AIML Foundation Module

COURSE | Started On : Monday, 22 March 2021 11:00 | 1
Instructor: Evelyn Benedict Evelyn Benedict

Appreciation of different aspects of AIML 1. What is AI and how machines learn - concept and the m...


It may not be visible but today algorithms are all around us. They’re in the cell phone, in the laptop, in the car, in the house and even in the toys. The banks run on algorithms, airlines and factories run on algorithms. In this course we try to understand what these algorithms are and more importantly, how we can generate these algorithms ourselves; not in the conventional way of coding but making the machine learn how to code themselves. It would not only enable us to do more but also enable us to generate more algorithms which are too complex. And it would be faster, possibly cheaper and definitely more versatile. By more versatile I mean doing things that machines have not been able to do with conventional programming, think like helping computers see and understand human language and sense several other things in the environment. Sounds quite interesting. So, let us start our journey. Hope by the end of the course you would have a much deeper understanding of what is artificial intelligence and machine learning and how we can make use of it in our daily lives and in our business. Not only to make things faster and easier but also to do transformative things which may not appear possible now.

Overview of AIML Overview of AIML
1. A brief history
2. What has changed in recent years
3. Why AIML has become a powerful force of change
4. How it can be applied in various domains
5. What we expect over the next few years
Different types of Machine Learning 1. Supervised Learning
2. Unsupervised Learning
Dimensionality Reduction
3. Deep Learning - brief introduction to
Artificial Neural Networks
Recurrent Neural Networks
Convolutional Neural Networks
Generative Networks
4. Reinforcement Learning
Understanding the suitability, pros and cons of using various algorithms for different types of business problems.
Application of Deep Learning 1. Conventional areas - making predictions, classification, regression ...
2. Natural Language processing
3. Computer vision
How different deep learning architectures make it possible.
Demystifying AIML Working with one sample problem - getting a feel of how to train machines to learn
Lifecycle of creating a solution Understanding the lifecycle stages of AIML and how it differs from conventional IT projects. Appreciation of changes needed in skills and mindset.
1. Problem formulation
2. Data Preparation
3. Model Selection
4. Feature Engineering
5. Model optimisation
Performance Metrics RMSE, Precision, Recall, F1-Score, Accuracy



  • Data Science

2 month ₹ 14999
Course Information
Evelyn Benedict


Published Date
2021-03-22 11:00:00

Certificate Term
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A brief history of AI and Machine Learning
Introduction to Data Science
Getting to know what we would learn
Overview of AIML
Finding use of ML is left to our imagination - importance of problem formulation
ML with Python is simple - an example
To be written
Types of Machine Learning - PPT
Types of Machine Learning
This is Types of Machine Learning
Regression - Part 1
This is on Regression
Regression - Part 2
Classification exercise with digital data base
Classification Performance Metrics
Use Cases of Classification
More about types of Supervised learning
This is More about types of Supervised learning
This is k-fold
Deep Learning
This is Deep Learning
Lifecycle of ML - Solution
This is the Lifecycle of ML - Solution
Lifecycle of ML - Regression
This is Lifecycle of ML - Regression
Lifecycle - Data Munging and Data Wrangling
This is the Lifecycle - Data Munging and Data Wrangling
Lifecycle of ML - 2 Regression
This is the Lifecycle of ML - 2 Regression
AIML Foundation Module