Machine Learning and Neural Networks

5 STUDENTS ENROLLED

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.

HIGHLIGHTS:
• 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

Course Curriculum

Introduction
Course Overview FREE 00:08:00
Pre-requisites FREE 00:02:00
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 Pre-requisites
Brushing-up Python (Part-I) 00:21:00
Brushing-up Python (Part-II) 00:19:00
Python for Machine Learning
NumPy 00:17:00
Pandas (Part-I) 00:14:00
Pandas (Part-II) 00:16:00
Matplotlib 00:16:00
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
ML Quiz1 00:06:00
Linear Regression
Machine Learning Model: Hypothesis Function 00:09:00
Hypothesis Function (Reading) 00:05:00
Linear Regression 00:11:00
Cost Function 00:13:00
Cost Function (Reading) 00:10:00
Derivatives 00:04:00
Derivatives (Reading) 00:05:00
Gradient Descent 00:06:00
Gradient Descent 00:10:00
Debugging gradient descent 00:04:00
Mean Normalization 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
ML Quiz2 00:05:00
Linear Regression Assignment 00:10:00
Data Visualization
Seaborn (Part-I) 00:19:00
Seaborn (Part-II) 00:12:00
Plotly 00:17:00
ML Quiz3 00:05:00
Artificial Neural Networks with TensorFlow
Matrix Multiplication 00:06:00
Artificial Neural Networks 00:17:00
Artificial Neural Networks (Reading) 00:00:00
Non-Linear Activation 00:10:00
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
ANN Assignment 00:00:00
Creating Datasets with Google Images & Building Classificaion Model
Downloading URLs 00:14:00
Building Cat vs Dog classification model 00:16:00
ML Quiz4 00:05:00
What's Next?
Deep learning and computer vision 00:13:00

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