Machine Learning Fundamentals In Depth
- Offered bySkill Lync
Machine Learning Fundamentals In Depth at Skill Lync Overview
Duration | 12 weeks |
Total fee | ₹7,000 |
Mode of learning | Online |
Credential | Certificate |
Machine Learning Fundamentals In Depth at Skill Lync Highlights
- The duration of the machine learning and artificial intelligence course is 12 weeks.
- Besides the course completion certificate for all participants, the top 5% of learners get a merit certificate.
- Student will get Individual Video Support, Group Video Support, Email Support, and Forum Support to clear your queries and doubts.
- Real-time industry-relevant projects will make your learning purposeful.
Machine Learning Fundamentals In Depth at Skill Lync Course details
- Engineering graduates and working professionals with a background in similar fields can take this course.
- Basic probability and statistics essential for understanding ML and AI
- Supervised learning (Prediction, classification)
- Random Forest and Model Evaluation
- Unsupervised Learning (K Means, Hierarchical, PCA)
- This course will offer aspiring engineers a solid understanding of the fundamental machine learning algorithms
- The main objective of this Machine Learning Fundamentals In Depth course is to teach student from basics to advanced concepts.
- Expertise in Machine Learning and Artificial Intelligence will help student secure lucrative career opportunities.
- Knowledge of Machine Learning and Artificial Intelligence will help student predict customer behavior patterns.
- This AI & ML course will cover both supervised and unsupervised learning in Machine Learning
Machine Learning Fundamentals In Depth at Skill Lync Curriculum
Week 1 - Basics of Probability and Statistics
Basics of Probability
Basics of Statistics
What ML & AI is
Week 2 - Basics of Machine Learning (ML) & Artificial intelligence (AI)
Normal Distribution & Standard Normal Distribution: Introduction
Business Moments: Introduction
Artificial Intelligence
Week 3 - Supervised Learning - Prediction
Supervised learning: Introduction
What linear regression is
One hot encoding
Cost function and gradient descent
Week 4 - Supervised Learning - Classification
Classification problems: Introduction
What logistic regression is
Cost function and gradient descent
Week 5 - Supervised Learning - Classification
Decision tree
Entropy
Information gain
Week 6 - Random Forest & Model Evaluation
Random forest
Bootstrapping and majority rule
Evaluation of classifiers
Week 7- Supervised Learning - Classification
Support Vector Machines (SVM)
Mathematical intuition behind SVM
How SVM is different from other classifiers
Week 8 - Supervised Learning - Classification
K-Nearest Neighbor
Lazy Algorithm
Single-layer Neural Network
Week 9 - Unsupervised Learning - K-Means
What clustering is
Why clustering is important
K Means and elbow curve
Week 10 - Unsupervised Learning - Hierarchical
Hierarchical Clustering
Dendrogram
Evaluation of clustering algorithms
Week 11 - Unsupervised Learning - PCA
Feature Selection
Principal Component Analysis (PCA)
Mathematical intuition behind PCA
Week 12 - Supervised Learning - Classification
Artificial Neural Networks
Deep learning
Different activation functions
Understanding back propagation
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