Courses
Difficulty Level: BEGINNER
Age group: 15-39
Machine Learning - Introduction to Machine Learning - Supervised Learning - Regression - Classification - Decision Trees and Random Forest - Unsupervised Learning - Time Series Modeling - Ensemble Learning - Recommender Systems Introduction to Machine Learning - Explanation of machine Learning - Relationship between Artificial Intelligence, Machine Learning, and Data Science - Definition and Features of Machine Learning - Machine Learning Approaches Supervised Learning - Supervised Learning - Supervised Learning: Real Life Scenario - Understanding the Algorithm - Supervised Learning Flow - Types of Supervised Learning - Types of Classification Algorithms Regression - Types of Regression Algorithms - Regression Use Case - Accuracy Metrics - Cost Function - Evaluating Coefficients - Demo: Linear Regression - Challenges in Prediction - Types of Regression Algorithms: Part II - Example as Bigmart - Logistic Regression - Sigmoid Probability - Accuracy Matrix - Demo: Survival of Titanic Passengers Classification - Overview of Classification - Classification: A Supervised Learning Algorithm - Use Cases - Classification Algorithms - Performance Measures: Confusion Matrix - Performance Measures: Cost Matrix - Naive Bayes Classifier - Steps to Calculate Posterior Probability - Support Vector Machines: Linear Separability - Support Vector Machines: Classification Margin - Linear SVM: Mathematical Representation - Non linear SVMs - The Kernel Trick Decision Trees and Random Forest - Decision Tree: Classifier - Decision Tree: Examples - Decision Tree: Formation - Choosing the Classifier - Overfitting of Decision Trees - Random Forest Classifier Bagging and Bootstrapping - Example of Linear Regression Unsupervised Learning - Overview of Unsupervised Learning - Example and Applications of Unsupervised Learning - Clustering - Hierarchical Clustering - Hierarchical Clustering: Example - K-means Clustering - Optimal Number of Clusters - Examples of Cluster Based Incentivization Time Series Modeling - Overview of Time Series Modeling - Time Series Pattern Types - White Noise - Stationarity - Removal of Non Stationarity - Demo: Air Passengers I - Time Series Models - Steps in Time Series Forecasting - Demo: Air Passengers II Ensemble Learning - Overview of Ensemble Learning - Ensemble Learning Methods: Part I and Part II - Working of AdaBoost - AdaBoost Algorithm and Flowchart - Gradient Boosting - XGBoost - XGBoost Parameters: Part I and Part II - Demo: Pima Indians Diabetes - Model Selection - Common Splitting Strategies - Demo: Cross Validation Recommender Systems - Introduction to Recommender Systems - Purposes of Recommender Systems - Paradigms of Recommender Systems - Collaborative Filtering: Part I and II - Association Rule: Generation Apriori Algorithm - Apriori Algorithm Example: Part I and II - Apriori Algorithm: Rule Selection - Demo: User Movie Recommendation Model - Association Rule: Mining Market Basket Analysis - Association Rule: Mining
knowledge of Mathematics and programming language will be helpful
Basic knowledge of Statistics and modeling
Conceptual knowledge of Data Science
Explore real-world applications of machine learning in various fields, and build a foundation for more advanced topics like deep learning and neural networks.
Develop the ability to split data into training and testing sets, and understand the importance of model evaluation metrics like accuracy, precision, and recall.
Learn to preprocess data, handle missing values, and perform feature scaling to prepare datasets for machine learning tasks.
Gain hands-on experience with basic machine learning tools and libraries, such as Python's Scikit-Learn, to implement and evaluate simple models.
Understand the fundamental concepts of machine learning, including supervised and unsupervised learning, and key algorithms like regression and clustering.
Understanding of the concept of Machine learning
Proficiency in Regression algorithm and Metrics
Ability to work on Supervised Learning with Real Life Scenario
Skill in Time Series Modelling, Ensemble Learning and Recommender Systems
Mastery in types of Machine Learning
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