Data Science Road Map With 5+ Industry level Projects

Learn Data Science through a comprehensive course curriculum encompassing essential topics like statistics etc.

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Last Updated: 2023-03-20 05:03:08 English Course Type: Self Learning

Learn the concepts of Python,Machine learning, Deep Learning,Time series. Implement Real World Projects with Proof Of Concept
This course consists of 25+ hours video content and Downloadable files for all videos
Data Scientists need to have a solid grasp of ML

Interested in the field of Machine Learning? Then this course is for you!

This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way.

A Road map connecting many of the most important concepts in machine learning, how to learn them and what tools to use to perform them.

Below are few Applications of Machine Learning in Practical Real World

Machine learning can help with the diagnosis of diseases. Many physicians use chat bot with speech recognition capabilities to discern patterns in symptoms. Real-world examples for medical diagnosis: Assisting in formulating a diagnosis or recommending a treatment option.
Google Maps uses machine learning in combination with various data sources including aggregate location data, historical traffic patterns, local government data, and real-time feedback from users, to predict traffic.

Python leads the pack, with 57% of data scientists and machine learning developers using it and 33% Prioritising it for development. So, In this course also you will able learn Basics of Python to Advance State of the Art Techniques of Deep Learning Models.

There are 4 different sections in this course for complete understanding of all the concepts in Artificial Intelligence such as Python, Machine Learning, Deep Learning, Time Series Analysis.
This course is fun and exciting, but at the same time, we dive deep into Machine Learning. It is structured 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 Application.

Moreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models.

Who this course is for:

Anyone interested in Machine Learning.

Students who have at least high school knowledge in maths and who want to start learning Machine Learning.

Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.

Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.

Any students in college who want to start a career in Data Science.

Any data analysts who want to level up in Machine Learning.

Any people who are not satisfied with their job and who want to become a Data Scientist.

Any people who want to create added value to their business by using powerful Machine Learning tools.

I hope you will Enjoy this course. I will see you in the course.

There is no specific prerequisite to learn machine learning. But you need to be from engineering/science/Maths/Stats background to understand the theory and the techniques used. You need to be good in mathematics. If you are not, still you can machine learning, but you will face difficulty when solving complex real world problems. Many say you need to know Linear algebra, Calculus etc. etc. but I never learnt it, yet I am able to work on machine learning.
No prior experience is required. We will start from the very basics

Beginner into Machine Learning
Career transition from Non Technical into Data Science
Fresher to get job into Machine Learning Engineer
Beginner into Python
Fresher to get job into Machine Learning Engineer

Section 1 : Python - Data Structures (Lists, Tuple, Dictionary) and String Manipulations 3 Lectures 02:27:16
Lecture 1 :
Python - Data Structures (Lists, Tuple, Dictionary) and String Manipulations Preview
Lecture 2 :
Python - Implementation Of Lambda, Recursion, Functions.
Lecture 3 :
Python - Understand Of Libraries,Exploratory Data Analysis,Descriptive Analysis
Section 2 : MACHINE LEARNING - Introduction to Basics of ML for Beginners 8 Lectures 07:32:17
Lecture 1 :
MACHINE LEARNING - Classical Machine Learning Algorithm Logistic Regression
Lecture 2 :
MACHINE LEARNING - Word Embedding Techniques BoW, TF -IDF, W2V etc
Lecture 3 :
MACHINE LEARNING - Text Cleaning and Preprocessing with Amazon Reviews Data
Lecture 4 :
MACHINE LEARNING - Classical Machine Learning Algorithm Linear Regression
Lecture 5 :
MACHINE LEARNING - Decision Tree Classifier and Regression with Example
Lecture 6 :
MACHINE LEARNING - Geometric Intuition of Ensembles Models and Flask Project
Lecture 7 :
MACHINE LEARNING - Data Analysis on Loan Approval Status
Lecture 8 :
MACHINE LEARNING - Unsupervised Learning Algorithms K means Cluster Techniques
Section 3 : DEEP LEARNING - Introduction to Basics of DL for Beginners 6 Lectures 04:41:11
Lecture 1 :
DEEP LEARNING - Introduction to Neural Networks and Basics of MLP, BACKPROP
Lecture 2 :
DEEP LEARNING - In Depth Understanding of RNN and LSTM with Examples
Lecture 3 :
DEEP LEARNING - Intuition Behind the Computer Vision and CNN Algorithm
Lecture 4 :
DEEP LEARNING - Convolutional Neural Networks with Pizza and CIFAR Projects
Lecture 5 :
DEEP LEARNING - Practical Examples on Transfer Learning for Vgg16 Model
Lecture 6 :
DEEP LEARNING - Web Based Flask Framework for Wild Animal Recognition with CNN

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