Machine Learning using Python


Description

The Machine Learning Course that dives deeper into the basic knowledge of the technology using one of the most popular and well-known language, i.e. Python. During this course, students will be taught about the significance of the Machine Learning and its applicability in the real world. Secondly, the machine learning online course will give you a proper overview about Machine Learning topics such as its algorithms, model evaluation as well as supervised vs unsupervised learning.

Who is the right candidate for the course?

  • Anyone who is keen to learn machine learning algorithm using Python
  • Any person who wants to learn about practical application of machine learning to solve real world problems
  • Individuals with basic knowledge of Machine Learning who want to develop their understanding of the machine learning algorithms
  • Intermediate EXCEL users not able to work with large datasets
  • Anyone looking to start a career as a data scientist
  • Individuals who want to utilize and apply the technology of Machine learning to their domain

  • Training | Skills

    Self-paced and live sessions by working professionals.

  • Certification | Recognition

    International certifications to prove your skills among the crowd.

  • Internship | Experience

    Exposure with live projects from the industry.

Available Modes

and deliverables

Modes Training videos Co-Branded Certification Internship MTA Certification Live Mentorship Price

$50


25-35 Hours

$80


25-35 Hours


16
Hours

$100

Register

How It Works

STEP 1: Proceed to register from here.
STEP 2: At the payment page, you'll be asked for your email address. Enter the email ID which you'll also be using to create an account on Foxmula.
STEP 3: After successful payment of $20, Create an account (Signup) on Foxmula-Y
STEP 4: Login to you account in Foxmula-Y and then go to Dashboard. Your program will start reflecting in 1-2 working hours.

Curriculum

    • Perform data operations using Data Types and Operators
    • Control Flow with Decision and Loops
    • Perform Input and Output Operations
    • Document and Structure Code
    • Perform Troubleshooting and Error Handling
    • Perform Operations using Modules and Tools
    • Data Preprocessing: Missing Data, Categorical Data & Feature Scaling
    • Regression I: Linear Regression, Multiple Regression, Polynomial Regression
    • Regression II: Logistic Regression, K-Nearest Neighbors
    • Support Vector Machines (SVM) & Kernel SVM
    • Clustering: K-Means and Hierarchical
    • Natural Language Processing (NLP)
    • Intro to Neural Networks: Artificial Neural Networks (ANN)
    • Enterprise Application of Machine Learning
    • Difference between Python2 and Python3
    • Print function and Strings
    • Math function and programming basics
    • Variables and Loops introduction
    • Loops detailed
    • Functions and Function Parameters
    • Global and Local Variables
    • Packages and Modules with PIP
    • Writing/Reading/Appending to a file
    • Common pythonic errors
    • Getting user Input
    • Stats with python
    • Module Import
    • List and Multidimensional lists
    • Reading from CSV
    • Multi Line Print
    • Dictionaries
    • Built in functions
    • Built in Modules
    • Introduction to pandas
    • Pandas basics
    • Concatenating and appending dataframes
    • What is Machine Learning?
    • Difference between a rule based algorithm and a machine learning algorithm
    • Supervised vs Unsupervised learning
    • Classification vs Regression
    • Practical Machine Learning
    • Training and testing Data
    • Features and labels
    • Linear Regression
    • Logistic Regression
    • KNN classification
    • Support Vector Machines
    • K—Means Clustering
    • Random Forest
    • Implementation of all the algorithms usingSKlearn
    • Introduction to NLTK
    • Stopwords
    • Stemming
    • Lemmetization
    • Text classification
    • Part of speechtag