Machine learning with python training Machine Learning in Python Training in Kathmandu, Nepal

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:

  1. Python Programming
  2. Python with Data Science


Duration: 10 Weeks

  1. Introduction

    1. Machine Learning: Introduction, Supervised, Unsupervised, Reinforcement Learning
    2. Python Programming
    3. Data for Machine Learning, DataSets.
  2. Python Programming

    1. Overview, Requirement Installation
    2. Operators, Decision Making, Loops, Functions
    3. Strings, Lists, Tuples, Dictionary (Comprehensions), File handling
    4. Lambda, zip, map, filter, reduce
    5. Modules (re, os, datetime, numpy, scipy, scikit-learn, matplotlib, seaborn..)
    6. Data extraction and Visualization
    7. Jupyter Notebook
  3. Machine Learning

    1. Linear Algebra: Matrices & Vectors, Addition and Scalar Multiplication, Matrix Vector Multiplication, Inverse & Transpose.
    2. Regression: Simple, Multiple, Polynomial, Model Evaluation, Estimation, Prediction & Assumptions.
    3. Classification: Logistic Regression, K-NN, Naïve Bayes, Random Forest.
    4. Python Modules exposure.
  4. Machine Learning implementation with Python

    1. Implementing Real-world/Extracted Dataset (scipy,scikit-learn..)
    2. Visualization & presentation.

Course Duration:  10 Weeks

TimeLine:

  1. Week 1: Introduction to ML, ML & Python Programming
  2. Week 2: Python Programming & Data Collection
  3. Week 3-10: Implementing Machine Learning Techniques with Python.

 

Enrollment Requirements:

  1. Python Programming
  2. Python with Data Science