Machine Learning

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    Machine Learning

    Machine Learning

    Requirements : Age 11+

    Duration : 32 hours ( 4 Months )

    What is this Course ?

    Have you ever wondered how Amazon, eBay suggest items for you to buy?
    How Gmail filters your emails in the spam and non-spam categories?
    How Netflix predicts the shows of your liking?
    How do they do it? These are the few questions we ponder from time to time.
    In reality, doing such tasks are impossible without the availability of data. Data science is all about using data to solve problems. The problem could be decision making such as identifying which email is spam and which is not.
    Or a product recommendation such as which movie to watch? Or predicting the outcome such as who will be the next President of the USA?
    So, the core job of a data scientist is to understand the data, extract useful information out of it and apply this in solving the problems.

    Why this Course ?

    Why ML is so good today; for this, there are a couple of reasons like below but not limited to though.
    1. The explosion of big data
    2. Hunger for new business and revenue streams in this business shrinking times
    3. Advancements in machine learning algorithms
    4. Development of extremely powerful machine with high capacity & faster computing ability
    5. Storage capacity

    Learning Outcome

    1. Students will develop relevant programming abilities.
    2. Students will demonstrate proficiency with statistical analysis of data.
    3. Students will develop the ability to build and assess data-based models.
    4. Students will execute statistical analyses with professional statistical software.
    5. Students will demonstrate skill in data management.
    6. Students will apply data science concepts and methods to solve problems in real-world contexts and will communicate these solutions effectively

    Skills Acquired

    1. Develop relevant programming abilities.
    2. Demonstrate proficiency with statistical analysis of data.
    3. Develop the ability to build and assess data-based models.
    4. Execute statistical analyses with professional statistical software.
    5. Demonstrate skill in data management.


    Supervised Learning
    o Real-life Scenario
    o Understanding the algorithm
    o Supervised Learning Flow
    o Types of Supervised Learning
    o Types of Classification algorithms
    o Types of Regression Algorithms
    o Accuracy Metrics
    o Cost Function
    o Evaluating Coeffecients
    o Logistic Regression
    o Sigmoid Probability
    o Accuracy Matrix
    o Use Case: Survival of Titanic Passengers
    · Feature Engineering
    o Feature Selection
    o Factor Analysis
    o Principal Component Analysis(PCA)
    o Feature Reduction
    · Supervised Learning Classification
    o Overview of classification
    o Classification Algorithms
    o Decision Tree Classifier
    o Decision Tree Examples
    o Random Forest Classifier
    o Performance Measures:Confusion Matrix
    o Performance Measures:Cost Matrix
    o Practice:Loan Risk Analysis
    o Naïve Bayes Classifier
    o Steps to Calculate Posterior Probability
    o Support Vector Machines:Linear Seperability
    o Support Vector Machines:Classification Margin
    o Linear SVM:Mathematical Representation
    o Non-linear SVM’s
    · Non-Supervised Learning
    o Example and Applications
    o Clustering
    o Hierarchical Clustering
    o Practice:Customer Segmentation
    o K-Means Clustering
    o Optimal Number of Clusters
    · Ensemble Learning
    o Overview
    o Ensemble Learning Methods
    o AdaBoost algorithm
    o Gradient Boosting
    o Model Selection
    o Cross Validation
    · Recommender Systems
    o Introduction
    o Purpose
    o Collaborative Filtering
    o Association Rule Mining
    o Association Rule Mining:Market Basket Analysis
    o Apriori Algorithm
    o Practice:Movie Recommendation
    · Reinforcement Learning
    o What is Reinforcement learning
    o Reinforcement learning vs Rest
    o Basic Concepts
    o How Reinforcement Works
    o Simple Implementation
    o Conclusion

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