Learn to create machine learning algorithms in Python for students and professionals
What you will learn
Learn Python programming and Scikit learn applied to machine learning regression
Understand the underlying theory behind simple and multiple linear regression techniques
Learn to solve regression problems (linear regression and logistic regression)
Learn the theory and the practical implementation of logistic regression using sklearn
Learn the mathematics behind decision trees
Learn about the different algorithms for clustering
Description
To understand how organizations like Google, Amazon, and even Udemy use machine learning and artificial intelligence (AI) to extract meaning and insights from enormous data sets, this machine learning course will provide you with the essentials. According to Glassdoor and Indeed, data scientists earn an average income of $120,000, and that is just the norm!
When it comes to being attractive, data scientists are already there. In a highly competitive job market, it is tough to keep them after they have been hired. People with a unique mix of scientific training, computer expertise, and analytical abilities are hard to find.
Like the Wall Street “quants” of the 1980s and 1990s, modern-day data scientists are expected to have a similar skill set. People with a background in physics and mathematics flocked to investment banks and hedge funds in those days because they could come up with novel algorithms and data methods.
That being said, data science is becoming one of the most well-suited occupations for success in the twenty-first century. It is computerized, programming-driven, and analytical in nature. Consequently, it comes as no surprise that the need for data scientists has been increasing in the employment market over the last several years.
The supply, on the other hand, has been quite restricted. It is challenging to get the knowledge and abilities required to be recruited as a data scientist.
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In this course, mathematical notations and jargon are minimized, each topic is explained in simple English, making it easier to understand. Once you’ve gotten your hands on the code, you’ll be able to play with it and build on it. The emphasis of this course is on understanding and using these algorithms in the real world, not in a theoretical or academic context.
You’ll walk away from each video with a fresh idea that you can put to use right away!
All skill levels are welcome in this course, and even if you have no prior statistical experience, you will be able to succeed!
English
language
Content
Introduction to Machine Learning
What is Machine Learning?
Applications of Machine Learning
Machine learning Methods
What is Supervised learning?
What is Unsupervised learning?
Supervised learning vs Unsupervised learning
Course Materials
Simple Linear Regression
Introduction to regression
How Does Linear Regression Work?
Line representation
Implementation in python: Importing libraries & datasets
Implementation in python: Distribution of the data
Implementation in python: Creating a linear regression object
Multiple Linear Regression
Understanding Multiple linear regression
Implementation in python: Exploring the dataset
Implementation in python: Encoding Categorical Data
Implementation in python: Splitting data into Train and Test Sets
Implementation in python: Training the model on the Training set
Implementation in python: Predicting the Test Set results
Evaluating the performance of the regression model
Root Mean Squared Error in Python
Classification Algorithms: K-Nearest Neighbors
Introduction to classification
K-Nearest Neighbors algorithm
Example of KNN
K-Nearest Neighbours (KNN) using python
Implementation in python: Importing required libraries
Implementation in python: Importing the dataset
Implementation in python: Splitting data into Train and Test Sets
Implementation in python: Feature Scaling
Implementation in python: Importing the KNN classifier
Implementation in python: Results prediction & Confusion matrix
Classification Algorithms: Decision Tree
Introduction to decision trees
What is Entropy?
Exploring the dataset
Decision tree structure
Implementation in python: Importing libraries & datasets
Implementation in python: Encoding Categorical Data
Implementation in python: Splitting data into Train and Test Sets
Implementation in python: Results prediction & Accuracy
Classification Algorithms: Logistic regression
Introduction
Implementation steps
Implementation in python: Importing libraries & datasets
Implementation in python: Splitting data into Train and Test Sets
Implementation in python: Pre-processing
Implementation in python: Training the model
Implementation in python: Results prediction & Confusion matrix
Logistic Regression vs Linear Regression
Clustering
Introduction to clustering
Use cases
K-Means Clustering Algorithm
Elbow method
Steps of the Elbow method
Implementation in python
Hierarchical clustering
Density-based clustering
Implementation of k-means clustering in python
Importing the dataset
Visualizing the dataset
Defining the classifier
3D Visualization of the clusters
3D Visualization of the predicted values
Number of predicted clusters
Recommender System
Introduction
Collaborative Filtering in Recommender Systems
Content-based Recommender System
Implementation in python: Importing libraries & datasets
Merging datasets into one dataframe
Sorting by title and rating
Histogram showing number of ratings
Frequency distribution
Jointplot of the ratings and number of ratings
Data pre-processing
Sorting the most-rated movies
Grabbing the ratings for two movies
Correlation between the most-rated movies
Sorting the data by correlation
Filtering out movies
Sorting values
Repeating the process for another movie
Quiz Time
Conclusion
Conclusion
Add-On Information:
- Course Overview
- Embark on a transformative journey into the world of Artificial Intelligence with this comprehensive beginner’s guide to Machine Learning using Python.
- This course is meticulously crafted to demystify complex concepts, providing a solid foundation for individuals with little to no prior experience in programming or machine learning.
- You’ll transition from Python novice to a capable practitioner, equipped to build, train, and evaluate foundational machine learning models.
- Our curriculum focuses on practical application, ensuring you gain hands-on experience by working through real-world datasets and scenarios.
- We emphasize understanding the “why” behind the algorithms, not just the “how,” fostering a deeper comprehension of the machine learning process.
- The course is structured to build your confidence incrementally, starting with core Python skills and progressively introducing machine learning paradigms.
- You’ll develop the ability to identify suitable machine learning approaches for various data-driven challenges.
- This program serves as your springboard into advanced machine learning topics and a career in data science.
- Discover the power of predictive modeling and gain insights from data through the lens of Python and its robust machine learning libraries.
- We aim to empower you with the essential skills to start contributing to data-driven projects from day one.
- Requirements / Prerequisites
- No Prior Programming Experience Required: We assume absolute beginners, and the course will guide you through Python fundamentals from scratch.
- Basic Computer Literacy: Familiarity with operating a computer, installing software, and navigating file systems is expected.
- Access to a Computer: A stable internet connection and a personal computer capable of running Python and its libraries.
- Curiosity and Eagerness to Learn: A genuine interest in understanding how machines can learn from data.
- Optional: Basic Mathematical Aptitude: While complex math is explained, a comfort with fundamental mathematical concepts will enhance understanding.
- Skills Covered / Tools Used
- Python Fundamentals: Master core Python concepts including variables, data types, control flow, functions, and data structures.
- Data Manipulation with Pandas: Learn to efficiently clean, transform, and analyze data using the powerful Pandas library.
- Numerical Computing with NumPy: Understand how to perform efficient numerical operations and array manipulations with NumPy.
- Data Visualization: Develop the ability to create insightful visualizations using Matplotlib and Seaborn to explore and communicate data patterns.
- Model Evaluation Metrics: Learn to assess the performance of your machine learning models using standard evaluation techniques.
- Data Preprocessing Techniques: Acquire skills in preparing raw data for machine learning algorithms, including handling missing values and feature scaling.
- Introduction to Scikit-learn: Gain practical experience with the industry-standard Scikit-learn library for implementing machine learning algorithms.
- Algorithm Interpretation: Develop an understanding of how different machine learning algorithms work under the hood.
- Problem Solving with Data: Apply learned concepts to solve practical data-related challenges.
- Development Environment Setup: Learn to set up your Python development environment using tools like Anaconda or Pip.
- Benefits / Outcomes
- Become Job-Ready: Gain foundational skills highly sought after in the rapidly growing field of data science and machine learning.
- Unlock Career Opportunities: Open doors to roles such as Junior Data Scientist, Machine Learning Analyst, or AI Enthusiast.
- Automate Tasks: Learn to build systems that can learn and make predictions, automating repetitive tasks.
- Make Data-Driven Decisions: Develop the ability to extract meaningful insights from data to inform strategic decisions.
- Understand AI Concepts: Demystify the core principles of artificial intelligence and machine learning.
- Build a Portfolio: Complete practical projects that can be showcased to potential employers.
- Boost Your Resume: Add valuable and in-demand technical skills to your professional profile.
- Personal Projects: Empower yourself to undertake personal machine learning projects and explore your own datasets.
- Foundation for Advanced Learning: This course provides the essential building blocks for pursuing more specialized machine learning topics.
- Enhanced Problem-Solving Abilities: Develop a more analytical and data-centric approach to problem-solving.
- PROS
- Extremely Beginner-Friendly: Designed for individuals with zero prior coding or ML experience.
- Practical, Hands-On Approach: Focuses on building real models and solving actual problems.
- Comprehensive Coverage of Fundamentals: Ensures a strong grasp of both Python and essential ML concepts.
- Builds Confidence: The incremental learning path is designed to make complex topics accessible.
- Valuable Skillset for Today’s Market: Equips learners with highly sought-after technical abilities.
- CONS
- Focus on Foundational Algorithms: While comprehensive, it may not delve into highly advanced or niche machine learning techniques.