Deep Learning
by MAGNUS EKMAN

Best YouTube Playlists for Data Science

▶️ Python
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▶️ SQL
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▶️ Data Analysis
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▶️ Data Analyst
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▶️ Linear Algebra
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▶️ Calculus
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▶️ Statistics
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▶️ Machine Learning
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▶️ Deep Learning
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▶️ Excel Power Query
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▶️ Ruby
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▶️ Microsoft Excel
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The Top 5 Machine Learning Libraries in Python

A Gentle Introduction to the Top Python Libraries used in Applied Machine Learning

Rating ⭐️: 4.4 out 5
Students 👨‍🎓 : 103,885
Duration ⏰ : 1hr 27min of on-demand video
Created by 👨‍🏫: Mike West

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#Python #Libraries #Machine_Learning #programming
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Data Scientist vs Data Engineer vs Data Analyst

Drag, Drop, Analyze: The Rise of No-Code Data Science

No-code or low-code functionalities in data science have gained significant traction in recent years. These solutions are well-proven and matured, and they make data science more accessible to a wider range of people.
No-code or low-code data science solutions can be very rewarding. "The first and most important benefit is that they can lead to better forms of collaboration," Mierswa underscores. "Everyone can understand visual workflows or models if they are explained, however, not everyone is a computer scientist or programmer, and not everyone can understand code." So, in order to collaborate effectively, you need to understand what assets the team is collectively producing. "Data science is, at the end of the day, a team sport. You need people who understand the business problems, whether or not they can code, as coding may not be their daily business."

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6 Data Science Applications

Data Scientist Roadmap
|
|-- 1. Basic Foundations
| |-- a. Mathematics
| | |-- i. Linear Algebra
| | |-- ii. Calculus
| | |-- iii. Probability
| | `-- iv. Statistics
| |
| |-- b. Programming
| | |-- i. Python
| | | |-- 1. Syntax and Basic Concepts
| | | |-- 2. Data Structures
| | | |-- 3. Control Structures
| | | |-- 4. Functions
| | | `-- 5. Object-Oriented Programming
| | |
| | `-- ii. R (optional, based on preference)
| |
| |-- c. Data Manipulation
| | |-- i. Numpy (Python)
| | |-- ii. Pandas (Python)
| | `-- iii. Dplyr (R)
| |
| `-- d. Data Visualization
| |-- i. Matplotlib (Python)
| |-- ii. Seaborn (Python)
| `-- iii. ggplot2 (R)
|
|-- 2. Data Exploration and Preprocessing
| |-- a. Exploratory Data Analysis (EDA)
| |-- b. Feature Engineering
| |-- c. Data Cleaning
| |-- d. Handling Missing Data
| `-- e. Data Scaling and Normalization
|
|-- 3. Machine Learning
| |-- a. Supervised Learning
| | |-- i. Regression
| | | |-- 1. Linear Regression
| | | `-- 2. Polynomial Regression
| | |
| | `-- ii. Classification
| | |-- 1. Logistic Regression
| | |-- 2. k-Nearest Neighbors
| | |-- 3. Support Vector Machines
| | |-- 4. Decision Trees
| | `-- 5. Random Forest
| |
| |-- b. Unsupervised Learning
| | |-- i. Clustering
| | | |-- 1. K-means
| | | |-- 2. DBSCAN
| | | `-- 3. Hierarchical Clustering
| | |
| | `-- ii. Dimensionality Reduction
| | |-- 1. Principal Component Analysis (PCA)
| | |-- 2. t-Distributed Stochastic Neighbor Embedding (t-SNE)
| | `-- 3. Linear Discriminant Analysis (LDA)
| |
| |-- c. Reinforcement Learning
| |-- d. Model Evaluation and Validation
| | |-- i. Cross-validation
| | |-- ii. Hyperparameter Tuning
| | `-- iii. Model Selection
| |
| `-- e. ML Libraries and Frameworks
| |-- i. Scikit-learn (Python)
| |-- ii. TensorFlow (Python)
| |-- iii. Keras (Python)
| `-- iv. PyTorch (Python)
|
|-- 4. Deep Learning
| |-- a. Neural Networks
| | |-- i. Perceptron
| | `-- ii. Multi-Layer Perceptron
| |
| |-- b. Convolutional Neural Networks (CNNs)
| | |-- i. Image Classification
| | |-- ii. Object Detection
| | `-- iii. Image Segmentation
| |
| |-- c. Recurrent Neural Networks (RNNs)
| | |-- i. Sequence-to-Sequence Models
| | |-- ii. Text Classification
| | `-- iii. Sentiment Analysis
| |
| |-- d. Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU)
| | |-- i. Time Series Forecasting
| | `-- ii. Language Modeling
| |
| `-- e. Generative Adversarial Networks (GANs)
| |-- i. Image Synthesis
| |-- ii. Style Transfer
| `-- iii. Data Augmentation
|
|-- 5. Big Data Technologies
| |-- a. Hadoop
| | |-- i. HDFS
| | `-- ii. MapReduce
| |
| |-- b. Spark
| | |-- i. RDDs
| | |-- ii. DataFrames
| | `-- iii. MLlib
| |
| `-- c. NoSQL Databases
| |-- i. MongoDB
| |-- ii. Cassandra
| |-- iii. HBase
| `-- iv. Couchbase
|
|-- 6. Data Visualization and Reporting
| |-- a. Dashboarding Tools
| | |-- i. Tableau
| | |-- ii. Power BI
| | |-- iii. Dash (Python)
| | `-- iv. Shiny (R)
| |
| |-- b. Storytelling with Data
| `-- c. Effective Communication
|
|-- 7. Domain Knowledge and Soft Skills
| |-- a. Industry-specific Knowledge
| |-- b. Problem-solving
| |-- c. Communication Skills
| |-- d. Time Management
| `-- e. Teamwork
|
`-- 8. Staying Updated and Continuous Learning
|-- a. Online Courses
|-- b. Books and Research Papers
|-- c. Blogs and Podcasts
|-- d. Conferences and Workshops
`-- e. Networking and Community Engagement

Practitioner's Guide to Data Science

by Hui Lin and Ming Li

8 Books that Will Teach You the Basics of Data Science

In an era where data is hailed as the new oil, the demand for data scientists continues to soar. Data science, a multidisciplinary field that extracts insights and knowledge from data, has become a cornerstone of many industries. For those aspiring to enter this dynamic field, building a solid foundation is essential. Books are a timeless source of knowledge, and in this article, we’ll explore eight must-read books that will teach you the basics of data science, making your journey into this fascinating world more accessible.

1. “Python for Data Analysis” by Wes McKinney
Wes McKinney’s book is a fantastic starting point for beginners. It focuses on the practical use of Python, one of the most popular programming languages in data science. You’ll learn how to work with data structures, perform data cleaning, and apply statistical analysis. The book also introduces the powerful Pandas library for data manipulation.


Source-Link: analyticsinsight

Logistic Regression Practical Case Study

Breast Cancer detection using Logistic Regression

Rating ⭐️: 4.7 out 5
Students 👨‍🎓 : 35,819
Duration ⏰ : 1hr 4min of on-demand video
Created by 👨‍🏫: Hadelin de Ponteves, SuperDataScience Team, Ligency Team

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#Logistic #Regression
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7 Platforms for Getting High Paying Data Science Jobs
1. LinkedIn
2. Wellfound
3. Toptal
4. Upwork
5. Kolabtree
6. Indeed
7. Amazon Jobs

Python Libraries For Data Science

Top 5 Reasons Why Machine Learning Projects Fail

The intent of our article today is to help you get acquainted with the many reasons behind machine learning projects’ failure. We are hopeful that the information will help you plan a better implementation, one that carries fewer chances of failure in all three stages of ML execution: pre-project, during the project, and post-project.

1. Insufficient data
2. ML Models unsynchronized with the legacy systems
3. Lack of enough data scientists
4. Difficulty in updating
5. Lack of leaders’ support

The solution to addressing these challenges more often than not lies with partnering with a skilled machine learning solution provider company that understands both business and technical implications of applying a new-gen technology in a non-digital organization. They can help you in not just creating a work plan of how to integrate machine learning projects but also with adopting the new system in the most optimal way.

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Data Science: Theories, Models, Algorithms, and Analytics

by SANJIV RANJAN DAS

Data Engineer's Pathway

R, ggplot, and Simple Linear Regression

Begin to use R and ggplot while learning the basics of linear regression

Rating ⭐️: 4.1 out 5
Students 👨‍🎓 : 42,633
Duration ⏰ : 2hr 14min of on-demand video
Created by 👨‍🏫: Charles Redmond

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#R #linear #Regression
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Introduction to Data Science

One question to make your data project 10x more valuable

If you are the "data person" for your organization, then providing meaningful results to stakeholder data requests can sometimes feel like shots in the dark. However, you can make sure your data analysis is actionable by asking one magic question before getting started.

The magic question
Luckily, we don't need to spend all of our time defining the problem. Here is the one simple question that will get to the heart of any data request within minutes:
"What decision are you trying to make?"
Subtext: What action will you take once you have the answers?
If there is no action, then there will be no impact. This question will cut through all of the clutter and get straight to the action.
And the answer can be VERY telling! That's why it's so powerful.
A good response is specific! Almost immediately, you should be able to picture what they'll do once they see the data.

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How to choose a graph