Data Science: Machine Learning and Deep Learning with Python

Data Science: Machine Learning and Deep Learning with Python, available at $44.99, has an average rating of 4, with 60 lectures, based on 80 reviews, and has 3286 subscribers.

You will learn about From beginner level to advanced level understanding of : Data Science:(Online Data Parsing, Data visualization, Data Preprocessing, Preparing data for machine learning) Machine Learning:(Supervised Machine Learning, Unsupervised Machine Learning, Implementation of algorithms form scratch, Built-in algorithms usages.) amitDeep Learning:(Tensorflow, Hyperparameter tunings) Working with some data sets which are benchmarks in industry like : Titanic, Seeds, Rock and Mine This course is ideal for individuals who are Those who are interested in Artificial Intelligence or Those who have basic level of understanding of english or Those who have basic knowledge of any programming language or Those who have basic knowledge of OOP or hose who wants to write programs for predictions or Those who are interested in making automated computer programs or Those who wants to unlock the future of IT that is AI It is particularly useful for Those who are interested in Artificial Intelligence or Those who have basic level of understanding of english or Those who have basic knowledge of any programming language or Those who have basic knowledge of OOP or hose who wants to write programs for predictions or Those who are interested in making automated computer programs or Those who wants to unlock the future of IT that is AI.

Enroll now: Data Science: Machine Learning and Deep Learning with Python

Summary

Title: Data Science: Machine Learning and Deep Learning with Python

Price: $44.99

Average Rating: 4

Number of Lectures: 60

Number of Published Lectures: 60

Number of Curriculum Items: 60

Number of Published Curriculum Objects: 60

Original Price: $39.99

Quality Status: approved

Status: Live

What You Will Learn

From beginner level to advanced level understanding of :
Data Science:(Online Data Parsing, Data visualization, Data Preprocessing, Preparing data for machine learning)
Machine Learning:(Supervised Machine Learning, Unsupervised Machine Learning, Implementation of algorithms form scratch, Built-in algorithms usages.)
amitDeep Learning:(Tensorflow, Hyperparameter tunings)
Working with some data sets which are benchmarks in industry like : Titanic, Seeds, Rock and Mine

Who Should Attend

Those who are interested in Artificial Intelligence
Those who have basic level of understanding of english
Those who have basic knowledge of any programming language
Those who have basic knowledge of OOP
hose who wants to write programs for predictions
Those who are interested in making automated computer programs
Those who wants to unlock the future of IT that is AI

Target Audiences

Those who are interested in Artificial Intelligence
Those who have basic level of understanding of english
Those who have basic knowledge of any programming language
Those who have basic knowledge of OOP
hose who wants to write programs for predictions
Those who are interested in making automated computer programs
Those who wants to unlock the future of IT that is AI

This course focuses on the fundamentals of Data Science, Machine learning, and deep learning in the beginning and with the passage of time, the content and lectures become advanced and more practical. But before everything, the introduction of python is discussed. Python is one of the fastest-growing programming languages and if we specifically look from the perspective of Data Science, Machine learning and deep learning,there is no other choice then “python” as a programming language.

First of all, there is a crash course on python for those who are not very good with python and then there is an exercise for python that is supposed to be solved by you but if you feel any difficulty in solving the exercise, the solution is also provided.

Then we moved on towards the Data Science and we start from data parsing using Scrapythen the data visualizations by using several libraries of python and finally we end up learning different data preprocessing techniques. And in the end, there is a complete project that we’ll do together.

After that, we’ll be learning a few classical and a few advanced machine learning algorithms. Some of them will be implemented from scratch and the others will be implemented by using the builtin libraries of python. At the end of every algorithm, there will be a mini-project.

Finally, Deep learning will be discussed, the basic structure of an artificial neural network and it’s the implementation in TensorFlowfollowed by a complete deep learning-based project. And in the end, some hyperparameter tuning techniques will be discussed that’ll improve the performance of the model.

About The Instructor:

Below is an introduction to Mr. Sajjad Mustafa, the instructor of this course.

He an expert in Web Programming, Data Science, and Machine Learning. He has been working on different topics including the above-mentioned ones for almost 3 years and has been teaching on these projects for more than a year. He has attained mastery over understanding the requirements and making a way to the most unique and proper solutions to the given task.

He is well acquainted with and has deep knowledge of Python, Ruby, JavaScript. Django, ReactJS, React Native, JQuery, HTML, CSS, Bootstrap, C, C++, SQL (MySQL, mySQLite) are also my passion and interest.

He is passionate about new technologies and likes to have a good professional connection. Let’s meet with him on the course.

Course Curriculum

Chapter 1: Introduction and Overview

Lecture 1: problem statement

Lecture 2: Solution to a Problem

Lecture 3: An overview

Chapter 2: Python

Lecture 1: Intro to python

Lecture 2: Python crash course(1)

Lecture 3: Python crash course(2)

Lecture 4: Python crash course(3)

Lecture 5: Python crash course(4)

Lecture 6: Python Quiz Solution

Chapter 3: Data Science

Lecture 1: Intro to datascience

Lecture 2: Types of data in DS

Chapter 4: Data Parsing

Lecture 1: Introduction to Scrapy

Lecture 2: Spider to convert one quote into structured data

Lecture 3: Spider to convert the whole page into structured data

Lecture 4: Spider to scrape the paginations(1)

Lecture 5: Spider to scrape the paginations(2)

Lecture 6: Spider to scrape scrolling pages(1)

Lecture 7: Spider to scrape scrolling pages(2)

Lecture 8: Spider to scrape data by submitting form

Chapter 5: Libraries to deal with data

Lecture 1: Numpy(1)

Lecture 2: Numpy(2)

Lecture 3: Pandas(1)

Lecture 4: Pandas(2)

Chapter 6: Data Visualizations

Lecture 1: Matplotlib

Lecture 2: Seaborn(1)

Lecture 3: Seaborn(2)

Lecture 4: Plotly

Chapter 7: Data Prepocessing

Lecture 1: Missing Values(1)

Lecture 2: Missing Values(2)

Lecture 3: Outlier removal

Lecture 4: Data Normalization

Lecture 5: Encoding(1)

Lecture 6: Encoding(2)

Chapter 8: Data Science Project

Lecture 1: Data Science Project(1)

Lecture 2: Data Science Project(2)

Lecture 3: Data Science Project(3)

Chapter 9: Machine Learning

Lecture 1: Intro to ML(1)

Lecture 2: Intro to ML(2)

Chapter 10: Linear Regression

Lecture 1: Linear regression(theory)

Lecture 2: Linear regression (implementation – 1)

Lecture 3: Linear regression (implementation – 2)

Lecture 4: Gradient Decent (1)

Lecture 5: Gradient Decent (2)

Chapter 11: Sklearn

Lecture 1: Logistic regression

Lecture 2: SVM

Chapter 12: K Nearest Neighbors

Lecture 1: KNN(theory)

Lecture 2: KNN(implementation-1)

Lecture 3: KNN(implementation-2)

Lecture 4: KNN(sklearn-implementation)

Chapter 13: Unsupervised Machine Learning

Lecture 1: Unsupervised-Kmean

Lecture 2: Kmean(implementation)

Lecture 3: Kmean(sklearn-implementation)

Chapter 14: Deep Learning

Lecture 1: Introduction to Deep Learning(1)

Lecture 2: Introduction to Deep Learning(2)

Chapter 15: Tensorflow

Lecture 1: TensorFlow(dataset reading and preprocessing)

Lecture 2: TensorFlow(setting up layers and nodes)

Lecture 3: TensorFlow(setting up weights and biases)

Chapter 16: Tensorflow Project

Lecture 1: TensorFlow(implementation completed)

Lecture 2: TensorFlow(visualization of mse and accuracy)

Chapter 17: Parameter Tuning

Lecture 1: TensorFlow(parameter tuning)

Instructors

Teach Premium
Learning for All

Rating Distribution

1 stars: 3 votes
2 stars: 6 votes
3 stars: 14 votes
4 stars: 30 votes
5 stars: 27 votes

Frequently Asked Questions

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