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Artificial Intelligence and Machine Learning for Software Engineers

  /    /  Artificial Intelligence and Machine Learning for Software Engineers

Artificial Intelligence and Machine Learning Fundamentals for Software Engineers

Artificial Intelligence

Excellence in Machine Learning requires expertise in Data Manipulation and Analysis.

With more than a decade of experience leading and managing Machine Learning Platforms of different sizes, scale & complexity, this is a course by DataCouch that prepares you to become a Data Scientist

Course Overview

This course is stepping stone for “Machine Learning and Artificial Intelligence” learning path. It has been designed and developed for creating solid foundation for subsequent courses in the learning path.

The intended audience for this course:

  • Data Engineers
  • Data Scientists
  • Machine Learning Engineers
  • Integration Engineers
  • Architects
Session 1: Holistic View and Concepts

Have you ever wondered how Data Scientists spend their day? Are you confused with responsibilities of a Data Scientist, Data Analyst, Machine Learning Engineer, Data Engineer and Machine Learning Researcher? Have you ever evaluated various options for performing experiments? Do you know how to approach a Data Science problem? Do you know the power of Feature Engineering in the modeling process? Do you know various key types of Machine Learning? Have you got clarity on Bias Variance Trade Off? Do you Cross Validation can enable us to validate Model performance on limited datasets?

If you want to get a lucid understanding of above questions along with experiencing some amazing AI use cases and applications then this session is for you. Also towards the end of Day 1 session, you will actually be trained around Ethics in AI as “With Great Power comes Great Responsibility” 🙂

Session 2: Just Enough Python

Do you want to move into Machine Learning/Data Science but you don’t have any programming background? If yes then this session will assist you to transition to a programmer role in just a couple of hours. Below is more detailed information about the session –

During the session, you will learn
  • How to work with Anaconda Jupyter Notebooks/Labs for performing experiments with Data
  • To install custom libraries in Anaconda which are not part of the distribution
  • The concept of Virtual environments in Anaconda
  • How to get your hands dirty with some Python basics which will enable and empower you to write some basic Python programs
  • How to know practical applications of Complex Data Types List like Set, Dictionary etc.
  • How to learn about List Comprehensions, Lambda Functions, Custom Functions etc.

If any of the above questions relate with what you are looking to learn then this Pragmatic session is for you.

Session 3: Making Data Useful for Machine Learning

Since more than 60% of the time goes into Data Manipulation related activities in the Data Science process hence this session is very much useful and relevant for aspiring Data scientists.

During the session, you will learn
  • How to make data useful for Machine learning based on practical experience of the Instructor
  • How to read, select, summarize, filter, sort and join various datasets
  • Practically how Feature Engineering can be applied for Effective modeling process using various scenarios
  • Working with Categorical data
  • Detecting and handling of missing data
  • Detecting and handling of outlier data
  • How to perform Data Processing and Analysis using Pandas
Session 4: Data Pipelines and Data Exploration using Matplotlib and Seaborn

Data Exploration is one of the key steps in Data pipelines related to Machine Learning.

During the session, you will understand
  • Data pipelines for small as well as for large datasets
  • Why do we perform Data Exploration?
  • Key Types of Charts e.g. HeatMaps, Pairplot, Boxplot etc.
  • Which chart to choose when?
  • Perform Data Exploration using key Python libraries for Data Visualization
Session 5: Statistics Data has a story to tell.. decipher these stories using Statistics.
During the session, you will learn
  • What is Statistics- Key types of Statistics
  • Gain practical understanding of Inferential statistics using Z Test
  • Learn about role of Linear Algebra in Data Science
  • Get clarity about Stats terms e.g. Mean, Median, Mode, Variance, Standard Deviation Range, IQR, Skewness, Peakedness etc.
  • Bias and Variance Tradeoff
  • Covariance and Correlation
  • Pearson and Spearman Coefficients
  • Normal Distribution and it’s role in Machine Learning

Participants should preferably have some hands-on experience on Python

Course Information


3 Days

Mode of Delivery

Instructor led/Virtual


Beginner to Intermediate

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