Use classification to solve business problems and master the basics of Machine Learning classification in Python
- Understand how to interpret the result of Logistic Regression model and translate them into actionable insight
- Learn the linear discriminant analysis and K-Nearest Neighbors technique
- Learn how to solve real life problem using the different classification techniques
- Preliminary analysis of data using Univariate analysis before running classification model
- Predict future outcomes basis past data by implementing Machine Learning algorithm
- Indepth knowledge of data collection and data preprocessing for Machine Learning logistic regression problem
- Course contains a end-to-end DIY project to implement your learnings from the lectures
- Basic statistics using Numpy library in Python
- Data representation using Seaborn library in Python
- Classification techniques of Machine Learning using Scikit Learn and Statsmodel libraries of Python
- Students will need to install Python and Anaconda software but we have a separate lecture to help you install the same
The course “Machine Learning Basics: Classification models in Python” teaches you all the steps of creating a Classification model to solve business problems.
Below is a list of popular FAQs of students who want to start their Machine learning journey-
What is Machine Learning?
Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
Which all classification techniques are taught in this course?
In this course we learn both parametric and non-parametric classification techniques. The primary focus will be on the following three techniques:
- Logistic Regression
- Linear Discriminant Analysis
- K – Nearest Neighbors (KNN)
How much time does it take to learn Classification techniques of machine learning?
Classification is easy but no one can determine the learning time it takes. It totally depends on you. The method we adopted to help you learn classification starts from the basics and takes you to advanced level within hours. You can follow the same, but remember you can learn nothing without practicing it. Practice is the only way to remember whatever you have learnt. Therefore, we have also provided you with another data set to work on as a separate project of classification.
What are the steps I should follow to be able to build a Machine Learning model?
You can divide your learning process into 3 parts:
Statistics and Probability – Implementing Machine learning techniques require basic knowledge of Statistics and probability concepts. Second section of the course covers this part.
Understanding of Machine learning – Fourth section helps you understand the terms and concepts associated with Machine learning and gives you the steps to be followed to build a machine learning model
Programming Experience – A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in the recent days. Third section will help you set up the Python environment and teach you some basic operations. In later sections there is a video on how to implement each concept taught in theory lecture in Python
Understanding of models – Fifth and sixth section cover Classification models and with each theory lecture comes a corresponding practical lecture where we actually run each query with you.
Why use Python for Machine Learning?
Understanding Python is one of the valuable skills needed for a career in Machine Learning.
Though it hasn’t always been, Python is the programming language of choice for data science. Here’s a brief history:
In 2016, it overtook R on Kaggle, the premier platform for data science competitions.
In 2017, it overtook R on KDNuggets’s annual poll of data scientists’ most used tools.
In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals.
Machine Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it’s nice to know that employment opportunities are abundant (and growing) as well.
What is the difference between Data Mining, Machine Learning, and Deep Learning?
Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decision-making, and actions.
Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning.
- People pursuing a career in data science
- Working Professionals beginning their Data journey
- Statisticians needing more practical experience
- Anyone curious to master classification machine learning techniques from Beginner to Advanced in short span of time
Created by Start-Tech Academy
Last updated 4/2019
Size: 2.15 GB