Python and R for Machine Learning Deep Learning

Python and R for Machine Learning & Deep Learning, available at $54.99, has an average rating of 5, with 252 lectures, based on 4 reviews, and has 22 subscribers.

You will learn about Basics to advanced Python programming Data manipulation with Pandas Visualization with Matplotlib and Seaborn Fundamentals of R Statistical modeling in R Introduction to neural networks Building models with TensorFlow and Keras Convolutional and Recurrent Neural Networks Comprehensive understanding of machine learning and deep learning This course is ideal for individuals who are IT Professionals: Broaden your career prospects by transitioning into the field of data science or Students: Whether you’re an undergraduate or a postgraduate student, this course provides a robust framework for understanding machine learning and deep learning concepts or Career Changers: Looking to pivot into a rapidly growing field with immense opportunities? This course will provide you with the necessary skills and knowledge to make a successful transition into data science and machine learning. or Entrepreneurs and Business Owners: Leverage the power of machine learning and deep learning to drive business innovation and efficiency. Understand how to implement data-driven strategies to improve decision-making and gain a competitive edge. or Anyone Interested in Data Science: If you have a passion for data and a desire to learn how to extract valuable insights from it, this course is for you. Gain a comprehensive understanding of machine learning and deep learning, regardless of your current level of expertise. It is particularly useful for IT Professionals: Broaden your career prospects by transitioning into the field of data science or Students: Whether you’re an undergraduate or a postgraduate student, this course provides a robust framework for understanding machine learning and deep learning concepts or Career Changers: Looking to pivot into a rapidly growing field with immense opportunities? This course will provide you with the necessary skills and knowledge to make a successful transition into data science and machine learning. or Entrepreneurs and Business Owners: Leverage the power of machine learning and deep learning to drive business innovation and efficiency. Understand how to implement data-driven strategies to improve decision-making and gain a competitive edge. or Anyone Interested in Data Science: If you have a passion for data and a desire to learn how to extract valuable insights from it, this course is for you. Gain a comprehensive understanding of machine learning and deep learning, regardless of your current level of expertise.

Enroll now: Python and R for Machine Learning & Deep Learning

Summary

Title: Python and R for Machine Learning & Deep Learning

Price: $54.99

Average Rating: 5

Number of Lectures: 252

Number of Published Lectures: 252

Number of Curriculum Items: 252

Number of Published Curriculum Objects: 252

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Basics to advanced Python programming

Data manipulation with Pandas

Visualization with Matplotlib and Seaborn

Fundamentals of R

Statistical modeling in R

Introduction to neural networks

Building models with TensorFlow and Keras

Convolutional and Recurrent Neural Networks

Comprehensive understanding of machine learning and deep learning

Who Should Attend

IT Professionals: Broaden your career prospects by transitioning into the field of data science

Students: Whether you’re an undergraduate or a postgraduate student, this course provides a robust framework for understanding machine learning and deep learning concepts

Career Changers: Looking to pivot into a rapidly growing field with immense opportunities? This course will provide you with the necessary skills and knowledge to make a successful transition into data science and machine learning.

Entrepreneurs and Business Owners: Leverage the power of machine learning and deep learning to drive business innovation and efficiency. Understand how to implement data-driven strategies to improve decision-making and gain a competitive edge.

Anyone Interested in Data Science: If you have a passion for data and a desire to learn how to extract valuable insights from it, this course is for you. Gain a comprehensive understanding of machine learning and deep learning, regardless of your current level of expertise.

Target Audiences

IT Professionals: Broaden your career prospects by transitioning into the field of data science

Students: Whether you’re an undergraduate or a postgraduate student, this course provides a robust framework for understanding machine learning and deep learning concepts

Career Changers: Looking to pivot into a rapidly growing field with immense opportunities? This course will provide you with the necessary skills and knowledge to make a successful transition into data science and machine learning.

Entrepreneurs and Business Owners: Leverage the power of machine learning and deep learning to drive business innovation and efficiency. Understand how to implement data-driven strategies to improve decision-making and gain a competitive edge.

Anyone Interested in Data Science: If you have a passion for data and a desire to learn how to extract valuable insights from it, this course is for you. Gain a comprehensive understanding of machine learning and deep learning, regardless of your current level of expertise.

Welcome to the gateway to your journey into Python for Machine Learning & Deep Learning!

Unlock the power of Python and delve into the realms of Machine Learning and Deep Learning with our comprehensive course. Whether you’re a beginner eager to step into the world of artificial intelligence or a seasoned professional looking to enhance your skills, this course is designed to cater to all levels of expertise.

What sets this course apart?

1. Comprehensive Curriculum: Our meticulously crafted curriculum covers all the essential concepts of Python programming, machine learning algorithms, and deep learning architectures. From the basics to advanced techniques, we’ve got you covered.

2. Hands-On Projects: Theory is important, but practical experience is paramount. Dive into real-world projects that challenge you to apply what you’ve learned and reinforce your understanding.

3. Expert Guidance: Learn from industry expert who has years of experience in the field. Benefit from his insights, tips, and best practices to accelerate your learning journey.

4. Interactive Learning: Engage in interactive lessons, quizzes, and exercises designed to keep you motivated and actively involved throughout the course.

5. Flexibility: Life is busy, and we understand that. Our course offers flexible scheduling options, allowing you to learn at your own pace and convenience.

6. Career Opportunities: Machine Learning and Deep Learning are in high demand across various industries. By mastering these skills, you’ll open doors to exciting career opportunities and potentially higher earning potential.

Are you ready to embark on an exhilarating journey into the world of Python for Machine Learning & Deep Learning? Enroll now and take the first step towards becoming a proficient AI practitioner!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Overview

Chapter 2: Python & Jupyter Notebook – Essentials

Lecture 1: Installing Python & Anaconda

Lecture 2: Jupyter Overview

Lecture 3: Python Basics

Lecture 4: Python Basics 2

Lecture 5: Python Basics 3

Lecture 6: Numpy

Lecture 7: Pandas

Lecture 8: Seaborn

Chapter 3: R Studio & R Crash

Lecture 1: Installing R & Studio

Lecture 2: R & R Studio – Basics

Lecture 3: Packages in R

Lecture 4: Inbuilt datasets of R

Lecture 5: Manual data entry

Lecture 6: Importing from CSV or Text files

Lecture 7: Barplots

Lecture 8: Histograms

Chapter 4: Statistics – Basics

Lecture 1: Types of Data

Lecture 2: Types of Statistics

Lecture 3: Describing data Graphically

Lecture 4: Measures of Centers

Lecture 5: Measures of Dispersion

Chapter 5: Machine Learning

Lecture 1: Introduction to Machine Learning

Lecture 2: Building a Machine Learning Model

Lecture 3: Gathering Business Knowledge

Lecture 4: Data Exploration

Lecture 5: Dataset & Data Dictionary

Lecture 6: Importing Data in Python

Lecture 7: Importing the dataset into R

Lecture 8: Univariate analysis and EDD

Lecture 9: EDD in Python

Lecture 10: EDD in R

Lecture 11: Outlier Treatment

Lecture 12: Outlier Treatment in Python

Lecture 13: Outlier Treatment in R

Lecture 14: Missing Value Imputation

Lecture 15: Missing Value Imputation in Python

Lecture 16: Missing Value imputation in R

Lecture 17: Seasonality in Data

Lecture 18: Bi-variate analysis and Variable transformation

Lecture 19: Variable transformation and deletion in Python

Lecture 20: Variable transformation in R

Lecture 21: Non-usable variables

Lecture 22: Dummy variable creation: Handling qualitative data

Lecture 23: Dummy variable creation in Python

Lecture 24: Dummy variable creation in R

Lecture 25: Correlation Analysis

Lecture 26: Correlation Analysis in Python

Lecture 27: Correlation Matrix in R

Lecture 28: The Problem Statement

Lecture 29: Basic Equations and Ordinary Least Squares (OLS) method

Lecture 30: Assessing accuracy of predicted coefficients

Lecture 31: Assessing Model Accuracy: RSE and R squared

Lecture 32: Simple Linear Regression in Python

Lecture 33: Simple Linear Regression in R

Lecture 34: Multiple Linear Regression

Lecture 35: The F – statistic

Lecture 36: Interpreting results of Categorical variables

Lecture 37: Multiple Linear Regression in Python

Lecture 38: Multiple Linear Regression in R

Lecture 39: Test-train split

Lecture 40: Bias Variance trade-off

Lecture 41: Test train split in Python

Lecture 42: Test-Train Split in R

Lecture 43: Regression models other than OLS

Lecture 44: Subset selection techniques

Lecture 45: SubShrinkage methods: Ridge and Lassoset selection in R

Lecture 46: Ridge regression and Lasso in Python

Lecture 47: Heteroscedasticity

Lecture 48: Ridge Regression and Lasso in R

Lecture 49: importing the data into Python

Lecture 50: Importing the data into R

Lecture 51: Three Classifiers and the Problem statement

Lecture 52: Why cant we use Linear Regression?

Lecture 53: Logistic Regression

Lecture 54: Training a Simple Logistic Model in Python

Lecture 55: Training a Simple Logistic model in R

Lecture 56: Result of Simple Logistic Regression

Lecture 57: Logistic with multiple predictors

Lecture 58: Training multiple predictor Logistic model in Python

Lecture 59: Training multiple predictor Logistic model in R

Lecture 60: Confusion Matrix

Lecture 61: Creating Confusion Matrix in Python

Lecture 62: Evaluating performance of model

Lecture 63: Evaluating model performance in Python

Lecture 64: Predicting probabilities, assigning classes and making Confusion Matrix in R

Lecture 65: Linear Discriminant Analysis

Lecture 66: LDA in Python

Lecture 67: Linear Discriminant Analysis in R

Lecture 68: Test-Train Split

Lecture 69: Test-Train Split in Python

Lecture 70: Test-Train Split in R

Lecture 71: K-Nearest Neighbors classifier

Lecture 72: K-Nearest Neighbors in Python: Part 1

Lecture 73: K-Nearest Neighbors in Python: Part 2

Instructors

Manuel Ernesto Cambota
Analista Programador de Sistemas

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