Machine Learning & Deep Learning Projects For Beginners 2023

Machine Learning & Deep Learning Projects For Beginners 2023
Published 11/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 7.82 GB | Duration: 18h 45m

Work On 23 Projects in Machine Learning & Deep Learning, Regression, Classification, Clustering, ANN, CNN, RNN, & More!

What you'll learn

Complete understanding of Machine Learning Project flow

Machine Learning and Deep Learning Practical Applications

Machine Learning for Regression Problems

Machine Learning for Classification Problems

Clustering with Machine Learning

NLP with Machine Learning

Improving the performance of a machine learning model

Projects on Artificial Neural Network (ANN)

Projects on Convolutional Neural Network (CNN)

Projects on Recurrent Neural Network (RNN)

Projects on Transfer Learning

Requirements

Python Programming Basics

Basic understanding of Machine Learning and Deep Learning

Description

Hello Data Lover,Welcome to this course, 'Machine Learning & Deep Learning Projects for Beginners 2023'.In this course, I will teach you to work on different 23 projects, which are from various categories like Regression, Classification, Clustering, ANN, CNN, RNN, and Transfer Learning!Artificial Intelligence and Machine Learning are growing exponentially in today's world. There is multiple application of AI and Deep Learning like Self Driving Cars, Chatbots, Image Recognition, Virtual Assistance, ALEXA, and so on.With this course, you will understand the complexities of Machine Learning and Deep Learning in an easy way, as we will be working with Google colab Notebook.In Google Colab you can start the coding with Zero Installation, whether you're an expert or a beginner, in this course you will learn an end-to-end implementation of Machine Learning and Deep Learning Algorithms.List of the Projects that you will work on,Project 1: Breast Cancer DetectionProject 2: Customer churn rate predictionProject 3: Medical insurance premium predictionProject 4: House price predictionProject 5: E signing of customers based on financial dataProject 6: Credit card fraud detectionProject 7: Employee Attrition PredictionProject 8: Customer SegmentationProject 9: Used Car Price PredictionProject 10: Restaurant Reviews ClassificationProject 11: Multiclass image classification with ANNProject 12: Binary Data Classification with ANNProject 13: Object Recognition in Images with CNNProject 14: Binary Image Classification with CNNProject 15: Digit Recognition with CNNProject 16: Breast Cancer Detection with CNNProject 17: Predicting the Bank Customer Satisfaction with CNNProject 18: Credit Card Fraud Detection with CNNProject 19: IMDB Review Classification with RNN - LSTMProject 20: Multiclass Image Classification with RNN - LSTMProject 21: Google Stock Price Prediction with RNN and LSTMProject 22: Transfer Learning for Cats and Dogs ClassificationProject 23: Movie Review Classification with NLTKWith this course, I will teach you:1) To work on Regression, Classification, and Clustering Projects in Machine Learning2) Text projects in Machine Learning3) NN, CNN, RNN, and Transfer Learning Projects4) To build the Neural Networks from the scratch5) You will have a complete understanding of Artificial Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks3) You will learn to build the neural networks with LSTM and GRU4) Hands-On Transfer Learning5) Learn Natural Language Processing by doing a text classification projectSo what are you waiting for, Enroll Now and learn Machine Learning and Deep Learning to advance your career and increase your knowledge!Regards,Vijay Gadhave

Overview

Section 1: Introduction

Lecture 1 Course Overview

Lecture 2 Colab Notebooks

Section 2: Project 1: Breast Cancer Detection

Lecture 3 Business Problem

Lecture 4 Data Preprocessing: Part 1

Lecture 5 Data Preprocessing: Part 2

Lecture 6 Logistic Regression

Lecture 7 Random Forest Classifier

Lecture 8 Hyperparameter Tuning using Randomized search

Lecture 9 Predicting a Single Observation

Section 3: Project 2: Customer churn rate prediction

Lecture 10 Business Problem

Lecture 11 Data Preprocessing part 1

Lecture 12 Data Preprocessing part 2

Lecture 13 Logistic Regression

Lecture 14 Random Forest Classifier

Lecture 15 XGBoost Classifier

Lecture 16 Hyperparameter Tuning using Randomized Search

Lecture 17 Predicting a Single Observation

Section 4: Project 3: Medical insurance premium prediction

Lecture 18 Business Problem

Lecture 19 Data Preprocessing: Part 1

Lecture 20 Data Preprocessing: Part 2

Lecture 21 Building and Finalizing the model

Lecture 22 Predicting a single observation

Section 5: Project 4: House price prediction

Lecture 23 Business Problem

Lecture 24 Data Preprocessing: Part 1

Lecture 25 Data Preprocessing: Part 2

Lecture 26 Building and Finalizing the model

Lecture 27 Hyperparameter Tuning using Randomized Search

Section 6: Project 5: E signing of customers based on financial data

Lecture 28 Business Problem

Lecture 29 Data Preprocessing: Part 1

Lecture 30 Data Preprocessing: Part 2

Lecture 31 Building and Finalizing the model

Lecture 32 Hyperparameter Tuning using Randomized Search

Lecture 33 Predicting a single observation

Section 7: Project 6: Credit card fraud detection

Lecture 34 Business Problem

Lecture 35 Data Preprocessing: Part 1

Lecture 36 Data Preprocessing: Part 2

Lecture 37 Building and Finalizing the model

Lecture 38 Predicting a single observation

Section 8: Project 7: Employee Attrition Prediction

Lecture 39 Business Problem

Lecture 40 Data Preprocessing: Part 1

Lecture 41 Data Preprocessing: Part 2

Lecture 42 Building and Finalizing the model

Lecture 43 Hyperparameter Tuning using Randomized Search

Lecture 44 Predicting a single observation

Section 9: Project 8: Customer Segmentation

Lecture 45 Business Problem

Lecture 46 Data Preprocessing

Lecture 47 Building the model and predicting results

Section 10: Project 9: Used Car Price Prediction

Lecture 48 Business Problem

Lecture 49 Data Preprocessing Part 1

Lecture 50 Data Preprocessing Part 2

Lecture 51 Building and finalizing the Model

Lecture 52 Hyper-Parameter tuning and predicting the results

Section 11: Project 10: Restaurant Reviews Classification

Lecture 53 Business Problem

Lecture 54 Data Preprocessing Part 1

Lecture 55 Data Preprocessing Part 2

Lecture 56 Data Preprocessing Part 3

Lecture 57 Building and Finalizing the Model

Section 12: Project 11: Multiclass image classification with ANN

Lecture 58 Step 1 - Installation and Setup

Lecture 59 Step 2 - Data Preprocessing

Lecture 60 Imp Lecture (don't skip)

Lecture 61 Step 3 - Building the Model

Lecture 62 Step 4 - Training the Model

Lecture 63 Step 5 - Model evaluation and performance

Section 13: Project 12: Binary Data Classification with ANN

Lecture 64 Binary Data Classification Step 1

Lecture 65 Binary Data Classification Step 2

Lecture 66 Binary Data Classification Step 3

Lecture 67 Binary Data Classification Step 4

Lecture 68 Binary Data Classification Step 5

Section 14: Project 13: Object Recognition in Images with CNN

Lecture 69 Object Recognition in Images Step 1

Lecture 70 Object Recognition in Images Step 2

Lecture 71 Object Recognition in Images Step 3

Lecture 72 Object Recognition in Images Step 4

Lecture 73 Object Recognition in Images Step 5

Section 15: Project 14: Binary Image Classification with CNN

Lecture 74 Binary Image Classification Step 1

Lecture 75 Binary Image Classification Step 2

Lecture 76 Binary Image Classification Step 3

Lecture 77 Binary Image Classification Step 4

Lecture 78 Binary Image Classification Step 5

Section 16: Project 15: Digit Recognition with CNN

Lecture 79 Digit Recognition with CNN - Step 1

Lecture 80 Digit Recognition with CNN - Step 2

Lecture 81 Digit Recognition with CNN - Step 3

Section 17: Project 16: Breast Cancer Detection with CNN

Lecture 82 Breast Cancer Detection with CNN - Step 1

Lecture 83 Breast Cancer Detection with CNN - Step 2

Lecture 84 Breast Cancer Detection with CNN - Step 3

Section 18: Project 17: Predicting the Bank Customer Satisfaction with CNN

Lecture 85 Predicting the Bank Customer Satisfaction - Step 1

Lecture 86 Predicting the Bank Customer Satisfaction - Step 2

Lecture 87 Predicting the Bank Customer Satisfaction - Step 3

Lecture 88 Predicting the Bank Customer Satisfaction - Step 4

Section 19: Project 18: Credit Card Fraud Detection with CNN

Lecture 89 Credit Card Fraud Detection with CNN - Step 1

Lecture 90 Credit Card Fraud Detection with CNN - Step 2

Lecture 91 Credit Card Fraud Detection with CNN - Step 3

Lecture 92 Credit Card Fraud Detection with CNN - Step 4

Section 20: Project 19: IMDB Review Classification with RNN - LSTM

Lecture 93 IMDB Review Classification with RNN - LSTM: Step 1

Lecture 94 IMDB Review Classification with RNN - LSTM: Step 2

Lecture 95 IMDB Review Classification with RNN - LSTM: Step 3

Section 21: Project 20: Multiclass Image Classification with RNN - LSTM

Lecture 96 Multiclass Image Classification with RNN - LSTM: Step 1

Lecture 97 Multiclass Image Classification with RNN - LSTM: Step 2

Lecture 98 Multiclass Image Classification with RNN - LSTM: Step 3

Section 22: Project 21: Google Stock Price Prediction with RNN and LSTM

Lecture 99 Google Stock Price Prediction with RNN and LSTM: Step 1

Lecture 100 Google Stock Price Prediction with RNN and LSTM: Step 2

Lecture 101 Google Stock Price Prediction with RNN and LSTM: Step 3

Lecture 102 Google Stock Price Prediction with RNN and LSTM: Step 4

Lecture 103 Google Stock Price Prediction with RNN and LSTM: Step 5

Section 23: Project 22: Transfer Learning for Cats and Dogs Classification

Lecture 104 Cats and Dogs Classification Step 1

Lecture 105 Cats and Dogs Classification Step 2

Lecture 106 Cats and Dogs Classification Step 3

Lecture 107 Cats and Dogs Classification Step 4

Section 24: Project 23: Movie Review Classification with NLTK

Lecture 108 Movie Review Classifivation with NLTK Step 1

Lecture 109 Movie Review Classifivation with NLTK Step 2

Anyone who wants to learn practical applications of Machine Learning and Deep Learning