Published 8/2023
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.57 GB | Duration: 5h 33m
20 practical projects of Machine Learning and Deep Learning and their implementation in Python along with all the codes
What you’ll learn
Introducing the structure of Machine Learning and Deep Learning and their application in real problems
Introducing Machine Learning and Deep Learning algorithms and launching them in projects
Implementing Machine Learning and Deep Learning algorithms in Python
Familiarity with Python syntax for using Machine Learning and Deep Learning
Familiarity with Prediction Models
Data preparation and Visualization for use in Machine Learning and Deep Learning algorithms
Using Case Studies in projects
Learning how to use APIs to collect up-to-date data and learn about different Data sets
Introducing and using different Machine Learning and Deep Learning libraries in Python
Getting to know different Neural Networks and using them in real projects
Image processing using Artificial Neural Network (ANN) in Python
Classification with Neural Networks using Python
Familiarity with Natural Language Processing (NLP) and its use in projects
Forecasting the amount of sales, product price, sales price, etc.
Introducing and using algorithm validation metrics such as: Confusion matrix, Accuracy score, Precision score, Recall score, F1 score, etc.
+40 Cheat Sheets of Data Science, Machine Learning, Deep Learning and Python
Requirements
Basic Python
Description
Machine learning and Deep learning have revolutionized various industries by enabling the development of intelligent systems capable of making informed decisions and predictions. These technologies have been applied to a wide range of real-world projects, transforming the way businesses operate and improving outcomes across different domains.In this training, an attempt has been made to teach the audience, after the basic familiarity with machine learning and deep learning, their application in some real problems and projects (which are mostly popular and widely used projects).Also, all the coding and implementation of the models are done in Python, which in addition to machine learning, students’ skills in Python language will also increase and they will become more proficient in it.In this course, students will be introduced to some machine learning and deep learning algorithms such as Logistic regression, multinomial Naive Bayes, Gaussian Naive Bayes, SGDClassifier, … and different models. Also, they will use artificial neural networks for modeling to do the projects.The use of effective data sets in different fields, data preparation and pre-processing, visualization of results, use of validation metrics, different prediction methods, image processing, data analysis and statistical analysis are other parts of this course.Machine learning and deep learning have brought about a transformative impact across a multitude of industries, ushering in the creation of intelligent systems with the ability to make well-informed decisions and accurate predictions. These innovative technologies have been harnessed across a diverse array of real-world projects, reshaping the operational landscape of businesses and driving enhanced outcomes across various domains.Within this training course, the primary aim is to impart knowledge to the audience, assuming a foundational understanding of machine learning and deep learning concepts. The focus then shifts to their practical applications in addressing real-world challenges and undertaking projects, many of which are widely recognized and utilized within the field.Moreover, the entirety of coding and models implementation is conducted using the Python programming language. This dual approach not only deepens the students’ grasp of machine learning but also contributes to their proficiency in the Python language itself.The curriculum of this course encompasses the introduction of several fundamental machine learning and deep learning algorithms, including Logistic Regression, Multinomial Naive Bayes, Gaussian Naive Bayes, SGDClassifier, and some other algorithms among others, alongside diverse model architectures. As a pivotal component of the course, students delve into the utilization of artificial neural networks for modeling, which serves as the cornerstone for executing the various projects.Comprehensive utilization of pertinent datasets spanning diverse domains, coupled with comprehensive data preparation and preprocessing techniques, takes precedence. The students are further equipped with the skills to visualize and interpret outcomes effectively, employ validation metrics judiciously, explore varied prediction methodologies, engage in image processing, and undertake data analysis and statistical analysis. These facets collectively constitute the multifaceted landscape covered by this course.And at the end, more than 40 complete and practical cheat sheets in the field of data science, machine learning, deep learning and Python have been given to you.
Overview
Section 1: Introduction
Lecture 1 Introduction to Machine Learning
Section 2: Waiter Tips Prediction with Machine Learning
Lecture 2 Requirements
Lecture 3 Waiter Tips Prediction with Machine Learning
Lecture 4 Codes
Section 3: Future Sales Prediction with Machine Learning
Lecture 5 Requirements
Lecture 6 Future Sales Prediction with Machine Learning
Lecture 7 Codes
Section 4: Cryptocurrency Price Prediction with Machine Learning
Lecture 8 Cryptocurrency Price Prediction for the next 30 days
Lecture 9 Codes
Section 5: Stock Price Prediction with LSTM Neural Network
Lecture 10 Stock Price Prediction with LSTM Neural Network
Lecture 11 Codes
Section 6: Image Classification with Neural Networks
Lecture 12 Requirements
Lecture 13 Image Classification with Neural Networks
Lecture 14 Codes
Section 7: Visualize a Machine Learning Algorithm
Lecture 15 Requirements
Lecture 16 Visualize a Machine Learning Algorithm
Lecture 17 Codes
Section 8: Instagram Reach Analysis with Machine Learning
Lecture 18 Requirements
Lecture 19 Instagram Reach Analysis with Machine Learning
Lecture 20 Codes
Section 9: Mobile Price Classification with Machine Learning
Lecture 21 Requirements
Lecture 22 Mobile Price Classification with Machine Learning
Lecture 23 Codes
Section 10: Gold Price Prediction with Machine Learning
Lecture 24 Gold Price Prediction with Machine Learning
Lecture 25 Codes
Section 11: Language Translation with Machine Learning
Lecture 26 Requirements
Lecture 27 Language Translation with Machine Learning
Lecture 28 Codes
Section 12: Covid-19 Vaccine Sentiment Analysis
Lecture 29 Requirements
Lecture 30 Covid-19 Vaccine Sentiment Analysis
Lecture 31 Codes
Section 13: Hotel Recommendation System with Natural Language Processing (NLP)
Lecture 32 Requirements
Lecture 33 Hotel Recommendation System with NLP
Lecture 34 Codes
Section 14: Email Spam Detection with Natural Language Processing (NLP)
Lecture 35 Requirements
Lecture 36 Email Spam Detection with NLP
Lecture 37 Codes
Section 15: Data Augmentation in Deep Learning and Neural Networks model
Lecture 38 Requirements
Lecture 39 Data Augmentation in Deep Learning and Neural Networks model
Lecture 40 Codes
Section 16: Image to Pencil Sketch
Lecture 41 Requirements
Lecture 42 Image to Pencil Sketch
Lecture 43 Codes
Section 17: Hate Speech Detection with Machine Learning
Lecture 44 Requirements
Lecture 45 Hate Speech Detection Model
Lecture 46 Codes
Section 18: SMS Spam Detection with Machine Learning
Lecture 47 Requirements
Lecture 48 SMS Spam Detection with Machine Learning
Lecture 49 Codes
Section 19: Resume Screening with Machine Learning
Lecture 50 Requirements
Lecture 51 Resume Screening with Machine Learning
Lecture 52 Codes
Section 20: Credit Card Fraud Detection with Machine Learning
Lecture 53 Requirements
Lecture 54 Credit Card Fraud Detection with Machine Learning
Lecture 55 Codes
Section 21: YouTube Trending Videos Analysis
Lecture 56 Requirements
Lecture 57 YouTube Trending Videos Analysis
Lecture 58 Codes
Section 22: Cheat Sheet
Lecture 59 Data Science, Machine Learning, Deep Learning, and Python Cheat Sheets
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