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Duration : 1 Month
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Machine Learning From Scratch with Python

Our Course is designed to provide you a deep understanding of the applications of Machine Learning in major fields and kickstart your career in Machine Learning.

 

Benefits :

  1. 1-year premium access to all Courses and content on platform
  2. Instructor Led Interactive Live Sessions
  3. Discussion Forum
  4. 1-1 Mentor sessions 
  5. 5 day refund Policy
  6. Encrypted Certificate 

                                                                

 

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  • 4.9
  • Start Date Timing Days
    17 August 2020 8.30 PM - 11.30 PM (IST) Sat - SUN
    17 August 2020 7:00 AM - 10:00 AM (IST) Mon - Fri
    17 August 2020 8.30 PM - 11.30 PM (IST) Mon - Fri

    Machine Learning is one of the leading technology nowadays. In the year 2019, it is estimated that the machine learning industry grew by 400%. It is also found that every year, around 2.7 million jobs will be created by ML. In this course, you will learn all the machine learning models such as Regression, Classification and Clustering. All algorithms are explained with simple example. To implement the machine learning models, Python will be used in this course. The coding is explained step by step so that you can understand how to develop your own models. Each machine learning model in this course has the following 

    1. Theory - explained with simple examples

    2. Python code - Full and clean documented code available for download

    3. Interview questions - Helps to clear the machine learning interview.

  • Basics of Python

    1. *  Introduction to Python
    2. * Installing python
    3. * Making  OS recognize python
    4. * PATH variable introduction
    5. *  Variable declaration in python
    6. *  Operators in python

     

    Data Types in python

    1. *  Data type classification
    2. * String data type and list slicing

     

    Program Flow Control python

    1. *  Conditional statement: if-elif-else
    2. *  Looping in python
    3. * Implement examples on loops and conditional statements

     

    Functions, Modules and Packages in python

    1. *  Declaration and implementation of functions
    2. *  Importance of module programming in python
    3. *  Packages in python
    4. *  Lambda function declaration and use in python
    5. *  Programming using functions, modules and external packages

     

    Strings and Dictionary Manipulations in python

    1. *  Building blocks of python programs
    2. *  String using in build methods
    3. *  Use of manipulation in build methods
    4. *  Dictionary manipulation in python
    5. *  Programming in python

     

    File handling in python

    1. *  Reading config files in python
    2. *  Writing log files in python
    3. *  Understanding read functions, read(), readline() and readlines()
    4. *  Understanding write functions, write() and writelines()
    5. * Programming in python

     

    Oops with python

    1. * Oops concepts with python
    2. * Programming using Oops support

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    INTRODUCTION TO ML :

    •  What is it and where is it used ?
    • Major Applications and the companies using it
    • Overview of Types of ML


    UNDERSTANDING SUPERVISED LEARNING :

    •  Model Overview : training and testing
    • Hypothesis formation
    • Understanding the prameters
    • COST function (derivation and application)
    • Types of Errors (SSE,SSR)
    • Computing Cost by hand
    • Computing Cost with numpy
    • Gradient Descent (derivation and types)
    • Computing  Gradient descent with numpy


    ALGORITHM 1 : LINEAR REGRESSION:

    • Linear Regression with single Variable
    • OLS (ordinary least square)Estimator
    • multi Variable Linear Regression
    • Normal Equation
    • R2 score


        Project: Case Study

    • Implementation in numpy
    • Implementation in scikit-learn


    ALGORITHM 2 : LOGISTIC REGRESSION:

    • sigmoid function
    • Decision boundary
    • Cross-validation


        Project: Case Study

    • Implementation in numpy
    • Implementation in scikit-learn


    ALGORITHM 3 : Decision Trees:

    • Intro and Types
    • ID3 algo from scratch derviation
    • Regression and Classification Cases


        Random Forestsase Study

    • Implementation in numpy
    • Implementation in scikit-learn


    ALGORITHM 4 :Support Vector Machines:

    Case Study

    • Implementation in numpy
    • Implementation in scikit-learn


    UNSUPERVISED ALGORITHMS

    • K-MEANS clustering


        Project :Case Study

    • understanding image structure
    • creating color pallete with kmeans

     

    PART II : NEURAL NETWORKS


    1 . Artificial Neural Networks (ANN)

    • What are neural networks 
    • Neurons
    • Activation functions
    • How do Neural networks work


         Project :Churn Modelling 

    • Making a network to help out a bank


    2 . Convolutional Neural Networks (CNN)

    • Architecture Overview
    • Convolution layer
    • Kernels and feature maps
    • Pooling Layer
    • Flattening
    • Full Connection


        Project :Classify Cat or Dog 

    • Making a network to classify dog and cats


    PART III : Natural Language Processing

    • NLP meaning and applications
    • Tokenizing
    • Stop words
    • Stemming and Lemmatization
    • POS Tagging
    • Wordnet
    • SentiWordnet


        Project :
              Create program for sentiment analysis with NLTK

  • Course Faq

    • Who is the Trainer

      You trainer will be sir Vaqas ahmed , he is the founder of Pytholabs and has industry expert and experience of 5 years