Python programming for Machine Learning , Data Analytics

🎉 Python Programming for Machine Learning & Data Analytics 📘

Course Headline:

👉 "Learn to Create Machine Learning Algorithms in Python [Step by Step]"

Introduction:

Welcome to the Academy of Computing & Artificial Intelligence where we embark on a journey through the fascinating world of Data Science and Machine Learning using Python as our primary tool. By the end of this course, you'll not only grasp the fundamental concepts of Python programming but also have a solid understanding of key data science and machine learning principles.

Course Description:

This comprehensive course is designed to guide you step by step through the intricacies of machine learning and data science with Python. You'll enhance your core programming skills, reach an advanced level, and learn software design techniques such as flow charts, pseudocodes, and algorithms. By following this structured path, you'll cover a variety of critical areas in data science and machine learning, including:

1. Setting up the Environment for Python Machine Learning 🐍

Get started with setting up your Python environment using tools like Anaconda, ensuring you have all the necessary libraries and dependencies for your projects.

2. Understanding Data With Statistics & Data Pre-processing 📊

• Reading data from files
• Checking dimensions of data
• Statistical summary of data
• Correlation between attributes

3. Data Pre-processing 🔄

• Scaling your data with Python demonstrations
• Normalization, binarization, and standardization techniques in Python
• Feature Selection Techniques: Univariate Selection

4. Data Visualization with Python 📈

Master the art of visualizing data with Python through charting. Learn to prepare your data and create Bar Charts, Histograms, Pie Charts, and more.

5. Artificial Neural Networks with Python, KERAS 🧠

Dive into the world of neural networks using Keras. We'll guide you through developing an artificial neural network in Python, step by step.

6. Deep Learning - Handwritten Digits Recognition [Complete Project] 🖼️

Engage with a full-fledged deep learning project on handwritten digits recognition.

7. Naive Bayes Classifier with Python [Lecture & Demo] 📫

Understand and implement the Naive Bayes Classifier in Python, complete with a live demonstration.

8. Linear regression 📐

Explore the basics of linear regression, a fundamental algorithm in predicting numerical targets.

9. Logistic regression ⚖️

Transition from predicting numbers to predictions of categorical outcomes with logistic regression.

10. Introduction to clustering [K-Means Clustering] 📋

Learn about K-Means clustering, an unsupervised learning algorithm used for partitioning a dataset into K distinct, non-overlapping clusters.

Python Programming Essentials:

• Setting up the environment with Anaconda for Python beginners.
• Learning the basics of Python: variables, lists, tuples, dictionaries.
• Understanding boolean operations, conditions, loops (sequence, selection, repetition/iteration).
• Mastering functions and file handling in Python.
• Crafting flow charts and understanding algorithms.

Introduction to Software Design:

• Grasp the fundamentals of problem-solving in software design.
• Dive into software design through flowcharts (sequence, modular design, repetition).
• Learn the importance of modular design for better program structure.

Key Takeaways:

By the end of this course, you'll have a solid understanding of Python programming, data science, and machine learning concepts, equipping you with the skills to create your own predictive models and algorithms. You'll be able to set up your environment, prepare and visualize your data, and implement machine learning techniques, all within the powerful framework of Python.

🎓 Join us on this exciting learning adventure and unlock your potential in the realm of Data Science and Machine Learning with Python! 🚀