Machine Learning Crash Course
Learn your machine by implementing your industrial knowledge within a short time.
Course curriculum
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Understanding data scenario
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Insight generation from the scenario
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Pre-work towards model development
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Python Intermediate (till function)
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Approaching a machine learning Problem
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Object Oriented Approach (showing by solving a machine)
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EDA(exploratory data analysis) with pandas
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Numerical analysis with numpy
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Advanced EDA (functional and oop approach) using pandas
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Numpy Implementation (Basic)
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Numpy Implementation (Advanced Numeric Computation with Scipy operations)
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Feature Engineering
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Model Implementation
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How to choose best features
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Handling Categorical Data for modeling
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How to efficiently create your model and validate it (Scikit-learn, metrics)
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How to use git and github for project management efficiently
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How to clean your data (advanced data cleaning methods)
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Statistical Knowledge to understand your data (Distribution, Correlation)
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Hyperparameter tuning for machine learning models
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Regression Algorithm
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Classification Algorithm
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Overview of neural network
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Regression Method (Advanced and Efficient Implementation)
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Binary, Multiclass classification algorithms
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Multilevel and Imbalanced classification algorithms
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Logistic Regression model using neural network approach
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Deployment methods
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Project deployment using django and flask API
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How to deploy your model (Django and Flask)
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Using API to deploy your model (Fast API)
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Regression - Loss and Regularization
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Basics of tensorflow and keras
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Implementing tensorflow for machine learning model
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How to optimize your linked in profile
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How to optimize your github profile
