Machine Learning Course
₹699.00
The Machine learning online course will cover all the concepts from basics to intermediate and will upgrade the skills of a learner
The course contains both theory and practical.
The course is 40 hours of extensive learning Lifetime access to videos Complete all modules and get certified.
Mission | Machine Learning Course
Online Machine Learning Course combines computer science, statistics, and Data Science to harness that predictive power. It’s a must-have skill for anyone else who wants to wrestle all that raw data into refined trends and predictions.
Course Description
This comprehensive course will be your guide to learning how to use the power of Python to analyze
data, create beautiful visualizations, and use powerful machine learning and deep learning algorithms
like SVM, Artificial Neural Network, Convolutional Neural Network. This course is designed for beginners with some programming experience or experienced developers.
Prerequisites | Machine Learning Training
- General Requirements
You are enthusiastic and motivated to learn. Participation in machine learning training courses requires consistently meeting
project deadlines and active participation. - Program-Specific Requirements
You have access to a computer with an internet connection, on which you’ll install a professional
code/text editor (VSCode).
You can independently solve and describe your solution to a math or programming problem.
Prior experience in python language.
Course Content
Skills you will Learn
1. Problems on Hackerrank
2. Plotting Crime Data using folium, and
most used Data Science packages
3. Data Exploration on dataset like Titanic
from Kaggle
4. Applying machine learning model on the
above projects and submitting the solution
on Kaggle
5. Cat Dog classification using SVM
6. Clustering movie Dataset
7. Working on MNIST dataset and submit
solution on Kaggle
https://www.kaggle.com/c/digit-recognizer
8. Extra: Yolo Object detection
Projects
1. Problem Solving Skills, Introduction to
Online judges and Python Syntax
2. How to read the documentation and
understand packages
3. Data exploration, cleaning and visualization
Feature Engineering
4. Intro to Machine Learning, Decision Trees,
Random Forests, SVM, Ensembles: Bagging vs
Boosting, Clustering for Unsupervised Learning,
K-Means on Two-Dimensional Data,
K-Means on n-Dimensional Data
5. Learn what is a Neural Network, implementing
with Artificial Neural Network, Convolutional
Neural Network.
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