🖥 Roadmap of free courses for learning Python and Machine learning.
▪Data Science
▪ AI/ML
▪ Web Dev
1. Start with this
https://kaggle.com/learn/python
2. Take any one of these
❯ https://openclassrooms.com/courses/6900856-learn-programming-with-python
❯ https://scaler.com/topics/course/python-for-beginners/
❯ https://simplilearn.com/learn-python-basics-free-course-skillup
3. Then take this
https://netacad.com/courses/programming/pcap-programming-essentials-python
4. Attempt for this certification
https://freecodecamp.org/learn/scientific-computing-with-python/
5. Take it to next level
❯ Data Scrapping, NumPy, Pandas
https://scaler.com/topics/course/python-for-data-science/
❯ Data Analysis
https://openclassrooms.com/courses/2304731-learn-python-basics-for-data-analysis
❯ Data Visualization
https://kaggle.com/learn/data-visualization
❯ Django
https://openclassrooms.com/courses/6967196-create-a-web-application-with-django
❯ Machine Learning
http://developers.google.com/machine-learning/crash-course
❯ Deep Learning (TensorFlow)
http://kaggle.com/learn/intro-to-deep-learning
https://www.tgoop.com/CodeProgrammer
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▪Data Science
▪ AI/ML
▪ Web Dev
1. Start with this
https://kaggle.com/learn/python
2. Take any one of these
❯ https://openclassrooms.com/courses/6900856-learn-programming-with-python
❯ https://scaler.com/topics/course/python-for-beginners/
❯ https://simplilearn.com/learn-python-basics-free-course-skillup
3. Then take this
https://netacad.com/courses/programming/pcap-programming-essentials-python
4. Attempt for this certification
https://freecodecamp.org/learn/scientific-computing-with-python/
5. Take it to next level
❯ Data Scrapping, NumPy, Pandas
https://scaler.com/topics/course/python-for-data-science/
❯ Data Analysis
https://openclassrooms.com/courses/2304731-learn-python-basics-for-data-analysis
❯ Data Visualization
https://kaggle.com/learn/data-visualization
❯ Django
https://openclassrooms.com/courses/6967196-create-a-web-application-with-django
❯ Machine Learning
http://developers.google.com/machine-learning/crash-course
❯ Deep Learning (TensorFlow)
http://kaggle.com/learn/intro-to-deep-learning
https://www.tgoop.com/CodeProgrammer
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ML_cheatsheets.pdf
6.5 MB
Machine Learning cheatsheet (very important)
https://www.tgoop.com/CodeProgrammer
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https://www.tgoop.com/CodeProgrammer
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Python | Machine Learning | Coding | R
✋ Hand gesture recognition Full Source Code 👇👇👇👇
✋ Hand gesture recognition
https://www.tgoop.com/CodeProgrammer
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import cv2
import mediapipe as mp
# Initialize MediaPipe Hands module
mp_hands = mp.solutions.hands
hands = mp_hands.Hands()
# Initialize MediaPipe Drawing module for drawing landmarks
mp_drawing = mp.solutions.drawing_utils
# Open a video capture object (0 for the default camera)
cap = cv2.VideoCapture(0)
while cap.isOpened():
ret, frame = cap.read()
if not ret:
continue
# Convert the frame to RGB format
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# Process the frame to detect hands
results = hands.process(frame_rgb)
# Check if hands are detected
if results.multi_hand_landmarks:
for hand_landmarks in results.multi_hand_landmarks:
# Draw landmarks on the frame
mp_drawing.draw_landmarks(frame, hand_landmarks, mp_hands.HAND_CONNECTIONS)
# Display the frame with hand landmarks
cv2.imshow('Hand Recognition', frame)
# Exit when 'q' is pressed
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# Release the video capture object and close the OpenCV windows
cap.release()
cv2.destroyAllWindows()https://www.tgoop.com/CodeProgrammer
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📈 Predictive Modeling for Future Stock Prices in Python: A Step-by-Step Guide
The process of building a stock price prediction model using Python.
1. Import required modules
2. Obtaining historical data on stock prices
3. Selection of features.
4. Definition of features and target variable
5. Preparing data for training
6. Separation of data into training and test sets
7. Building and training the model
8. Making forecasts
9. Trading Strategy Testing
https://www.tgoop.com/CodeProgrammer
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The process of building a stock price prediction model using Python.
1. Import required modules
2. Obtaining historical data on stock prices
3. Selection of features.
4. Definition of features and target variable
5. Preparing data for training
6. Separation of data into training and test sets
7. Building and training the model
8. Making forecasts
9. Trading Strategy Testing
https://www.tgoop.com/CodeProgrammer
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Code Stars
Get ahead of the game with Code Stars! Our platform sends you notifications about the hottest GitHub repositories that are rapidly gaining popularity. Don't miss out - join us now and be the first to discover the trending repositories that everyone will be talking about soon!
Get ahead of the game with Code Stars! Our platform sends you notifications about the hottest GitHub repositories that are rapidly gaining popularity. Don't miss out - join us now and be the first to discover the trending repositories that everyone will be talking about soon!
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How to Download Files From URLs With Python
https://realpython.com/python-download-file-from-url
https://www.tgoop.com/CodeProgrammer
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https://realpython.com/python-download-file-from-url
https://www.tgoop.com/CodeProgrammer
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🔥 Master Data Science for free
📂 Computer Science 101
https://online.stanford.edu/courses/soe-ycscs101-computer-science-101
📂 Machine Learning Specialization
https://coursera.org/specializations/machine-learning-introduction
📂 Artificial Intelligence for Robotics
https://udacity.com/course/artificial-intelligence-for-robotics--cs373
📂 Designing Your Career
https://online.stanford.edu/courses/tds-y0003-designing-your-career
📂 Stanford: Game Theory
https://online.stanford.edu/courses/soe-ycs0002-game-theory
📂 Machine Learning with Python
https://www.freecodecamp.org/learn/machine-learning-with-python/
📂 Probability and Statistics: To P or Not To P? (Coursera)
https://www.coursera.org/learn/probability-statistics
📂 Numpy complete free course
https://www.youtube.com/playlist?list=PLysMDSbb9Hcz3Gdi9oV-btohZ9zhths-r
📂Advanced Machine Learning
https://www.kaggle.com/learn/intro-to-machine-learning
📂 Stat 110: Harvard University (YouTube)
https://www.youtube.com/watch?v=KbB0FjPg0mw&list=PL2SOU6wwxB0uwwH80KTQ6ht66KWxbzTIo&index=1
📂 The Open Source Data Science Masters
https://github.com/datasciencemasters/go
📂 Google - artificial intelligence for everyone
https://edx.org/learn/artificial-intelligence/google-google-ai-for-anyone
📂Microsoft - AI for Beginners
https://microsoft.github.io/AI-For-Beginners
📂 IBM - AI for Everyone: Master the Basics
https://edx.org/learn/artificial-intelligence/ibm-ai-for-everyone-master-the-basics
📂 Harvard - Introduction to Artificial Intelligence with Python
https://cs50.harvard.edu/ai/2023
📂 Introduction to Generative AI
https://cloudskillsboost.google/journeys/118
📂 Deep Learning - Finetuning Large Language Models
https://deeplearning.ai/short-courses/finetuning-large-language-models/
📂Microsoft - AI Basics in Azure
https://learn.microsoft.com/en-us/training/paths/create-no-code-predictive-models-azure-machine-learning/
https://www.tgoop.com/CodeProgrammer
📂Linux Foundation
https://edx.org/learn/computer-programming/the-linux-foundation-data-and-ai-fundamentals
📂 12 Linux courses:
https://www.tgoop.com/linuxkalii/538
📂 Alison - 13 free AI courses
https://alison.com/tag/artificial-intelligence
📂 Artificial Intelligence Projects:
https://mygreatlearning.com/academy/learn-for-free/courses/artificial-intelligence-projects
📂 Introduction to Internet of Things:
https://online.stanford.edu/courses/xee100-introduction-internet-things
📂 Graph Search, Shortest Paths, and Data Structures
https://coursera.org/learn/algorithms-graphs-data-structures
📂 Python:
http://cs50.harvard.edu/python/2022/
📂 Machine Learning:
http://developers.google.com/machine-learning/crash-course
📂 Deep Learning
http://introtodeeplearning.com
📂 Data Analysis
http://pll.harvard.edu/course/data-analysis-life-sciences-4-high-dimensional-data-analysis
📂 Linear algebra:
http://pll.harvard.edu/course/data-analysis-life-sciences-2-introduction-linear-models-and-matrix-algebra
📂 Algebra basics
https://www.khanacademy.org/math/algebra-basics
📂 Excel and PowerBI
http://learn.microsoft.com/training/paths/modern-analytics/
📂 Data visualization:
http://pll.harvard.edu/course/data-science-visualization
📂 PowerBI
http://learn.microsoft.com/users/collinschedler-0717/collections/m14nt4rdwnwp04
📂 Tableau:
http://tableau.com/learn/training
📂 Statistics:
http://cognitiveclass.ai/courses/statistics-101
📂 SQL:
http://online.stanford.edu/courses/soe-ydatabases0005-databases-relational-databases-and-sql
https://www.tgoop.com/CodeProgrammer
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📂 Computer Science 101
https://online.stanford.edu/courses/soe-ycscs101-computer-science-101
📂 Machine Learning Specialization
https://coursera.org/specializations/machine-learning-introduction
📂 Artificial Intelligence for Robotics
https://udacity.com/course/artificial-intelligence-for-robotics--cs373
📂 Designing Your Career
https://online.stanford.edu/courses/tds-y0003-designing-your-career
📂 Stanford: Game Theory
https://online.stanford.edu/courses/soe-ycs0002-game-theory
📂 Machine Learning with Python
https://www.freecodecamp.org/learn/machine-learning-with-python/
📂 Probability and Statistics: To P or Not To P? (Coursera)
https://www.coursera.org/learn/probability-statistics
📂 Numpy complete free course
https://www.youtube.com/playlist?list=PLysMDSbb9Hcz3Gdi9oV-btohZ9zhths-r
📂Advanced Machine Learning
https://www.kaggle.com/learn/intro-to-machine-learning
📂 Stat 110: Harvard University (YouTube)
https://www.youtube.com/watch?v=KbB0FjPg0mw&list=PL2SOU6wwxB0uwwH80KTQ6ht66KWxbzTIo&index=1
📂 The Open Source Data Science Masters
https://github.com/datasciencemasters/go
📂 Google - artificial intelligence for everyone
https://edx.org/learn/artificial-intelligence/google-google-ai-for-anyone
📂Microsoft - AI for Beginners
https://microsoft.github.io/AI-For-Beginners
📂 IBM - AI for Everyone: Master the Basics
https://edx.org/learn/artificial-intelligence/ibm-ai-for-everyone-master-the-basics
📂 Harvard - Introduction to Artificial Intelligence with Python
https://cs50.harvard.edu/ai/2023
📂 Introduction to Generative AI
https://cloudskillsboost.google/journeys/118
📂 Deep Learning - Finetuning Large Language Models
https://deeplearning.ai/short-courses/finetuning-large-language-models/
📂Microsoft - AI Basics in Azure
https://learn.microsoft.com/en-us/training/paths/create-no-code-predictive-models-azure-machine-learning/
https://www.tgoop.com/CodeProgrammer
📂Linux Foundation
https://edx.org/learn/computer-programming/the-linux-foundation-data-and-ai-fundamentals
📂 12 Linux courses:
https://www.tgoop.com/linuxkalii/538
📂 Alison - 13 free AI courses
https://alison.com/tag/artificial-intelligence
📂 Artificial Intelligence Projects:
https://mygreatlearning.com/academy/learn-for-free/courses/artificial-intelligence-projects
📂 Introduction to Internet of Things:
https://online.stanford.edu/courses/xee100-introduction-internet-things
📂 Graph Search, Shortest Paths, and Data Structures
https://coursera.org/learn/algorithms-graphs-data-structures
📂 Python:
http://cs50.harvard.edu/python/2022/
📂 Machine Learning:
http://developers.google.com/machine-learning/crash-course
📂 Deep Learning
http://introtodeeplearning.com
📂 Data Analysis
http://pll.harvard.edu/course/data-analysis-life-sciences-4-high-dimensional-data-analysis
📂 Linear algebra:
http://pll.harvard.edu/course/data-analysis-life-sciences-2-introduction-linear-models-and-matrix-algebra
📂 Algebra basics
https://www.khanacademy.org/math/algebra-basics
📂 Excel and PowerBI
http://learn.microsoft.com/training/paths/modern-analytics/
📂 Data visualization:
http://pll.harvard.edu/course/data-science-visualization
📂 PowerBI
http://learn.microsoft.com/users/collinschedler-0717/collections/m14nt4rdwnwp04
📂 Tableau:
http://tableau.com/learn/training
📂 Statistics:
http://cognitiveclass.ai/courses/statistics-101
📂 SQL:
http://online.stanford.edu/courses/soe-ydatabases0005-databases-relational-databases-and-sql
https://www.tgoop.com/CodeProgrammer
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