Machine Learning Bootcam: Hand-On Python in Data Science

Machine Learning Bootcam: Hand-On Python in Data Science, available at $19.99, has an average rating of 3.5, with 86 lectures, based on 21 reviews, and has 2937 subscribers.

You will learn about 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 & 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 & 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) This course is ideal for individuals who are Those who are interested in AI and Machine Learning or Those who have basic knowledge of any programming language or Those who want to be create awesome Machine Learning and AI modules or And those who want to earn some handsome amount of money from Machine Learning Field in Future It is particularly useful for Those who are interested in AI and Machine Learning or Those who have basic knowledge of any programming language or Those who want to be create awesome Machine Learning and AI modules or And those who want to earn some handsome amount of money from Machine Learning Field in Future.

Enroll now: Machine Learning Bootcam: Hand-On Python in Data Science

Summary

Title: Machine Learning Bootcam: Hand-On Python in Data Science

Price: $19.99

Average Rating: 3.5

Number of Lectures: 86

Number of Published Lectures: 86

Number of Curriculum Items: 86

Number of Published Curriculum Objects: 86

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will 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 & 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 & 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)

Who Should Attend

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

Target Audiences

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

This comprehensive course delves into the essential realm of Supervised Learning in Python, a pivotal branch of Machine Learning. Whether you are a Python novice or an experienced programmer, fear not, as the initial lectures devoted to Python and its integral libraries, including Numpy, Pandas, Seaborn, Scikit-Learn, and Tensorflow, are designed to equip you with the necessary skills and familiarity with the programming language.

The course is thoughtfully structured into two distinct sections. The first section focuses on Python basics and fundamental libraries, providing a solid foundation crucial for delving into the intricacies of Supervised Machine Learning. It serves as a preparatory phase, ensuring participants are well-versed in the tools required for effective engagement with the subsequent material.

The second section delves into the core of Supervised Learning, spanning three main chapters: Regression, Classification, and Deep Learning. Each chapter is meticulously dissected, offering a dual approach of theoretical understanding and hands-on experimentation. This method not only enhances conceptual comprehension but also ensures practical proficiency in implementing algorithms.

Throughout the course, emphasis is placed on the practical application of various machine learning algorithms. Participants will learn to harness these algorithms to construct impressive modules of Machine Learning. By the course’s culmination, you will have acquired the expertise to independently develop Recognition Systems, Prediction Models, and various other applications.

Embark on this learning journey, and by the course’s conclusion, you will be well-equipped to tackle real-world challenges using Supervised Learning techniques in Python. Let’s get started on this exciting exploration of the world of machine learning!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: The General Concept of Data Science

Lecture 3: Introduction to Supervised Machine Learning

Lecture 4: Basics of Python

Chapter 2: Environment Set up

Lecture 1: 00-Environmental Setup

Lecture 2: 01-Spyder and Jupyter Overview

Chapter 3: Numpy

Lecture 1: Introduction to Numpy

Lecture 2: Ndarray Object

Lecture 3: Data Types

Lecture 4: Array Attributes

Lecture 5: Array from Numerical Ranges

Lecture 6: Indexing & Slicing

Lecture 7: Array Manipulation part(a)

Lecture 8: Array Manipulation part(b)

Lecture 9: Array Manipulation part(c)

Lecture 10: Array Manipulation part(d)

Lecture 11: Array Manipulation part(e)

Lecture 12: Array Manipulation part(f)

Chapter 4: Pandas

Lecture 1: Introduction to Pandas

Lecture 2: Series Data Structures (part a)

Lecture 3: Series Data Structures (part b)

Lecture 4: Pandas DataFrame

Lecture 5: Pandas Basic Functionality

Chapter 5: Data Visualization & ScikitLearn (Crash Course)

Lecture 1: Data Visualization (Crash Course)

Lecture 2: ScikitLearn (Crash Course)

Chapter 6: Tensorflow

Lecture 1: Introduction to Tensorflow

Lecture 2: Tensorflow_Basic Syntax

Lecture 3: Tensorflow_Graphs

Lecture 4: Tensorflow_VariablesPlaceholders

Lecture 5: Tensorflow_NeuralNetwork1

Lecture 6: Tensorflow_NeuralNetwork2

Lecture 7: Tensorflow_NeuralNetwork3

Lecture 8: Tensorflow_Saving,Restoring Models

Lecture 9: Tensorflow_Tensorboard-1

Lecture 10: Tensorflow_Tensorboard-2

Chapter 7: Seaborn

Lecture 1: Seaborn_Distribution_Plots

Lecture 2: Seaborn_Categorical_Plots

Lecture 3: Seaborn_Categorical_Plots2

Lecture 4: Seaborn_Regression_Plots

Lecture 5: Seaborn_Style_Color

Chapter 8: Plotly and Cufflinks

Lecture 1: Covering the Plotly and cufflinks ML libraries

Chapter 9: Supervised Machine Learning (Why Machine Learning?)

Lecture 1: The Primary Concept

Lecture 2: Algorithms of Supervised Learning

Chapter 10: Regression – Simple Linear Regression

Lecture 1: Simple_Linear_Regression_Part1

Lecture 2: Simple_Linear_Regression_Part2

Lecture 3: Simple_Linear_Regression_Part3

Lecture 4: Simple_Linear_Regression_Part4

Chapter 11: Regression – Multiple Linear Regression

Lecture 1: Multiple Linear Regression_Part1

Lecture 2: Multiple Linear Regression_Part2

Lecture 3: Multiple Linear Regression_Part3

Chapter 12: Regression – Polynomial Regression

Lecture 1: Polynomial Regression_Part1

Lecture 2: Polynomial Regression_Part2

Lecture 3: Polynomial Regression_Part3a

Lecture 4: Polynomial Regression_Part3b

Lecture 5: Polynomial Regression_Part4

Chapter 13: Artificial Neural Network

Lecture 1: Plan of Attack

Lecture 2: What is Neuron?

Lecture 3: The Activation Function

Lecture 4: Working of Neural Networks?

Lecture 5: Learning of Neural Networks

Lecture 6: Gradient Descent

Lecture 7: 06-Stochastic Gradient Descent

Lecture 8: 07-Backpropagation

Lecture 9: The Problem Statement

Lecture 10: Importance of Balanced Dataset

Lecture 11: Preprocessing (1)

Lecture 12: Preprocessing (2)

Lecture 13: Preprocessing (3)

Lecture 14: Defining the Model

Lecture 15: Training the Model

Lecture 16: Evaluating the Model

Lecture 17: Homework for ANN’s

Lecture 18: Solution for ANN’s

Chapter 14: Convolutional Neural Networks

Lecture 1: Plan of Attack

Lecture 2: Building_CNN_introduction

Lecture 3: Building_CNN_Parameters

Lecture 4: Building_CNN_Convolution 2D

Lecture 5: Building_CNN_PoolActFlat

Lecture 6: Building_CNN_FcLayer

Lecture 7: Building_CNN_BuildModelCNN

Lecture 8: Building_CNN_CostFn and Optimizer

Lecture 9: Building_CNN_Tensorflow Session

Lecture 10: Building_CNN_Prediction

Chapter 15: Recurrent Neural Networks

Lecture 1: Building_RNN_Initializatino

Lecture 2: Building_RNN_Model

Instructors

Apex Education
Quality Training & Resources – A Step Ahead

Rating Distribution

1 stars: 4 votes
2 stars: 0 votes
3 stars: 4 votes
4 stars: 4 votes
5 stars: 9 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

Menu