Practical Machine Learning in Python Full Course

What is Machine Learning?
Can we train a machine to distinguish a cat from a dog? We will start with an overview of machine learning and its applications, then we will look at the various machine learning algorithms which are broadly classified as supervised, unsupervised and deep learning (neural network) algorithms.
We will start with a short theory and jump into practical examples and applications for linear regression, logistic regression, Clustering, Support Vector Machines, K Nearest…

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2 thoughts on “Practical Machine Learning in Python Full Course

  1. sir kindly tell me these modules include in machine learning Course????
    Module 5

    Computer vision using OpenCV and Python

    Module Contents:

    • Installing and configuring OpenCV

    • Data types and structures

    • Image types

    • Image pre-processing

    • Image denoising

    • Manipulating pixels

    • Scaling and rotating images

    • Using video inputs

    • Creating custom interfaces

    • Thresholding

    • Object detection

    • Face and feature detection

    • Template matching

    • CNN• Transfer Learning

    • Object Detection

    • Image Classification

    Module 6

    Natural language processing using NLTK and Python

    Module Contents:

    • Basic text analysis with NLTK

    • Text pre-processing

    • Stopword removal

    • Stemming and lemmatization

    • Parts of speech tagging

    • Chunking

    • Named entity recognition

    • Wordnet with NLTK

    • Text classification

    • Converting word to features

    • Classifying text documents using NLTK

    • Integration with scikit-learn classifiers

    • Gensim

    • Word2vec

    • Investigating data biasness using NLTK

    • Twitter sentiment analysis using NLTK

    Module 7

    Building AI systems through Deep Learning and NVIDIA GPUs: Keras and


    Module Contents:

    • What is Deep Learning and what are Neural Networks?

    • Artificial Neural Networks (ANN) Intuition

    • Building an ANN

    • Evaluating Performance of an ANN

    • Hands-On Exercise

    • Introduction to Keras and TenserFlow

    • Convolutional Neural Networks (CNN) Intuition

    • Building a CNN

    • Evaluating Performance of a CNN

    • Hands-On Exercise

    • Recurrent Neural Networks (RNN) Intuition

    • Building a RNN

    • Evaluating Performance of a RNN

    • Hands-On Exercise

    • Image Classification with DIGITS

    • Object Detection with DIGITS

    • Neutral Network Deployment with DIGITS and TensorRT

    Module 8

    Automatic speech recognition

    Module Contents:

    • How Speech Recognition Works – An Overview

    • Picking a Python Speech Recognition Package

    • Installing SpeechRecognition

    • The Recognizer Class

    • Working With Audio Files

    • Supported File Types

    • Using record() to Capture Data From a File• Capturing Segments With offset and duration

    • The Effect of Noise on Speech Recognition

    • Working With Microphones

    • Installing PyAudio

    • The Microphone Class

    • Using listen() to Capture Microphone Input

    • Handling Unrecognizable Speech

    • Putting It all Together: A “Guess the Word” Game

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