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    Machine Learning Fundamentals in Depth

    BY
    Skill Lync

    Brochure Enquire
    Lavel

    Beginner

    Mode

    Online

    Duration

    12 Weeks

    Fees

    ₹ 40000

    Quick Facts

    particular details
    Medium of instructions English
    Mode of learning Self study
    Mode of Delivery Video and Text Based

    Course and certificate fees

    Fees information
    ₹ 40,000
    certificate availability

    Yes

    certificate providing authority

    Skill Lync

    The syllabus

    Week 01 - Basics of Probability and Statistics

    • Basics of Probability
    • Basics of Statistics
    • What is ML & AI

    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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