Practical Machine Learning Bootcamp With Real World Projects

Two professional Data Scientists have designed this course so that they can share knowledge and help you learn complex theory, algorithms, and coding libraries. It creates a road map connecting several crucial concepts in Machine Learning, teaches them, and introduces tools to perform them.

Machine Learning Bootcamp Course Overview

In this course, you will learn the Basics of Python along with crucial techniques of  Deep Learning models. Python plays a major role in this training as 57% of data scientists and machine learning developers use it and 33% prioritize it for development.

You gain a complete understanding of all the concepts in Artificial Intelligence such as Python, Python for Data Science, Machine Learning, Deep Learning, and Time Series Analysis with the 4 different sections in this course.

It is structured in the following way:

PYTHON -

Data Structures, List, Tuples, Dictionary, Libraries, Functions, Operators, etc

Data Cleaning and Preprocessing

MACHINE LEARNING -

Regression: Simple Linear Regression, SVR, Decision Tree, Random Forest,

Clustering: K-Means, Hierarchical Clustering Algorithms

Classification: Logistic Regression, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification

Natural Language Processing: Bag-of-words model and algorithms for NLP

DEEP LEARNING -

Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Long short term Memory, Vgg16, Transfer learning, Web Based Flask Applications.

The course provides practical exercises based on real-life examples giving you hands-on practice building your own models.

Applications of Machine Learning in Practical Real World

1. Machine Learning assists in formulating a diagnosis or recommending a treatment option. Many physicians try to discern patterns in symptoms using chatbots with speech recognition capabilities.

2. Google Maps uses machine learning in combination with various data sources to predict traffic. This includes aggregate location data, historical traffic patterns, local government data, and real-time feedback from users.

Who this Course is for:

• Students with a minimum high school knowledge in maths and passionate to learn Machine Learning.

• Those who know the basics of Machine Learning and classical algorithms like linear regression or logistic regression.

• Anyone who wishes to learn Machine Learning and apply it easily on datasets, even if they are not comfortable with coding.

• Students who wish to start a career in Data Science.

• Data analysts who wish to level up in Machine Learning.

• Anyone who wishes to become a Data Scientist.

• Anyone who wishes to use powerful Machine Learning tools.