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Machine learning with python training

Machine Learning in Python Training in Kathmandu, Nepal

Duration: 10 Weeks Career Option: Data Scientist

Updated on: 6th Dec, 2018

Machine learning with Python Training

Machine learning explores the study and construction of algorithms that can learn from and make predictions on Data, also is the subfield of computer science that believed to evolve from study of Pattern Recognition and Computational Learning Theory in Artificial Intelligence. According to some authors, it gives computers the ability to learn without being explicitly programmed. Machine learning emphasizes on the development of computer programs, which when exposed to new data, can change.

Machine learning is about discovering and displaying the patterns inside/from the data, whereas Data analytics is about discovering knowledge from Data. Sometimes, also known as method of Data analysis that automates analytical model building. In machine learning, computers apply statistical learning techniques to automatically identify patterns in data. These techniques can be used to make highly accurate predictions. Machine learning is mostly used on email filtering, web search, speech recognition, computer vision (acquiring, processing, analyzing, understanding digital image, and producing numerical or symbolic information), optical character recognition, computational statistics, prediction model, data analytics etc.

Python as a programming language has evolved over the years and today, it is the number one choice for a learner, programmers and research workers. Python offers a good blend of functionality and specialized packages containing Machine Learning Algorithms. Python is an often-used language that is well known for producing compact, readable code. This fact has led a number of leading companies, research institutions to adopt Python for prototyping and deployment; it is also used in industrial applications and in scientific programming. It has a number of packages that support computationally intensive application like machine learning, and it is a good collection of the leading machine learning algorithms. 

Course contents and resources comprises concepts of advance level of python programming with mathematical/statistical implementation. After completion of this course, students will learn about the effective machine learning techniques. Additionally, they will learn to implement them and work using Python programming language.

 

Pre-requisites for Machine learning with Python Training

  1. Python Programming
  2. Python with Data Science

Courses Outline :- Machine learning with python training
  • Getting Started with Python
  • Environment setup
  • The Python programming language
  • Applications Area

Values and types

  • Variables
  • Variable names and keywords
  • Operators and operands
  • Expressions and statements
  • Interactive mode and script mode
  • Order of operations
  • String operations
  • Comments
  • Debugging

Function calls

  • Type conversion functions
  • Math functions
  • Composition
  • Adding new functions
  • Definitions and uses
  • Flow of execution
  • Parameters and arguments
  • Variables and parameters are local
  • Stack diagrams
  • Fruitful functions and void functions
  • Why functions?
  • Importing with from
  • Debugging

Modulus operator

  • Boolean expressions
  • Logical operators
  • Conditional execution
  • Alternative execution
  • Chained conditionals
  • Nested conditionals
  • Recursion
  • Stack diagrams for recursive functions
  • Infinite recursion
  • Keyboard input
  • Debugging

 

Return values

  • Incremental development
  • Composition
  • Boolean functions
  • More recursion
  • Leap of faith
  • Checking types
  • Debugging

Multiple assignments

  • Updating variables
  • The while statement
  • Break
  • Debugging
  • For loop

A string is a sequence

  • Len
  • Traversal with a for loop
  • String slices
  • Strings are immutable
  • Searching
  • Looping and counting
  • String methods
  • The in operator
  • String comparison
  • Debugging

A list is a sequence

  • Lists are mutable
  • Traversing a list
  • List operations
  • List slices
  • List methods
  • Map, filter and reduce
  • Deleting elements
  • Lists and strings
  • Objects and values
  • Aliasing
  • List arguments
  • Debugging

 

    Dictionary as a set of counters

  • Looping and dictionaries
  • Reverse lookup
  • Dictionaries and lists
  • Memos
  • Global variables
  • Long integers
  • Debugging

Tuples are immutable

  • Tuple assignment
  • Tuples as return values
  • Variable-length argument tuples
  • Lists and tuples
  • Dictionaries and tuples
  • Comparing tuples
  • Sequences of sequences
  • Debugging

     Usage

  • Union, Difference
  • Available methods

Introduction

  • Exceptions versus Syntax Errors
  • Raising an Exception
  • The AssertionError Exception
  • The try and except Block: Handling Exceptions
  • The else Clause

Persistence

  • Reading and writing
  • Format operator
  • Filenames and paths
  • Catching exceptions
  • Databases
  • Writing modules
  • Debugging

      Introduction, Application, and Usage

  • Reader and writer
  • DictReader and DictWriter
  • Simple CSV processing using functional programming

User-defined types

  • Attributes
  • Real World Example
  • Instances as return values
  • Objects are mutable
  • Copying
  • Debugging

Object-oriented features

  • The self
  • Printing objects
  • The init method
  • The __str__ method
  • Other special methods
  • Operator overloading
  • Type-based dispatch
  • Polymorphism
  • @staticmethod
  • Debugging

Introduction

  • checking callable or not
  • Decorators
  • creating and using decorators

 

      Introduction

  • Example
  • Class attributes
  • Private, Protected and Public
  • Multiple Inheritance
  • Class diagrams
  • Debugging
  • Data encapsulation

 

     Advanced Strings, Date & Time

  • Python os, re, sys
  • Comprehensions: List, Dictionary
  • CSV, JSON, XML, SQLite with Python
  • Introduction to Machine Learning concepts
  • Supervised vs. Unsupervised Learning
  • Installing Anaconda and Managing Environment
  • Introduction to Spyder and Jupyter notebook
  • Familiarization with Datasets
  • Data Exploration and Analysis
  • Machine Learning Libraries
    • Numpy
    • Scikit Learn
    • Matplotlib
    • Seaborn
    • Pandas
    • Scipy


 

Get the dataset

  • Importing the Libraries
  • Importing the Dataset
  • Missing Data
  • Categorical Data
  • Splitting the Dataset into the Training set and Test set
  • Feature Scaling


 

Introduction

  • Optimization and gradient descent
  • Linear regression implementation
  • Correlation Analysis and Feature Selection
  • Evaluate Model Performance
  • Multiple Regression and Feature Importance
  • Variance Bias Trade Off - Validation Curve
  • Variance Bias Trade Off - Learning Curve
  • Cross validation
     


 

  sigmoid function

  • Cross validation
  • Confusion Matrix, Precision, Recall and F1 Score
  • Precision and Recall Tradeoff
  • The ROC Curve
  • Introduction
  • Linear SVM Classification
  • Polynomial Kernel
  • Gaussian Radial Basis Function
  • Support Vector Regression
  • Introduction
  • Lazy learning
  • Euclidean-distance
  • Implementation
  • Introduction
  • Implementation
  • Classification and clustering applications
     
  • Decision trees
  • Basics
  • Entropy
  • Information gain
  • Implementation

 

  • Introduction
  • Principal Component Analysis
  • K-means clustering
  • Hierarchical clustering
  • Introduction to Reinforcement Learning
  • Applications of Reinforcement Learning
  • Examples including AlphaGo case study
     

Introduction to Deep Learning

  • Artificial Neural Networks
  • Introduction to deep learning libraries such as tensorflow, keras, pytorch etc.
  • Research works in deep learning
     


 

     Simple Linear Regression Modelling with Boston Housing Data

  • Iris Flowers Classification Project
  • MNIST Project - Logistic Regression
  • Text Classification using SVM

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