Machine Learning using Python at Goeduhub Technologies Jaipur

Machine Learning using Python

Prerequisites -Python

Goal: In this module, you’ll get a complete knowledge of python and it’s libraries which are going to be used in better understanding in problem solving of Machine Learning.

Topics:

  • Getting Started with python
  • Data Types and Variables
  • Operators
  • Structural Data Types-Lists, Tuples, Strings & Dictionaries
  • Conditional Code
  • Loops and Iterations
  • Functions
  • Files I/O
  • Accessing Web Data

Python Libraries for Data Science

Introduction to NumPy

  • Understanding Data Types in Python
  • Fixed-Type Arrays in Python
  • Creating Arrays from Python Lists
  • Creating Arrays from Scratch
  • NumPy Array Attributes
  • Reshaping of Arrays
  • Computation on NumPy Arrays: Universal Functions
  • Fancy Indexing
  • Sorting Arrays
  • Structured Data: NumPy’s Structured Arrays

Data Manipulation with Pandas

  • Installing and Using Pandas
  • Introducing Pandas Objects
  • Data Indexing and Selection
  • Operating on Data in Pandas
  • Handling Missing Data
  • Hierarchical Indexing
  • Combining Datasets: Concat and Append
  • Combining Datasets: Merge and Join
  • Aggregation and Grouping
  • Pivot Tables
  • High-Performance Pandas: eval() and query()

Visualization with Matplotlib

  • Importing matplotlib
  • Simple Line Plots
  • Simple Scatter Plots
  • Visualizing Errors
  • Density and Contour Plots
  • Histograms, Binnings, and Density
  • Multiple Subplots
  • Text and Annotation
  • Other Python Libraries for Data Scientists-
  • Scipy
  • Scikit-learn
  • Seaborn
  • Reading Data; Selecting and Filtering the Data; Data manipulation, sorting, grouping, rearranging
  • Plotting the data
  • Descriptive statistics
  • Inferential statistics

Machine Learning

  • The Math behind Machine Learning: Linear Algebra
  • Scalars
  • Vectors
  • Matrices
  • Tensors
  • Hyperplanes

The Math Behind Machine Learning: Statistics

  • Probability
  • Conditional Probabilities
  • Posterior Probability
  • Distributions
  • Samples vs Population
  • Resampling Methods
  • Selection Bias
  • Likelihood

Introduction to Machine Learning

  • 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 : LINEAR REGRESSION:

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

Project: Case Study

  • House Price Prediction
  • Implementation in numpy
  • Implementation in scikit-learn

ALGORITHM : LOGISTIC REGRESSION:

  • sigmoid function
  • Decision boundary
  • Cross-validation

Project: Case Study

  • IRIS Dataset Prediction
  • Implementation in numpy
  • Implementation in scikit-learn

ALGORITHM : Decision Trees:

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

Project case study

  • Implementation in numpy
  • Implementation in scikit-learn

Clustering (K-Means)

  • Unsupervised
  • Features and data vectors
  • Various steps of algo
  • Understanding of Clusters and various types of
  • Applying K-Means on datasets and their practical use
  • Applications of Clustering and the algorithm

UNSUPERVISED ALGORITHMS

  • K-MEANS clustering
  • Generative Modeling Through Baysian Sampling

Project :Case Study

  • understanding image structure
  • creating color pallete with kmeans
  • Generating Handwritten Digits

Neural Network (NN)

  • What’s a Neural Network?
  • Various Structures of NN
  • Understanding Fundamentals and Various parameters of NN
  • ANN,CNN and RNN
  • Deep Dive with the Implementaion of NN on various datasets
  • Applying CNN on Images

Applications and its complexities over other algorithms Project:- Smart Machine Learning System

Integration of Machine Learning

Goal : In this module, you’ll about classical image analysis techniques such as Edge

detection,watershed,distance transformations using the OpenCV library .Here you’ll explore the evolution of image analysis ,from classical deep learning techniques.

Objectives - At the end of this module, you should be able to:

  • Introduction to computer vision and Image Processing
  • Image processing using OpenCV
  • Video processing and Image extraction using OpenCV
  • Convolutional Features for visual recognition
  • Object ,Face and Gestures Detection using Haar Cascade Classifier
  • Object Tracking and Action Recognition
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