The Computer Scientist
Lecture #24: How to Get a Certificate
1:24
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Lecture #23: How to Leave a Review
1:27
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Lecture #22: Final Lecture
0:53
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Lecture #21: How to Communicate with Clients
4:06
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Lecture #20: How to Сraft a Proposal
5:32
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Lecture #19: Advantages and Disadvantages in the RSS Search of Projects
3:27
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Lecture #18: RSS Project Feed and RSS Readers for Its Processing
3:50
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Lecture #17: Validation of Projects and Customers
2:43
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Lecture #16: Project Filtration on the Different Stages of Your Upwork Career
3:52
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Lecture #15: How to Filtrate Projects with Keywords and Phrases Advanced search
2:39
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Lecture #14: Overview of Projects Catalog Basic Filters Job Search
8:19
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Lecture #13: Processing and Analysis of Proposals from Other Freelancers
1:49
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Lecture #12: Creation of Your Project Client Profile Overview
4:10
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Lecture #11: How to Create a Client Profile
1:59
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Lecture #10: Video Introduction Book a Consultation Projects Catalog
2:26
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Lecture #9: Portfolio
3:12
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Lecture #8: How to Create a Compelling Upwork Title and Profile Description
3:20
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Lecture #7: What Catches the Customer’s Eye in Your Profile
4:19
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Lecture #6: Specializations of Your Profile
2:04
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Lecture #5: Optimizing Your Profile for Upwork Algorithms
3:17
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Lecture #4: Projects Catalog Working with Offers from Freelancers
2:32
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Lecture #3: Talents Catalog Analysis of Direct Competitors
5:16
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Lecture #2: Jobs Catalog Using Keywords and Categories for Searching Relevant Projects
4:40
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Lecture #1: Introduction to the Course
5:07
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Lecture #20: LinkedIn Marketing Frequently Asked Questions
5:09
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Lecture #19: LinkedIn Marketing Success Stories
5:57
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Lecture #18: LinkedIn Marketing Premium Tools And Services To Consider
5:12
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Lecture #17: LinkedIn Marketing Do’s And Don’ts
5:19
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Lecture #16: Powerful InMail Strategies That Work In 2024
4:19
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Lecture #15: How To Make The LinkedIn Algorithm Work For You In 2024
5:27
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Lecture #14: Tips For Creating Awesome LinkedIn Native Content In 2024
4:44
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Lecture #13: Simple LinkedIn Marketing Hacks To Grow Your B2B Audience
5:38
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Lecture #12: The Perfect Step By Step LinkedIn Marketing Plan For 2024
6:38
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Lecture #11: Creating A LinkedIn Messaging Ad
5:40
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Lecture #10: Creating A LinkedIn Video Ad
4:27
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Lecture #9: Creating A LinkedIn Traffic Ad
5:36
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Lecture #8: Using “Search” To Find And Connect With Leads
3:39
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Lecture #7: Creating And Growing A LinkedIn Group For Prospects
4:04
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Lecture #6: Sponsoring LinkedIn Marketing Posts The Right Way
4:52
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Lecture #5: Creating Effective LinkedIn Marketing Posts
4:10
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Lecture #4: Using “Content Suggestions” To Find Trending LinkedIn Topics
4:28
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Lecture #3: Creating an Engaging Company Page
5:35
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Lecture #2: Setting Up a LinkedIn Profile That’s Optimized For Business
5:05
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Lecture #1: Introduction
4:36
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Lecture #54: Stock Price Prediction using LSTM
7:10
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Lecture #53: Implementation of CNN using keras and tensor flow
12:58
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Lecture #52: Gold Price Prediction using ANN
9:17
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Lecture #51: Medical Insurance Cost Prediction
16:45
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Lecture #50: Diabetes Prediction
14:23
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Lecture #49: Recurrent Neural Network RNN
8:20
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Lecture #48: Convolutional Neural Network CNN
9:21
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Lecture #47: Optimizers in Neural Networks
7:35
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Lecture #46: Activation Functions in Neural Networks
7:43
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Lecture #45: Artificial Neural Network ANN
5:28
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Lecture #44: AWS SageMaker MarketPlace
3:31
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Lecture #43: No Code ML using AWS SageMaker Canvas
9:33
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Lecture #42: Linear Learner Algorithm Practical Implementation
17:56
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Lecture #41: Built in ML Algorithms in AWS SageMaker
7:11
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Lecture #40: First Machine Learning Practical on AWS SageMaker
9:53
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Lecture #39: Introduction to AWS SageMaker
5:35
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Lecture #38: Different AWS Services
7:46
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Lecture #37: Introduction to AWS
9:37
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Lecture #36: Cloud Computing Introduction
6:57
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Lecture #35: Machine Learning Model Evaluation Metrics
7:23
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Lecture #34: Machine Learning Pipeline
9:37
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Lecture #33: Hyper Parameter Optimization with GridSearchCV
13:19
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Lecture #32: K Means Clustering
10:31
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Lecture #31: Random Forest
7:44
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Lecture #30: Decision Tree
10:52
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Lecture #29: SVM
12:13
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Lecture #28: KNN
10:23
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Lecture #27: Logistic Regression
11:02
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Lecture #26: Linear Regression
10:40
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Lecture #25: Regression Analysis
6:53
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Lecture #24: Train Test Split
3:22
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Lecture #23: Machine Learning Life Cycle
5:39
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Lecture #22: Unsupervised Machine Learning
3:17
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Lecture #21: Supervised Machine Learning
4:24
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Lecture #20: Introduction to Machine Learning
6:38
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Lecture #19: Feature Scaling
7:48
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Lecture #18: Feature Encoding
11:07
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Lecture #17: Handling Missing Values
9:08
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Lecture #16: Seaborn
11:29
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Lecture #15: Different type of plots in Matplotlib
9:59
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Lecture #14: Introduction to Matplotlib
4:22
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Lecture #13: Analyzing Data Frames in Pandas
6:59
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Lecture #12: Read CSV
3:41
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Lecture #11: Pandas DataFrame
6:41
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Lecture #10: Series in Pandas
4:54
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Lecture #9: Pandas Introduction
4:31
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Lecture #8: Numpy Array Searching and Sorting
7:26
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Lecture #7: Slicing
8:57
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Lecture #6: Iterating Numpy Arrays
6:15
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Lecture #5: Numpy Array Indexing
8:12
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Lecture #4: Shape and Reshape
11:22
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Lecture #3: Numpy Arrays
10:49
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Lecture #2: Introduction to Numpy
6:52
The Computer Scientist
Lecture #1: Introduction
6:26