In this free machine learning certification course, you will learn Python, the basics of machine learning, how to build machine learning models, and feature engineering techniques to improve the performance of your machine learning models.
What is Machine Learning?
Machine learning is a branch of artificial intelligence (AI) that allows computers to learn and improve on their own without having to be explicitly programmed. Machine learning is concerned with the creation of computer programs that can access data and learn on their own.
Applications Of Machine Learning
- Smartphones detecting faces while taking photos or unlocking themselves
- Facebook, LinkedIn or any other social media site recommending your friends and ads you might be interested in
- Amazon recommending you the products based on your browsing history
- Banks using Machine Learning to detect Fraud transactions in real-time
3 Real World Problems that Can Be Solved By Machine Learning
Machine Learning problems can be divided into 3 broad classes:
- Supervised Machine Learning
- Unsupervised Machine Learning
- Reinforcement Learning
1. Supervised Machine Learning methods are used when you have previous data with outcomes (labels in machine learning language) and wish to predict future results. Problems involving supervised machine learning can be classified into two types:
- Classification Problems: When you need to categorize results into multiple groups. For example, determining whether a customer would default on their loan is a classification problem that any bank is interested in.
- Regression Problem: When you’re trying to figure out how much anything costs, you’re dealing with regression problems. A regression challenge, for example, is determining the expected amount of default from a customer.
2. Unsupervised Machine Learning: There are occasions when you don’t want to forecast an Outcome to the millisecond. You simply need to do some segmentation or grouping. For example, a bank would wish to segment its customers in order to better understand their behavior. Because we are not forecasting any outcomes, this is an Unsupervised Machine Learning issue.
3. Reinforcement Learning is thought to be the only way to achieve true artificial intelligence. And rightly so, because Reinforcement Learning has enormous promise. It’s a more complicated issue than typical machine learning, but it’s just as important in the future.
Prerequisites for the Free Machine Learning Certification Course for Beginners
This course requires no prior knowledge about Data Science or any tool.
There is no prerequisite to start with this course. In this course, you will be trained from basic to advance, so you don’t have to worry about anything, just need your continuity, focus, and dedication towards learning and completing this course. This course requires no prior knowledge about Data Science or any tool.
What’re You Gonna Learn From This Course?
- Python libraries like Numpy, Pandas, etc. to analyze your data efficiently.
- Importance of Statistics and Exploratory Data Analysis (EDA) in the data science field.
- Linear Regression, Logistic Regression, and Decision Trees for building machine learning models.
- Understand how to solve Classification and Regression problems using machine learning
- How to evaluate your machine learning models using the right evaluation metrics?
- Improve and enhance your machine learning model’s accuracy through feature engineering
Course Curriculum (Machine Learning Syllabus)
- Overview of the Course
- Introduction to Data Science and Machine Learning
- Setting up your system
- Introduction to Python
- Variables and Data Types
- Conditional Statements
- Looping Constructs
- Data Structures
- String Manipulation
- Modules, Packages and Standard Libraries
- Handling Text Files in Python
- Introduction to Python Libraries for Data Science
- Python Libraries for Data Science
- Reading Data Files in Python
- Preprocessing, Subsetting and Modifying Pandas Dataframes
- Sorting and Aggregating Data in Pandas
- Visualizing Patterns and Trends in Data
- Machine Learning Lifecycle
- Problem statement and Hypothesis Generation
- Importance of Stats and EDA
- Build Your First Predictive Model
- Evaluation Metrics
- Preprocessing Data
- Build Your First ML Model: k-NN
- Selecting the Right Model
- Linear Models
- Project: Customer Churn Prediction
- Decision Tree
- Feature Engineering
- Project: NYC Taxi Trip Duration prediction
Tools Covered In this Free Course
2 Real-Time Hands-on Projects On this Machine Learning Course
1. Customer Churn Prediction
A Bank wants to take care of customer retention for their product: savings accounts. The bank wants you to identify customers likely to churn balances below the minimum balance in the next quarter. You have the customer’s information such as age, gender, demographics along with their transactions with the bank. Your task as a data scientist would be to predict the propensity to churn for each customer.
2. NYC Taxi Trip Duration Prediction
Uber, Lyft, Ola, and many more online ride-hailing services are trying hard to use their extensive data to create data products such as pricing engines, driver allotment, etc. To improve the efficiency of taxi dispatching systems for such services, it is important to be able to predict how long a driver will have his taxi occupied or in other words the trip duration. This project will cover techniques to extract important features and accurately predict trip duration for taxi trips in New York using data from the TLC commission New York.
About the Instructor (Analytics Vidya)
For Analytics and Data Science professionals, Analytics Vidhya is a community-based knowledge platform. The platform’s goal is to become a comprehensive resource for Data Science professionals in terms of information and employment opportunities.
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