Peer Networking: Join the academy Slack group for collaboration and feedback.
This 6-week course is intense but designed to give you all the key skills to jumpstart your career in Machine Learning. You’ll leave with solid project experience, a polished portfolio, and the confidence to apply for jobs in the field.
