Python and ML and Open AI

The objective of this course is to give you a holistic understanding of machine learning, covering theory, application, and inner workings of supervised, unsupervised, and deep learning algorithms.
In this series, we'll be covering linear regression, K Nearest Neighbors, Support Vector Machines (SVM), flat clustering, hierarchical clustering, and neural networks.

Practical Machine Learning Tutorial with Python

  • Introduction
  • Regression Intro
  • Regression Features and Labels
  • Regression Training and Testing
  • Regression forecasting and predicting
  • Pickling and Scaling
  • Regression How it Works
  • How to program the Best Fit Slope
  • How to program the Best Fit Line
  • R Squared Theory
  • Programming R Squared
  • Testing Assumptions
  • Classification w/ K Nearest Neighbors Intro
  • K Nearest Neighbors Application
  • Euclidean Distance
  • Creating Our K Nearest Neighbors Algorithm
  • Writing our own K Nearest Neighbors in Code
  • Applying our K Nearest Neighbors Algorithm
  • Final thoughts on K Nearest Neighbors
  • Support Vector Machine Intro and Application
  • Understanding Vectors
  • Support Vector Assertion
  • Support Vector Machine Fundamentals
  • Support Vector Machine Optimization
  • Creating an SVM from scratch
  • SVM Training
  • SVM Optimization
  • Completing SVM from Scratch
  • Kernels Introduction
  • Why Kernels
  • Soft Margin SVM
  • Soft Margin SVM and Kernels with CVXOPT
  • SVM Parameters
  • Clustering Introduction
  • Handling Non-Numeric Data
  • K Means with Titanic Dataset
  • Custom K Means
  • K Means from Scratch
  • Mean Shift Intro
  • Mean Shift with Titanic Dataset
  • Mean Shift from Scratch
  • Mean Shift Dynamic Bandwidth

Deep Learning with Neural Networks and TensorFlow

  • Introduction
  • Installing TensorFlow (OPTIONAL)
  • TensorFlow Basics
  • Neural Network Model
  • Running our Network
  • Processing our own Data
  • Preprocessing cont'd
  • Training/Testing on our Data
  • Using More Data
  • Installing the GPU version of TensorFlow for making use of your CUDA GPU
  • Installing CPU and GPU TensorFlow on Windows
  • Recurrent Neural Networks (RNN)
  • RNN Example in Tensorflow
  • Convolutional Neural Networks Basics
  • Convolutional Neural Networks with TensorFlow

Training a neural network to play a game with TensorFlow and Open AI

  • TFLearn
  • Intro
  • Training Data
  • Training Model
  • Testing Network

Using Convolutional Neural Network to Identify Dogs vs Cats

  • Intro and preprocessing
  • Building the Network
  • Training
  • Using our Network

3D Convolutional Neural Network

  • Introduction
  • Reading Files
  • Visualizing
  • Resizing Data
  • Preprocessing data
  • Running the Network

Frequently Asked Questions

Will I get a certificate?

You can download your certificate immediately after the course completion

If I get doubts while taking the course who will help me?

We have a great commununity in our website. You can post your questions and also share your work in the community to get answers and feeback. Everyone will support you, including the mentor.

I am a beginner can I learn this course?

Yes you can learn it. All our courses are very beginner friendly once you start we will take you till advanced level. By the end of the course you will become industry ready.

I am from a different background will I get a job?

Yes absolutely. It does not matter from which background you are from... We will make you industry ready.

Will my past experience from differnet industry be considered?

Yes it will be considered, any experience is always useful.

How our placements work?

Once you complete the course, work on project, make your portfolio, create the resume and send it to us we will forward to 100s of companies with our recommendation and you will get calls from companies 😊.

How much will be my salary?

The salary starts at Rs.15,000 per month for freshers and goes upto Rs.60,000 in just 3 years. If you are an experienced person your salary depends on current salary.

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