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Machine Learning & Deep Learning : Fundamentals to Projects

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Machine Learning & Deep Learning : Fundamentals to Projects, available at $64.99, has an average rating of 4.05, with 307 lectures, based on 25 reviews, and has 227 subscribers.

You will learn about Theory, Maths and Implementation of machine learning and deep learning algorithms. Classification Models used in classical Machine Learning such as Logistic Regression, KNN, Support Vector Machines, Decision Trees, and Random Forest Build Artificial Neural Networks and use them for Regression and Classification Problems Using GPU with Neural Networks and Deep Learning Models. Convolutional Neural Networks Transfer Learning Recurrent Neural Networks and LSTM Time series forecasting and classification. Autoencoders Generative Adversarial Networks (GANs) Python from scratch Numpy, Matplotlib, Seaborn, Pandas, Pytorch, Scikit-learn and other python libraries. More than 80 projects solved with Machine Learning and Deep Learning models This course is ideal for individuals who are Students in Machine Learning and Deep Learning course or Beginners Who want to Learn Machine Learning and Deep Learning from Scratch or Researchers in Artificial Intelligence or Students and Researchers who want to develop Python Programming skills to solve Machine Learning and Deep Learning Tasks or Those who know Matlab and Other Programming Languages and want to switch to Python for Machine Learning and Deep Learning It is particularly useful for Students in Machine Learning and Deep Learning course or Beginners Who want to Learn Machine Learning and Deep Learning from Scratch or Researchers in Artificial Intelligence or Students and Researchers who want to develop Python Programming skills to solve Machine Learning and Deep Learning Tasks or Those who know Matlab and Other Programming Languages and want to switch to Python for Machine Learning and Deep Learning.

Enroll now: Machine Learning & Deep Learning : Fundamentals to Projects

Summary

Title: Machine Learning & Deep Learning : Fundamentals to Projects

Price: $64.99

Average Rating: 4.05

Number of Lectures: 307

Number of Published Lectures: 307

Number of Curriculum Items: 307

Number of Published Curriculum Objects: 307

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

  • Theory, Maths and Implementation of machine learning and deep learning algorithms.
  • Classification Models used in classical Machine Learning such as Logistic Regression, KNN, Support Vector Machines, Decision Trees, and Random Forest
  • Build Artificial Neural Networks and use them for Regression and Classification Problems
  • Using GPU with Neural Networks and Deep Learning Models.
  • Convolutional Neural Networks
  • Transfer Learning
  • Recurrent Neural Networks and LSTM
  • Time series forecasting and classification.
  • Autoencoders
  • Generative Adversarial Networks (GANs)
  • Python from scratch
  • Numpy, Matplotlib, Seaborn, Pandas, Pytorch, Scikit-learn and other python libraries.
  • More than 80 projects solved with Machine Learning and Deep Learning models

Who Should Attend

  • Students in Machine Learning and Deep Learning course
  • Beginners Who want to Learn Machine Learning and Deep Learning from Scratch
  • Researchers in Artificial Intelligence
  • Students and Researchers who want to develop Python Programming skills to solve Machine Learning and Deep Learning Tasks
  • Those who know Matlab and Other Programming Languages and want to switch to Python for Machine Learning and Deep Learning

Target Audiences

  • Students in Machine Learning and Deep Learning course
  • Beginners Who want to Learn Machine Learning and Deep Learning from Scratch
  • Researchers in Artificial Intelligence
  • Students and Researchers who want to develop Python Programming skills to solve Machine Learning and Deep Learning Tasks
  • Those who know Matlab and Other Programming Languages and want to switch to Python for Machine Learning and Deep Learning

Introduction

Introduction of the Course

Introduction to Machine Learning and Deep Learning

Introduction to Google Colab

Python Crash Course

Data Preprocessing

Supervised Machine Learning

Regression Analysis

Logistic Regression

K-Nearest Neighbor (KNN)

Bayes Theorem and Naive Bayes Classifier

Support Vector Machine (SVM)

Decision Trees

Random Forest

Boosting Methods in Machine Learning

Introduction to Neural Networks and Deep Learning

Activation Functions

Loss Functions

Back Propagation

Neural Networks for Regression Analysis

Neural Networks for Classification

Dropout Regularization and Batch Normalization

Convolutional Neural Network (CNN)

Recurrent Neural Network (RNN)

Autoencoders

Generative Adversarial Network (GAN)

Unsupervised Machine Learning

K-Means Clustering

Hierarchical Clustering

Density Based Spatial Clustering Of Applications With Noise (DBSCAN)

Gaussian Mixture Model (GMM) Clustering

Principal Component Analysis (PCA)

What you’ll learn

  • Theory, Maths and Implementation of machine learning and deep learning algorithms.

  • Regression Analysis.

  • Classification Models used in classical Machine Learning such as Logistic Regression, KNN, Support Vector Machines, Decision Trees, Random Forest, and Boosting Methods in Machine Learning.

  • Build Artificial Neural Networks and use them for Regression and Classification Problems.

  • Using GPU with Deep Learning Models.

  • Convolutional Neural Networks

  • Transfer Learning

  • Recurrent Neural Networks

  • Time series forecasting and classification.

  • Autoencoders

  • Generative Adversarial Networks

  • Python from scratch

  • Numpy, Matplotlib, seaborn, Pandas, Pytorch, scikit-learn and other python libraries.

  • More than 80 projects solved with Machine Learning and Deep Learning models.

Course Curriculum

Chapter 1: Introduction and Course Material

Lecture 1: Introduction of the course

Lecture 2: Course Material

Lecture 3: How to succeed in this course

Chapter 2: Introduction to Machine Learning and Deep Learning

Lecture 1: Introduction of the Section

Lecture 2: What in Intelligence?

Lecture 3: Machine Learning

Lecture 4: Supervised Machine Learning

Lecture 5: Unsupervised Machine Learning

Lecture 6: Deep Learning

Chapter 3: Introduction to Google Colab

Lecture 1: Introduction of the Section

Lecture 2: Importing Dataset in Google Colab

Lecture 3: Importing and Displaying Image in Google Colab

Lecture 4: Importing more datasets

Lecture 5: Uploading Course Material on your Google Drive

Chapter 4: Python Crash Course

Lecture 1: Introduction of the Section

Lecture 2: Arithmetic With Python

Lecture 3: Comparison and Logical Operations

Lecture 4: Conditional Statements

Lecture 5: Dealing With Numpy Arrays-Part01

Lecture 6: Dealing With Numpy Arrays-Part02

Lecture 7: Dealing With Numpy Arrays-Part03

Lecture 8: Plotting and Visualization-Part01

Lecture 9: Plotting and Visualization-Part02

Lecture 10: Plotting and Visualization-Part03

Lecture 11: Plotting and Visualization-Part04

Lecture 12: Lists in Python

Lecture 13: For Loops-Part01

Lecture 14: For Loops-Part02

Lecture 15: Strings

Lecture 16: Print Formatting With Strings

Lecture 17: Dictionaries-Part01

Lecture 18: Dictionaries-Part02

Lecture 19: Functions in Python-Part01

Lecture 20: Functions in Python-Part02

Lecture 21: Pandas-Part01

Lecture 22: Pandas-Part02

Lecture 23: Pandas-Part03

Lecture 24: Pandas-Part04

Lecture 25: Seaborn-Part01

Lecture 26: Seaborn-Part02

Lecture 27: Seaborn-Part03

Lecture 28: Tuples

Lecture 29: Classes in Python

Chapter 5: Data Preprocessing

Lecture 1: Introduction of the Section

Lecture 2: Need of Data Preprocessing

Lecture 3: Data Normalization and Min-Max Scaling

Lecture 4: Project01-Data Normalization and Min-Max Scaling-Part01

Lecture 5: Project01-Data Normalization and Min-Max Scaling-Part02

Lecture 6: Data Standardization

Lecture 7: Project02-Data Standardization

Lecture 8: Project03-Dealing With Missing Values

Lecture 9: Project04-Dealing With Categorical Features

Lecture 10: Project05-Feature Engineering

Lecture 11: Project06-Feature Engineering by Window Method

Chapter 6: Supervised Machine Learning

Lecture 1: Supervised Machine Learning

Chapter 7: Regression Analysis

Lecture 1: Introduction of the Section

Lecture 2: Origin of the Regression

Lecture 3: Definition of Regression

Lecture 4: Requirement from Regression

Lecture 5: Simple Linear Regression

Lecture 6: Multiple Linear Regression

Lecture 7: Target and Predicted Values

Lecture 8: Loss Function

Lecture 9: Regression With Least Square Method

Lecture 10: Least Square Method With Numerical Example

Lecture 11: Evaluation Metrics for Regression

Lecture 12: Project01-Simple Regression-Part01

Lecture 13: Project01-Simple Regression-Part02

Lecture 14: Project01-Simple Regression-Part03

Lecture 15: Project02-Multiple Regression-Part01

Lecture 16: Project02-Multiple Regression-Part02

Lecture 17: Project02-Multiple Regression-Part03

Lecture 18: Project03-Another Multiple Regression

Lecture 19: Regression by Gradient Descent

Lecture 20: Project04-Simple Regression With Gradient Descent

Lecture 21: Project05-Multiple Regression With Gradient Descent

Lecture 22: Polynomial Regression

Lecture 23: Project06-Polynomial Regression

Lecture 24: Cross-validation

Lecture 25: Project07-Cross-validation

Lecture 26: Underfitting and Overfitting ( Bias-Variance Tradeoff )

Lecture 27: Concept of Regularization

Lecture 28: Ridge Regression OR L2 Regularization

Lecture 29: Lasso Regression OR L1 Regularization

Lecture 30: Comparing Ridge and Lasso Regression

Lecture 31: Elastic Net Regularization

Lecture 32: Project08-Regularizations

Lecture 33: Grid search Cross-validation

Lecture 34: Project09-Grid Search Cross-validation

Chapter 8: Logistic Regression

Lecture 1: Introduction of the Section

Lecture 2: Fundamentals of Logistic Regression

Lecture 3: Limitations of Regression Models

Instructors

  • Zeeshan Ahmad
    Zeeshan Ahmad
    Machine Learning and Statistical Signal Processing

Rating Distribution

  • 1 stars: 2 votes
  • 2 stars: 2 votes
  • 3 stars: 3 votes
  • 4 stars: 2 votes
  • 5 stars: 16 votes

Frequently Asked Questions


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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!

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