Basics of Machine Learning using Python Live Classroom Training

The Basics of Machine Learning using Python Online live Classroom Training by GingerBoard Academy is 4 weeks, 1.5 hours per day, 5 Days a week covering the basic concepts of Python, covering important libraries used for Data analysis like Pandas, Numpy, Matplotlib, Regex, etc. and using Machine Learning libraries for building ML models and how to improve performance of a model. Student will also be doing a project during the training which needs to be submitted one week after the completion of the course.

You will learn through online live classroom and get great hands on experience. This program is packed with assignments, class tests, weekly quiz, coding exercises and final exam.

For doubt clearing you can post query on forum and get answers within 48 hours. Also, a student counceller to connect with on queries and support.

Please Note:

This will be regorous 4 week long program, there will be timed class test before each class of what you have learned a day before. Every week Quiz and aptitude test along with assignment and Final Exam after each module. (Module 1: Python, Module 2: Unsupervised Learning, Module 3: Supervised Learning). You will also be expected to do a project as final submission for certificate.

Online Live Classroom Course


Course Structure

Unit 1 - Python
  • Introduction to Python Language
  • The advantages of Python over other programming languages
  • Python installation (Windows & Linux distribution for Anaconda Python)
  • Deploying Python IDE
  • Basic Python commands
  • Data types
  • Variables
  • Keywords
  • Built-in data types in Python
  • Tabs and spaces indentation
  • Code comment Pound # character
  • Variables and names
  • Python built-in data types(Numeric, int float, complex, list, tuple, set, dict)
  • Containers
  • Text sequence
  • Exceptions
  • Instances
  • Classes
  • Modules
  • Str(String)
  • Ellipsis Object
  • Null Object
  • Ellipsis
  • Debug
  • Basic operators(comparision, arithmetic, slicing and slice operator, logical, bitwise)
  • Loop and control statements(while, for, if, break, else, continue)
  • How to write OOP concepts in Python
  • Connecting to a database
  • Classes and objects in Python
  • OOPs paradigm
  • Important concepts in OOP(polymorphism, inheritance, encapsulation)
  • Python functions, return types
  • Lambda expressions
  • Connectnig to databse and pulling data
  • The xrange() function
  • List Comprehensions
  • Generator Expressions
  • Dictonaries and Sets
  • Reading data from CSV, Excel, JSON
  • Writing data to CSV, Excel, JSON, HTML
  • Reading data from database and storing in data frame
  • Writing data frame to database
  • Handling PDF files - tabula-py
  • Introduction to arrays and matrices
  • Indexing of array
  • Data types
  • Broadcasting of array math
  • Standard deviation
  • Conditional probability
  • Correlation and covariance
  • Pandas
  • Series
  • Constructing from dictionaries
  • Custom index
  • Data Frames
  • Constructing from a dictionary
  • With values as lists
  • Custom indexing
  • Rearranging the columns
  • Accessing values loc(), iloc(), at() & iat()
  • Setting values
  • Sum
  • Cumulative sum
  • Assigning a column to the data frame
  • Adding a new column
  • Deleting a column
  • Slicing
  • Indexing and advanced indexing
  • Boolean indexing
  • Transposing
  • Sort by
  • Concatenate
  • Merge
  • Inner join
  • Outer join
  • left outer join
  • Right outer join
  • Merge on columns
  • Join
  • Group By-Aggregation
  • Data munging
  • Introduction to SciPy and its function
  • Building on top of NumPy
  • Cluster
  • Linalg
  • Signal
  • Optimize
  • Integrate
  • Subpackages
  • SciPy with Bayes Theorem
  • How to plot graph and chart with Python
  • Various aspects of line
  • Scatter
  • Bar
  • Histogram
  • Subplots
  • Math function
  • Functions
  • re.match()
  • start()
  • end()
  • group()
  • re.search()
  • re.findall()
  • Regex symbols
Unit 2 - Machine Learning & Python
  • Dimension Reduction
  • Principal Component Analysis
  • Singular Value Decomposition
  • K-means Clustering
  • Introduction
  • Metrics lift, support, Confidence and conviction
  • Apriori Model
  • What is supervised Learning
  • Algorithms in Supervised Learning
  • Steps in Supervised learning
  • Regression Vs Classification
  • Accuracy Metrics
Unit 3 - Classification
  • Pros and Cons
  • Evaluation Metrics
  • Evaluation Metrics
  • Pros and Cons
  • Evaluation Metrics
  • Pros and cons
  • Kernel models
  • Evaluation Metrics
  • Pros and cons
  • Applications
  • Extracting text from website, URLs,PDF
  • Text Cleaning
  • Text clustering
  • Word cloud
  • N-grams
  • Sentiment Analysis
  • NLP

Reasons to Join this Course

Live Training Classes

Live Training Classes

Project

Project

Councellor and Forum Support

Councellor and Forum Support


Certificate

Certificate

Hands on Practice

Hands on Practice

Technical Interview Preparation during the course

Technical Interview Preparation during the course

Pursue your Dream Career with our Best Courses and expert trainers! Register Today!

Connect with us