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Course Overview
This Machine Learning course provides a structured and comprehensive journey through foundational concepts and advanced techniques. Spanning a broad array of topics, including statistical foundations, algorithms, model evaluation, and deployment, this course is designed to equip students with practical skills and theoretical understanding necessary to build and deploy machine learning models. Students will engage in coding exercises, quizzes, and real-world case studies, making it an ideal course for aspiring data scientists, analysts, and AI practitioners.
Modules
Linear Regression
Feature Engineering
Logistic Regression
K Nearest Neighbour & Support Vector Machine
Decision Trees
Random Forest
Boosting and Naive Bayes Classification
K-Means & Hierarchical Clustering
ML Model Deployment and Operations
Natural Language Processing
Neural Network & Deep Learning
Trends in Machine Learning and AI
ML Basics and Maths Overview
Topics:
- Introduction to Machine Learning, Types of Machine Learning, Linear Algebra, Data Frames, Matrix Algebra, Probability and Statistics
Reference Material:
- Introduction to Statistical Learning
Activities:
- Common Interview Questions with Answers, Quiz
Linear Regression
Topics:
- Introduction to Linear Regression, OLS, Cost Function, Gradient Descent, Polynomial Regression, Regularization (Ridge & Lasso Regression), Cross-Validation
Activities:
- Implementation and Coding, Common Interview Questions, Quiz
Feature Engineering
Topics:
- Feature Engineering and Data Preparation, Outliers, Upsampling & Downsampling, PCA, Factor Analysis
Activities:
- Implementation and Coding, Quiz
Logistic Regression
Topics:
- Introduction to Logistic Regression, Maximum Likelihood, Model Training, Classification Matrix (Precision, Recall, Accuracy, F1 Score, ROC Curve)
Activities:
- Hands-on Exercise and Coding, Quiz
K Nearest Neighbour & Support Vector Machine
Topics:
- Introduction to KNN, SVM Concepts
Activities:
- Hands-on Exercise and Coding, Quiz
Decision Trees
Topics:
- Decision Trees, Model Creation, Gini Index for Model Evaluation
Activities:
- Hands-on Exercise and Coding, Quiz
Random Forest
Topics:
- Random Forest Introduction, Bootstrapping, Out-of-Bag Error, Advanced Model Building
Activities:
- Hands-on Exercise and Coding, Quiz
Boosting and Naive Bayes Classification
Topics:
- Introduction to Boosting, Gradient Boosting Theory, Naive Bayes Theorem & Modeling
Activities:
- Hands-on Exercise and Coding, Quiz
K-Means & Hierarchical Clustering
Topics:
- Introduction to K-Means Clustering, Hierarchical Clustering
Activities:
- Hands-on Exercise and Coding, Quiz
ML Model Deployment and Operations
Topics:
- Model Deployment, Online vs Offline Models, ML Model API, Testing
Activities:
- Quiz
Natural Language Processing
Topics:
- Introduction to NLP, Transformers, Encoder-Decoder Models
Activities:
- Take Home Quiz
Neural Network & Deep Learning
Topics:
- Introduction to ANN, Perceptron Models, Activation Function, Gradient Descent
Activities:
- Quiz
Trends in Machine Learning and AI
Topics:
- Generative AI Introduction, RAG Models, Prompt Structures
Activities:
- Case Study
Learning Outcomes
- Understand and differentiate between various types of machine learning algorithms and applications.
- Apply mathematical concepts, including linear algebra, probability, and statistics, to solve machine learning problems.
- Develop, train, and evaluate models using regression techniques such as Linear and Logistic Regression.
- Implement feature engineering and dimensionality reduction techniques to optimize datasets.
- Utilize various machine learning algorithms, such as KNN, SVM, Decision Trees, Random Forest, and Boosting methods, for different applications.
- Deploy machine learning models, understand API configurations, and assess model performance for operational use.
- Grasp fundamental concepts of NLP and Deep Learning, and apply them to real-world datasets.
- Explore current trends in AI, including Generative AI and advanced model prompts.
Fees & Enrollment
- Course Fee: INR 49,999
Career Services
- 100% Job Placement Support
- Interview Practice and Preparation
- 1 on 1 Career Mentoring Sessions
- Career Development Workshops
- Resume and LinkedIn Profile Enhancement
Machine Learning Course
-
Duration: 10 weeks
-
Course Format: Online Interactive Sessions with Virtual Lab Components
-
Weekly Schedule: 2-hour sessions, held twice on weekdays or weekends.
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Certification: Globally Recognized Machine Learning Certificate
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Basic understanding of Python programming is recommended. Some familiarity with statistics and algebra will be helpful, although the course will cover the essential concepts.
Yes, virtual practical coding exercises are included throughout the course, along with quizzes and take-home exercises for each module.
Yes, Introduction to Statistical Learning is a recommended text, particularly for foundational statistical concepts.
This course is designed to equip you with essential machine learning skills and practical experience, making you well-prepared for roles in data science, machine learning engineering, and AI.
Yes, students who complete the course and quizzes will receive a certification of completion.
Each lecture includes quizzes, and practical coding assignments are a core part of the curriculum, ensuring skill mastery through application.
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Explore diverse courses in AI, Machine Learning, Sustainability, and more.
Personalized Learning Paths
Tailor your educational journey to meet your specific goals and interests.
Flexible Learning Options
Study at your own pace with accessible online courses and tutorials.
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