English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 147 lectures (18h 51m) | 9.45 GB
Learn To Build Machine Learning Project Practically From Scratch
Studying Machine Learning opens a world of opportunities to develop cutting edge applications in various areas, such as cybersecurity, image recognition, medicine, and face recognition.
Machine Learning is no longer just a niche subfield of computer science but technology giants have been using it for years – Machine learning algorithms power Walmart product recommendations, surge pricing at Uber, fraud detection at top financial institutions, content that Twitter, LinkedIn, Facebook and Instagram display on social media feeds or Google Maps. Machine learning products are being used daily, perhaps without realizing it. The future of machine learning is already here, it’s just that machine learning career is exploding now because of smart algorithms being used everywhere from email to mobile apps to marketing campaigns. If you are in search of the most in-demand and most-exciting career domains, gearing up yourself with machine learning skills is a good move now.
With several machine learning companies on the verge of hiring skilled ML engineers, it is becoming the brain behind business intelligence. Netflix announced prize worth $1 million to the first individual who could enhance the accuracy of its recommendation ML algorithm by 10%. This is a clear evidence on how significant even a slight enhancement is in the accuracy of recommendation machine learning algorithms to improve the profitability of Netflix. Every customer- centric organization is looking to adopt machine learning technology and is the next big thing paving opportunities for IT professionals. Machine learning algorithms have become the darlings of business and consumers so if you want to put yourselves somewhere in the upper echelon of software engineers then this is the best time to learn ML.
What you’ll learn
- Understanding how to create machine learning projects
- Learn to create machine learning models
- Learn to build various ML methods
- Create Exploratory Data Analysis
Table of Contents
Introduction to the course
1 Introduction To The Course
2 Course Project Outline
Project-1 Image Caption Bot
3 introduction, importing libraries – Image caption bot
4 data cleaning
5 data preprocessing
6 data preparation
7 Training the model
8 Download The Project Files
Project-2 Costa Rican household poverty prediction
9 Importing libraries and data -costa rican house hold
10 Data preprocessing and feature engineering
11 Creating models-costa rican house hold
12 Download The Project Files
Project-3 Stroke prediction problem
13 Importing libraries and data – Stroke Prediction Problem
14 data preprocessing
15 creating models
16 PCA -Stroke Prediction Problem
17 Download The Project Files
Project-4 Car price prediction
18 importing libraries data – car price prediction
19 understanding the data
20 creating and hypertunning model – car price prediction
21 Download The Project Files
Project-5 Bigmart sales prediction
22 importing libraries and data Bigmart sales prediction
23 understanding the data
24 EDA
25 Creating models
26 Hypertuning
27 Download The Project Files
Project-6 Loan Prediction Analysis
28 importing libraries data – Loan Prediction Analysis
29 data preprocessing, visualization
30 Creating models
31 hypertuning models
32 Download The Project Files
Project-7 Predicting employee attrition
33 Importing libraries and data – Predicting employee attrition
34 data preprocessing and visualization
35 Feature selection and model building
36 Hypertuning
37 Download The Project Files
Project-8 Predicting Hotel Booking
38 Importing libraries data – Predicting Hotel Booking
39 data preprocessing, EDA
40 Feature engineering, model building
41 Download The Project Files
Project-9 Apparent temperature prediction
42 Importing libraries data – Apparent temperature prediction
43 dataprocessing
44 Model building part1
45 Model building part2
46 Download The Project Files
Project-10 Consumer Complaint classification
47 Importing libraries data – Consumer Complaint classification
48 Data preprocessing
49 Model building
50 Hyperparameter tuning
51 Download The Project Files
Project-11 Live Sketch Project
52 Introduction to case study
53 Understanding the code
54 Write function to capture video
55 completing the function
56 testing the code
57 Download The Project Files
Project-12 Traditional Dance
58 Introduction
59 Introduction To Colab
60 Importing the dataset
61 splitting the data
62 creating the model.
63 complile the model
64 Train the model
65 Download The Project Files
Project-13 Leaf disease detector
66 Introduction to Leaf Disease Dectector
67 Importing Libraries and dataset
68 Creating inception layer
69 Data processing and creating model
70 Compiling the model.
71 Training the model
72 Download The Project Files
Project-14 Car Brand Identification
73 Introduction To Car Brand Detector
74 Importing the data
75 Creating the model
76 Compile the model.
77 Train the model
78 Download The Project Files
Project-15 Tweet Analysis Project
79 TWEET ANALYSIS INTRODUCTION
80 Importing libraries
81 Understanding the data
82 Splitting the data
83 Data Preprocessing
84 Download The Project Files
Project-16 Emotion Analysis using NLP
85 Introduction
86 Importing libraries and data
87 understand data
88 data visualization
89 data preprocessing
90 mp4
91 Download The Project Files
Project-17 Amazon Alexa Review system
92 Introduction
93 Importing lib and data
94 EDA
95 Model building
96 Download The Project Files
Project-18 Disaster Predictor System
97 Introduction to disaster system
98 importing lib and data
99 data preprocessing
100 data visualization
101 Creating NLP model
102 Download The Project Files
Project-19 IMDB Movie Review system
103 Intro to IMDB review system
104 Importing Libraries and dataset
105 Data Visualization
106 Data Preprocessing
107 Building Models
108 Download The Project Files
Project-20 Vaccination for justified user system using NLP
109 Introduction to vaccination for justified user system using NLP.
110 Importing data and libraries
111 data preprocessing
112 data visualization
113 creating models
114 Download The Project Files
Project-21 Blood donation analysis
115 Introduction
116 import libraries
117 EDA or data exploration.
118 model building
119 final
120 Download The Project Files
Project-22 DNA classification
121 Introduction
122 importing libraries
123 EDA Human data
124 EDA chimp data
125 model building human data
126 model building chimp data
127 final
128 Download The Project Files
Project-23 Kyphosis disease classification
129 Introduction
130 importing data
131 EDA or visualize data
132 model building
133 final
134 Download The Project Files
Project-24 Mortality prediction in ICU using ANN
135 mortality prediction intro
136 Importing libraries
137 EDA-1
138 EDA-2 correlations
139 model building
140 mortality prediction in ICU using ANN final
141 Download The Project Files
Project-25 Sucide rate trend analysis
142 sucide rate trend analysis intro
143 importing libraries and dataset
144 EDA
145 model building
146 final
147 Download The Project Files
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