Machine Learning Bootcamp™: Hand-On Python in Data Science
3.5 (16 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
2,923 students enrolled

Machine Learning Bootcamp™: Hand-On Python in Data Science

Learn Complete hands-on guide to implementing Supervised Machine Learning Algorithm in Python including ANN, CNN & RNN
3.5 (16 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
2,923 students enrolled
Created by Apex Education
Last updated 4/2020
English
English [Auto]
Current price: $13.99 Original price: $19.99 Discount: 30% off
23 hours left at this price!
30-Day Money-Back Guarantee
This course includes
  • 17.5 hours on-demand video
  • 1 article
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
Training 5 or more people?

Get your team access to 4,000+ top Udemy courses anytime, anywhere.

Try Udemy for Business
What you'll learn
  • Basics of Python (Introduction to Spyder & Jupyter Notebook)
  • Numpy (•Introduction to the Library •Nd-array Object •Data Types •Array Attributes •Indexing and Slicing •Array Manipulation)
  • Pandas (•Introduction to the Library •Series Data Structures •Pandas Data Frame •Pandas Basic Functionality • Crash Course – Data Visualization • Crash Course – ScikitLearn)
  • Tensorflow (•Introduction to the Library •Basic Syntax •Tensorflow Graphs •Variable Place Holders •Neural Network •Tensorboard)
  • Seaborn (•Distribution Plots •Categorical Plots •Regression Plots •Style and Color)
  • Plotly and Cufflinks
  • Regression (• Simple Linear Regression •Multiple Linear Regression •Polynomial Regression •Support Vector Regression • Decision Tree Regression • Random Forest Regression
  • Classification (•Logistic Regression •K-Nearest Neighbors • Support Vector Machine •Kernel SVM •Naïve Bayes •Decision Tree Classification •Random Forest)
  • Deep Learning (•Artificial Neural Networks •Convolutional Neural Networks •Recurrent Neural Networks)
Course content
Expand all 86 lectures 17:44:59
+ Numpy
12 lectures 02:19:35
Array Manipulation part(b)
13:04
Array Manipulation part(c)
14:29
Array Manipulation part(d)
12:35
Array Manipulation part(e)
14:11
Array Manipulation part(f)
17:44
+ Pandas
5 lectures 01:08:02
Introduction to Pandas
08:17
Series Data Structures (part a)
11:39
Series Data Structures (part b)
12:34
Pandas DataFrame
19:58
Pandas Basic Functionality
15:34
+ Data Visualization & ScikitLearn (Crash Course)
2 lectures 29:55
Data Visualization (Crash Course)
15:14
ScikitLearn (Crash Course)
14:41
+ Tensorflow
10 lectures 02:16:40
Introduction to Tensorflow
03:46
Tensorflow_Basic Syntax
19:53
Tensorflow_Graphs
11:50
Tensorflow_VariablesPlaceholders
22:30
Tensorflow_NeuralNetwork1
14:52
Tensorflow_NeuralNetwork2
13:16
Tensorflow_NeuralNetwork3
18:47
Tensorflow_Saving,Restoring Models
10:22
Tensorflow_Tensorboard-1
14:17
Tensorflow_Tensorboard-2
07:07
+ Seaborn
5 lectures 01:23:18
Seaborn_Distribution_Plots
26:18
Seaborn_Categorical_Plots
19:26
Seaborn_Categorical_Plots2
09:57
Seaborn_Regression_Plots
12:03
Seaborn_Style_Color
15:34
+ Plotly and Cufflinks
1 lecture 28:35
Covering the Plotly and cufflinks ML libraries
28:35
+ Supervised Machine Learning (Why Machine Learning?)
2 lectures 13:05
The Primary Concept
06:59
Algorithms of Supervised Learning
06:06
+ Regression - Simple Linear Regression
4 lectures 42:41
Simple_Linear_Regression_Part1
11:41
Simple_Linear_Regression_Part2
06:50
Simple_Linear_Regression_Part3
07:01
Simple_Linear_Regression_Part4
17:09
Requirements
  • Basic Knowledge of any programming language
  • Passion for learning
Description

This course focuses on one of the main branches of Machine Learning that is Supervised Learning in Python. If you are not familiar with Python, there is nothing to worry about because the Lectures comprising the Python Libraries will train you enough and will make you comfortable with the programming language.

The course is divided into two sections, in the first section, you will be having lectures about Python and the fundamental libraries like Numpy, Pandas, Seaborn, Scikit-Learn and Tensorflow that are necessary for one to be familiar with before putting his hands-on Supervised Machine Learning.

Then is the Supervised Learning part, which basically comprises three main chapters Regression, Classification, and Deep Learning, each chapter is thoroughly explained, both theoretically and experimentally.

During all of these lectures, we’ll be learning how to use the different machine learning algorithms to create some mind-blowing modules of Machine Learning, and at the end of the course, you’ll be trained enough that you would be able to develop you own Recognitions Systems and Prediction Models and many more.

Let's get started!

Who this course is for:
  • Those who are interested in AI and Machine Learning
  • Those who have basic knowledge of any programming language
  • Those who want to be create awesome Machine Learning and AI modules
  • And those who want to earn some handsome amount of money from Machine Learning Field in Future