Machine Learning and Neural Networks
Machine learning is changing the world rapidly. Applications like self-driving cars are possible because of technologies like image processing applied with machine learning. Get to know how machine learning does this with neural networks with this course on Machine learning and Neural networks.
• Learn to use Data Science libraries like NumPy, Pandas, Scikit-learn
• learn how to use matplotlib, Seaborn, Plotly, python libraries to plot the graphs and visualize the data.
• Deep Learning framework TensorFlow Keras.
• Linear regression.
• Hands On: Building Linear Regression model to predict the house price based on its features like area, number of bedrooms etc.
• Project 1: Predict the selling price of the car from features of car like transmission and fuel type, age of the car etc.
• Neural Networks
• create neural network with TensorFlow Keras and train using the same.
• Hands On: Create a Neural Network capable of recognizing digit in a given image
• Project 2: Create a Neural Network Capable of recognizing clothing like T-Shirt, Sandal, Bag etc.
• Learn to build a datasets from google Images
• Hands On: Build a classification model using Neural Networks for dog V/S cat
Prerequisites: Familiarity with programming
|Setting Up the Environment|
|Setup for Machine Learning : Part-I||00:08:00|
|Setup for Machine Learning : Part-II||00:10:00|
|Setup Locally (Reading)||00:05:00|
|Brushing-up Python (Part-I)||00:21:00|
|Brushing-up Python (Part-II)||00:19:00|
|Python for Machine Learning|
|Getting Started with Machine Learning|
|Overview of Machine Learning Section||00:03:00|
|What is ML and AI | where is it used?||00:00:00|
|Applications of Machine Learning and Artificial Intelligence (Reading)||00:05:00|
|Defining Machine Learning||00:06:00|
|Defining Machine learning (Reading)||00:10:00|
|Classification of Machine Learning||00:11:00|
|Reading : Classification of machine learning||00:05:00|
|Machine Learning Model: Hypothesis Function||00:09:00|
|Hypothesis Function (Reading)||00:05:00|
|Cost Function (Reading)||00:10:00|
|Debugging gradient descent||00:04:00|
|Mean Normalization (Reading)||00:05:00|
|Training a model||00:02:00|
|Training a model (Reading)||00:05:00|
|Debugging gradient descent (Reading)||00:15:00|
|Hands on: Linear Regression for housing price prediction||00:20:00|
|Hands on: Linear Regression with Multiple Variables||00:25:00|
|Linear Regression Assignment||00:10:00|
|Artificial Neural Networks with TensorFlow|
|Artificial Neural Networks||00:17:00|
|Artificial Neural Networks (Reading)||00:00:00|
|Activation Function (Reading)||00:05:00|
|What are deep learning frameworks||00:07:00|
|Representation of Images||00:06:00|
|Representation of images (Reading)||00:05:00|
|Techniques used for training||00:17:00|
|Techniques for training the data (Reading)||00:20:00|
|Hands-on : Building Digit Recognizer with keras||00:15:00|
|Predicting digit present in your image||00:06:00|
|Creating Datasets with Google Images & Building Classificaion Model|
|Building Cat vs Dog classification model||00:16:00|
|Deep learning and computer vision||00:13:00|
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