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Machine Learning

Learn AI ML with Screen Reader Support

Skilling | General

English

Level: Intermediate

Last updated: Sept. 18, 2023

Arunkumar

Viewed by669
9

Prerequisites

Basics of Python and Maths are a must
In python candidate should know
1. Data types, variables and operators
2. Data structures like set , lists , tuples
3. Conditional statements and loops
4. Functions and classes

10th Grade mathematics as per Indian standards.

Description

What is Machine Learning?
Machine learning is crucial because it empowers computers to learn and make predictions from data, enabling automation and intelligence in various domains. It enhances decision-making, from healthcare diagnosis to personalized recommendations, by extracting valuable insights from vast datasets. ML is essential for tackling complex problems like fraud detection, natural language processing, and autonomous vehicles. Its ability to adapt and improve over time makes it invaluable in an ever-evolving technological landscape, fostering innovation and efficiency. Learning machine learning is vital for professionals to harness its potential, driving advancements and creating solutions that improve our lives and industries.

Why this course?
1.
2. This course provides a variety of learning resources, such as video lectures, written materials, and coding exercises. This caters to different learning styles.
3. It is a self paced course. which allows flexibility to learners in terms of pacing and scheduling. It should accommodate both full-time students and working professionals, allowing learners to study at their own pace.
4. The course provides a strong foundation in machine learning concepts, algorithms, and techniques. It should assume no prior knowledge of machine learning and gradually build up the necessary skills.
5. The course uses clear and accessible language, making complex concepts understandable to beginners while still offering insights and challenges for professionals.
6. This course emphasizes on the practical application of machine learning in real-world scenarios,

What will you learn?
Candidate will learn about the life cycle of Machine learning - which includes data analysis, data cleaning, feature engineering, feature selection, model creation, hyper parameter tuning and model deployment.

Demo Link
The demo of this course is available at https://youtu.be/H3pe5v0i6hg

Who can enroll for the course?

This is one of its kind course in India.This course is suitable for both beginners and professionals looking to start their journey in machine learning. This course provides screen reader support for persons with vision impairment however anyone who can listen, type and understand can learn from this course.

Will you recieve a certificate after course completion?

Yes, we provide certificate after assessment and after an additional cost of INR 299. Please write to admin@prishni.in for more details.

Course content

Expand all
6 modules
13 hours 51 minutes

Python crash course and environment setup

17 submodules 165.0

Introduction to Machine Learning

15.0

Download course resources

5.0

Introduction to Machine Learning (Reading Material)

5.0

Environmental Setup

15.0

Environmental setup (Reading material)

5.0

Jupyter notebook

15.0

Jupyter notebook (reading material)

5.0

Python in jupyter notebook

15.0

Python in jupyter notebook (reading material)

5.0

If.. else, loops, functions

15.0

if..else, loops, functions (reading material)

5.0

python data structures

15.0

python data structures (reading material)

5.0

python files

15.0

python files (study material)

5.0

Python classes and objects

15.0

python classes and objects (reading materials)

5.0

Data analysis, Data cleaning, Feature engineering

14 submodules 140.0

Numpy

15.0

Numpy (reading material)

5.0

Pandas

15.0

Pandas (reading material)

5.0

Data cleaning

15.0

Data cleaning (readingmaterial)

5.0

Removing Outlier

15.0

Removing Outlier (reading material)

5.0

Feature Engineering - Part 1

15.0

Feature Engineering - Part 1 (reading material)

5.0

Feature Engineering - Part 2

15.0

Feature Engineering - part 2 (reading material)

5.0

Feature Selection

15.0

Feature Selection(reading material)

5.0

Supervised learning

22 submodules 200.0

Simple linear regression

15.0

Simple linear regression (reading material)

5.0

multiple linear regression

15.0

Multiple linear regression (reading material)

5.0

polynomial regression

15.0

polynomial regression (reading material)

5.0

Regression metrics

15.0

Regression metrics (reading material)

5.0

Understanding of Linear regression

15.0

Understanding of Linear regression (reading material)

5.0

SVR, DT, RF models

15.0

SVR, DT, RF (reading material)

5.0

implementation of SVR, DT, RF

15.0

SVR (reading material)

5.0

DT (reading material)

5.0

RF (reading material)

5.0

Understanding of Classification

15.0

Understanding for classification (reading material)

5.0

implementation of LR, KNN, SVM

15.0

Logistic regression (reading material)

5.0

KNN (reading material)

5.0

SVM (reading material)

5.0

Unsupervised learning

12 submodules 120.0

Introduction to Unsupervised learning

15.0

Introduction of Unsupervised learning (reading material)

5.0

K means clustering

15.0

K means clustering (reading material)

5.0

Anomaly detection

15.0

anomaly detection (reading material)

5.0

Association rule mining

15.0

Association rule mining (reading material)

5.0

Timeseries - part 1

15.0

Timeseries - part 1 (reading material)

5.0

Timeseries -part 2

15.0

Timeseries - part 2 (reading material)

5.0

Hyperparameter tuning, ML pipeline and AutoML

16 submodules 160.0

Hyperparameter tuning

15.0

Hyperparameter tuning (reading material)

5.0

Grid search

15.0

Grid search (reading material)

5.0

Random search

15.0

Random search (reading material)

5.0

Machine learning pipeline

15.0

Machine learning pipeline (reading material)

5.0

ML pipeline implementation

15.0

ML pipeline implementation(reading material)

5.0

AutoML

15.0

AutoMl (reading material)

5.0

Pycaret

15.0

Pycaret (reading material)

5.0

Pycaret timeseries

15.0

Pycaret timeseries (reading material)

5.0

Model deployment

6 submodules 46.0

Saving and loading model

1.0

Save and load model (reading material)

5.0

Model deployment

15.0

Model deployment (reading material)

5.0

Course Summary and next steps

15.0

Course summary and next steps (reading material)

5.0

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