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Machine Learning

₹8,999.00

  • 6-Week Intensive Curriculum
  • Accredited by Wipro
  • Internship Opportunities with 100+ partner companies
  • One-on-One Mentorship with industry leaders
  • Letter of Recommendation 
  • Guaranteed Internship Placement
  • Detailed Project Reports for your capstone projects
Category:

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.

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