This course covers a wide range of topics in data science projects, including deep learning in medical science, creating a movie recommender system, stock prediction using LSTM recurrent neural networks, and more. The course aims to teach skills such as implementing various machine learning algorithms, deploying deep learning models, and performing data analysis using Python libraries like sklearn and Keras. The teaching method includes tutorials, hands-on projects, and practical implementations. This course is intended for individuals interested in advancing their skills in data science projects, machine learning, and deep learning.
Overview
Syllabus
Deep Learning in Medical Science.
Complete Life Cycle of a Data Science Project.
Movie Recommender System using Python.
Stock Prediction using LSTM Recurrent Neural Network.
Artificial Neural Network for Customer's Exit Prediction from Bank.
OpenPose Tutorial with Tensorflow.
Create custom Alexa Skill- Intent Interface- Part1.
Create custom Alexa Skill- Lambda function- Part2.
Principle Component Analysis (PCA) using sklearn and python.
PySpark Tutorial for Beginners | Apache Spark with Python -Linear Regression Algorithm.
Creating a Dataset and training an Artificial Neural Network with Keras.
Gender Classifier and Age Estimator using Resnet Convolution Neural Network.
Unlock Your Application With Your Face using OpenCV.
Setting up Raspberry pi 3 B+.
Linear Regression Mathematical Intuition.
DBSCAN Clustering Easily Explained with Implementation.
Deployment of Deep Learning Model using Flask.
How to Visualize Multiple Linear Regression in python.
Predicting Heart Disease using Machine Learning.
Predicting Lungs Disease using Deep Learning.
Stock Sentiment Analysis using News Headlines.
Credit Card Fraud Detection using Machine Learning from Kaggle.
Hyperparameter Optimization for Xgboost.
Credit card Risk Assessment using Machine Learning.
Diabetes Prediction using Machine Learning from Kaggle.
Malaria Disease Detection using Deep Learning.
Taught by
Krish Naik
