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Machine Learning Training

This machine learning course provides a detailed overview of machine learning topics, including real-time data manipulation, the use of unsupervised learning to develop algorithms, regression analysis, classification, and time series modeling. In this machine learning training course, you'll learn how to use Python to predict from your data. This machine learning certification online course is an exciting branch of AI that will provide the skills to become a machine learning engineer and unleash the power of this emerging technology.

Machine Learning Training Overview

Machine Learning Training has several modules that have been developed by the Python that will help the programmers to implement Machine Learning skills. The process of prediction and training involves the use of algorithms that are very specific to learn Machine Learning. This Machine Learning course covers several aspects of Machine Learning using Python to enhance the skills of learners.

Topics covered in Machine Learning Training and Python

  • K-nearest neighbor classifier
  • Neural Networks
  • Neural networks from scratch in Python
  • Dropout neural networks
  • Machine Learning with Scikit and Python
  • Neural Network in Python using NumPy
  • Naive Bayes Classifier
  • Introduction into text classification using Python and Naive Bayes

Machine Learning Online Training using big data and analysis has grown much and has changed the ways that businesses operate. Python has started as a strong prospect and is used to carry out prophetic analysis on Big Data and other modules that related to it including its clarity, syntax, and easy read.

Python combines a very clear syntax with great functionality. Python can also be used as an extension language for applications written in easy-to-use scripts or in other languages that require automated interfaces. Python is widely recognized as the language of choice for teaching and learning ml (machine learning). Python programming language is potent which helps to build several algorithms. These algorithms are used for intelligent and smart machines that can complete the work without human interruption.

Machine Learning Training With Python

Python with Machine Learning is evolving and improves with the help of new data available. Machine learning is used in different types of industries such as healthcare, banking, software, insurance, and several other industries. The industry of machine learning is growing and this trend is considered to sweep in the near future. Master Machine Learning course from our course offered and you will become a part of the revolution in technology that helps to shape the future world.

In this Machine Learning Training, you will learn and understand the behavior of a data as you build models way with the help of Machine Learning using Python. Tech Center Point will help you to learn about all different libraries that are offered by Python, and they help to manipulate, visualize and reprocess data.

Prerequisites to Learn Machine Learning

Machine Learning Training form Tech Center Point is open to everyone who is keen on machine learning. You don’t need any special skills or other degrees to enroll in this machine learning course. However, to better understand the course of machine learning, it is important to understand the following concepts in advance:

  1. Basic knowledge of linear algebra.
  2. The programming experience will help you understand the problem.
  3. Knowledge of statistical and probability models.

Before you go to the machine learning online course, you need to understand these basic courses, such as Python for data science, math review, and basic data science statistics.

Who is eligible for Machine Learning Training?

  1. Degree Holders & Job Seekers
  2. Business Analysts
  3. Developers
  4. IT Professionals who are interested in taking the Machine Learning career.
  5. Analytics Experts
  6. Network Admins
  7. Data Science Career Desired Professionals

Enroll Today For Free Webinar on Machine Learning Online Training..!

 

1
Machine Learning Certification Training using Python

Module 1: Introduction

  • What is Data, Machine Learning, Data Science, Big data, and why is it so important?
  • What are the Predictive Analysis, Data Mining, Soft Computing, Neural Network, Deep learning and where we use
  • Tools and technologies used in data science and machine learning.
  • Key Takeaways
  • Interview question
  • Case study

Module 2: Basics

  • Installation of Python and R framework and packages
  • Introduction functions, classes, packages, libraries, data types, operators, Mathematics in R and Python
  • Writing/Running python and R programs using various functions, classes and so on

Module 3: Debugging and Troubleshooting Applications

  • Use breakpoints to inspect a code during runtime
  • Install missing dependencies
  • Read and decipher log error messages

Module 4: Data science and concepts

  • Statistics, Types of Data measurements
  • Variables, Categorical and Quantitative Variables
  • Descriptive Statistics
  • Inferential Statistics

Module 5: Regression

  • Understanding Regression and Analysis Part
  • Simple linear Regression
  • Multiple Regression
  • Logistic Regression.
  • Polynomial Regression

Module 6: Classification

  • Decision Tree
  • Random Forest
  • Evaluating Classification Models Performance using Confusion Matrix, CAP Curve, and Accuracy

Module 7: Ensemble Modeling

  • Random Forest
  • Bagging, Boosting,
  • Tree-based modeling

Module 8: Rule-Based Learning

  • Apriori
  • Eclat

Module 9: Deep Learning

  • Artificial neural networks
  • Convolution neural networks

Module 10: Dimensionality Reeducation Techniques

  • Principal Component Analysis
  • Kernel PCA

Module 11: Model Selection and boosting

  • XGBoost


You can view and review the lecture materials indefinitely, like an on-demand channel.
Definitely! If you have an internet connection, courses on tech center point are available on any device at any time.
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Machine Learning Training

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  • Lifetime access with high-quality content and class recordings
  • 60 hours of course presentations by hands-on experts
  • 40 hours of lab time
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