- Basics of Probability
- Basics of Statistics
- What is ML & AI
Quick Facts
particular | details | |||
---|---|---|---|---|
Medium of instructions
English
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Mode of learning
Self study
|
Mode of Delivery
Video and Text Based
|
Course and certificate fees
Fees information
₹ 40,000
The fees for the course Machine Learning Fundamentals in Depth is -
Head | Amount |
Programme fees | Rs. 40,000 |
certificate availability
Yes
certificate providing authority
Skill Lync
The syllabus
Week 01 - Basics of Probability and Statistics
Week 02 - Basics of Machine Learning (ML) & Artificial intelligence(AI)
- Introduction to Normal Distribution & Standard Normal Distribution An introduction
- to business moments
- Artificial Intelligence
Week 03 - Supervised Learning - Prediction
- Introduction to supervised learning
- What is linear regression?
- One hot encoding
- Cost function and gradient descent
Week 04 - Supervised Learning - Classification
- Introduction to classification problems
- What is logistic regression?
- Cost function and gradient descent
Week 05 - Supervised Learning - Classification
- Decision tree
- Entropy
- Information gain
Week 06 - Random forest & Model Evaluation
- Random Forest
- Bootstrapping and majority rule
- Evaluation of classifiers
Week 07- Supervised Learning - Classification
- Support Vector Machines
- Mathematical intuition behind SVM
- How SVM is different from other classifiers
Week 08 - Supervised Learning - Classification
- K-Nearest Neighbour
- Lazy Algorithm
- Single-layer Neural Network
Week 09 - Unsupervised Learning - Kmeans
- What is clustering
- Why clustering is important
- Kmeans 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
- Mathematical intuition behind PCA
Week 12 - Supervised Learning - Classification
- An Artificial Neural Network
- Deep learning
- Different activation functions
- Understanding back propagation
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