Course Includes:
- Mode: Offline\Online
- Language: English\Hindi
- Certificate: Yes
- Project Certificate: Yes
Course Description
Machine learning is a subfield of artificial intelligence (AI) that focuses on the development of algorithms and statistical models that enable computer systems to improve their performance on a specific task through learning and experience. In other words, machine learning allows computers to learn from data and make data-driven predictions or decisions. Machine learning is a dynamic and rapidly evolving field with vast potential for innovation and impact across various industries and domains. It continues to be a transformative force in the modern world, with an ever-expanding range of applications and advancements.
This machine learning course is designed to provide a comprehensive introduction to the principles, techniques, and applications of machine learning. It covers the fundamental concepts, various types of machine learning algorithms, and practical implementation. The course focuses on hands-on experience, allowing students to work on real-world machine learning projects.
This machine learning course provides students with a solid understanding of machine learning concepts, practical skills, and the ability to apply machine learning in various domains. It serves as a stepping stone for further exploration of advanced machine learning topics and specialization in specific areas of interest.
Week 1: Introduction to Machine Learning
- What is Machine Learning?
- Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
- Machine Learning Lifecycle
- Python and Libraries for Machine Learning
Week 2: Data Pre-processing and Exploration
- Data Cleaning and Transformation
- Data Visualization
- Handling Missing Data
- Feature Selection and Engineering
Week 3: Supervised Learning
- Linear Regression
- Logistic Regression
- Decision Trees and Random Forest
- K-Nearest Neighbours
- Model Evaluation Metrics
Week 4: Unsupervised Learning
- Clustering (K-Means, Hierarchical)
- Dimensionality Reduction
Week 5: Unsupervised Learning
- Clustering (K-Means, Hierarchical)
- Dimensionality Reduction (PCA, t-SNE)
- Anomaly Detection
- Recommendation Systems
Week 6: Final Projects
- Students work on machine learning projects of their choice
- Project presentations
Recap of the course
Machine learning is a subset of artificial intelligence that enables computers to learn from data. Learning it is valuable for understanding data-driven decision-making.
While programming experience is helpful, many courses offer beginner-friendly content, and some don't require programming prerequisites.
A strong foundation in mathematics (especially linear algebra and calculus) and basic programming knowledge are helpful.
Python is the most popular language for machine learning due to its extensive libraries and frameworks.
The future holds advancements in deep learning, explainable AI, edge computing, quantum machine learning, and ethical AI.
Yes, machine learning is used in big data analytics and cloud-based services, and knowledge of these areas is valuable.
Build a strong portfolio, seek internships, and stay updated on the latest developments in the field.
Roles like machine learning engineer, data scientist, AI researcher, and data analyst are in high demand in various industries.
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