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Python Data Science Development

IPA-P102

Python Data Science Development

The IPA-P102: Python Data Science Development training program discuss the latest machine learning algorithms while also covering the common threads that can be used in the future for learning a wide range of algorithms. The course is a complete package that will help learners build their Python coding skillsets to meet the demand of the ML-AI industry which is growing by leaps and bounds in recent years.


Training Duration: 5 Days

OVERVIEW

Machine Learning, Artificial Intelligence and Deep Learning training program discusses the latest machine learning algorithms while also covering the common threads that can be used in the future for learning a wide range of algorithms. The course is a complete package that will help learners build their skillsets and meet the demand of the ML-AI industry which is growing by leaps and bounds in recent years.


This course on Machine Learning, Deep Learning and Artificial Intelligence goes beyond the theoretical concepts of the technology like regression, clustering, classification, etc. and discusses their applications as well.

OBJECTIVES

You will learn:

  • Introduction to Machine Learning, Artificial Intelligence, and Deep learning

  • Supervised and unsupervised learning concepts and modelling

  • Solving business problems using Artificial Intelligence and Machine Learning

  • Machine Learning algorithms

PREREQUISITES

Participants in this Machine Learning online course should have:

  • Familiarity with the fundamentals of Python programming

AUDIENCE

There is an increasing demand for skilled machine learning engineers across all industries, making this Machine Learning certification course well-suited for participants at the intermediate level of experience. We recommend this Machine Learning training course for the following professionals in particular:

  • Developers aspiring to be a data scientist or machine learning engineer

  • Analytics managers who are leading a team of analysts

  • Business analysts who want to understand data science techniques

  • Information architects who want to gain expertise in machine learning algorithms

  • Analytics professionals who want to work in machine learning or artificial intelligence

  • Graduates looking to build a career in data science and machine learning

  • Experienced professionals who would like to harness machine learning in their fields to get more insights

COURSE MODULES

Module 1: Introduction to Artificial Intelligence and Machine Learning

  • Artificial Intelligence

  • What is Machine Learning?

  • Machine Learning algorithms

  • Supervised Versus Unsupervised Learning

  • Machine Learning Algorithms

    • Regression

    • Classification

    • Clustering

  • Applications of Machine Learning

  • Machine learning examples

  • Setting up Anaconda & Python Notebooks.

  • Working on notebooks for Data Science


Module 2: Techniques of Machine Learning

  • Supervised learning

  • Unsupervised learning


Module 3: Mathematics & Statistics Refresher

  • Concepts of linear algebra

  • Euclidean and Non-Euclidean geometry

  • Introduction to Calculus

  • Probability, Conditional Probability, Bayes Theorem

  • Distributions, CDF, PDF

  • Mean, Median, Mode

  • Variance & Correlation,

  • Standard Deviation, quartiles, percentiles

  • Variable Relationships & Estimation

  • Hypothesis Testing


Module 4: Accessing/Importing and Exporting Data

  • Importing Data from various sources (Csv, txt, excel…etc)

  • Viewing Data objects

  • Exporting Data to various formats

  • Important python modules: numpy, pandas, scipy etc.


Module 5: Introduction to NumPy, Pandas

  • Create arrays using NumPy

  • Perform various operations on arrays and manipulate them

  • Indexing slicing and iterating

  • Read & write data from text/CSV files into arrays and vice-versa

  • Create Series and Data Frames in Pandas

  • Data structures & index operations in pandas

  • Importing and exporting data

  • Indexing and slicing of data structures in pandas

  • Reading and Writing data from Excel/CSV formats into Pandas


Module 6: Data Cleaning- Manipulation

  • Basic Functionalities of a data object

  • Merging of Data objects

  • Concatenation of data objects

  • Types of Joins on data objects

  • Exploring a Dataset

  • Analysing a dataset

  • Data Manipulation steps (sorting, filtering, duplicates, merging, appending, derived variables, sampling, Data type, conversions, renaming, formatting etc)

  • Data manipulation tools (Operators, Functions, Packages, control structures, Loops, arrays etc)

  • Python Built-in Functions (Text, numeric, date, utility functions)

  • Normalizing data

  • Formatting data


Module 7: Data Analysts-Visualization

  • Introduction exploratory data analysis

  • Descriptive statistics, Frequency Tables and summarization