najvajam

4-Convolutional-Neural-Network

Convolution_model_Step_by_Step

Applied-Machine-Learning-in-Python

This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis.

Convolution-Neural-Network

week1: Convolution_model_Step_by_Step

github-move

github-upload

Improving-Deep-Neural-Networks-Hyperparameter-tuning-Regularization-and-Optimization

najvajam

Config files for my GitHub profile.

Neural-Networks-and-Deep-Learning

Python-Basics-Python-3-Programming-Specialization-

Python 3 Programming Specialization assignments

Sequence-Models

najvajam/Applied-Machine-Learning-in-Python

This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis.

najvajam/Convolution-Neural-Network

week1: Convolution_model_Step_by_Step

najvajam/Python-Basics-Python-3-Programming-Specialization-

Python 3 Programming Specialization assignments

najvajam/4-Convolutional-Neural-Network

Convolution_model_Step_by_Step

najvajam/github-move

najvajam/github-upload

najvajam/Improving-Deep-Neural-Networks-Hyperparameter-tuning-Regularization-and-Optimization

najvajam/najvajam

Config files for my GitHub profile.

najvajam/Neural-Networks-and-Deep-Learning

najvajam/Sequence-Models

najvajam/simple-applications

najvajam/Simple-Face-Detection

Simple Face Detection

najvajam/Test

najvajam/Website

HTML