Enrol at AP2V Noida’s machine learning course in Noida and learn the best of automation technologies to build a fantastic career. Machine learning applications are rapidly growing in various industries, giving ample of opportunities for professionals to boost their career in machine learning. AP2V Noida is a leading machine learning training institute in Noida, offering efficient classes, placement support and experienced trainers to our students. If you’re looking for a cost-effective machine learning training in Noida, come to AP2V Noida and make your dreams come true!

 

Why Learn Machine Learning?

Machine learning is the science to get a computer to act without being specifically programmed. In the last decade, machine learning has brought us self-driving cars, speech recognition, effective online search and a deeper understanding of the human genome. Machine learning techniques are rapidly taking over the world and thus, there is a huge demand for professionals who know machine learning concepts inside out. The machine learning industry is expected to grow from $1.03 billion in 2016 to $8.81 billion in the year 2020. The demand for top machine learning engineers will grow 60% by 2020 and the top industries embracing machine learning strategy will be robotics, e-commerce, IoT and social media. The average salary for machine learning professionals is around $201k annually. Most in-demand job roles include data scientist, machine learning engineer, research scientist, and research engineer. When you complete your machine learning coaching in Noida with AP2V Noida, you can gear up for a futuristic career with excellent opportunities and a higher pay scale.

All about our Machine Learning Training:

Whether you choose a machine learning online course or a classroom training in machine learning, our trainers support you through every stage of the learning process. You will thoroughly understand the machine learning fundamentals and practice machine learning with real-life case studies so that you are prepared to handle the complex challenges in a corporate setup.

 

Machine Learning Course Contents:

  • Machine learning introduction and artificial intelligence concepts

  • The various techniques of machine learning

  • Data pre-processing

  • Math refresher

  • Supervised learning

  • Regression vs. classification

  • Linear regression

  • Logistic regression

  • Decision trees

  • Unsupervised learning

  • K-means clustering

  • Python fundamentals

  • Scientific packages

  • And much more

 

Who are the Right Candidates for the Machine Learning Coaching?

  • Developers who are looking to upskill their machine learning industry knowledge

  • Analytics managers who lead their team of analysts

  • Business analysts aspiring to understand the various data science techniques

  • Information architects who want to become masters in machine learning algorithms

  • Analytics professionals who want to work in ML or AI

  • Graduates who want to build their career in data science and machine learning

  • Experienced professionals who want to leverage machine learning tips in their fields to get additional insights and profits

 

Key Skills You Will Acquire After Completing Our Machine Learning Course:

Computer science programming and fundamentals – By practising programming and taking part in coding competitions and hackathons, we’ll help you hone your skills.

Statistics and probability – These are the heart of machine learning algorithms and are necessary to analyse data and build and validate models from observed data

Data modelling and evaluation – Required to find useful patterns like clusters and correlations in a set of data and evaluate how good a particular model is. These skills are required to apply even standard algorithms and will help you greatly in your career.

Application of machine learning algorithms and libraries – Choosing the right model to fit the data and understanding how hyper-parameters affect learning are important skills to acquire in order to grab advanced machine learning jobs.

Software engineering and system design – Understand the ecosystem of products and services along with using software efficiently. Learn best practices of software engineering to ensure productivity, collaboration, maintainability and quality.

FAQs

How to start learning machine learning?

If you are wondering how to learn machine learning, enrol in our machine learning certification course and let our trainers guide you throughout the learning process. By building a foundation of programming, statistics, and math, you’ll be able to make the most of your machine learning coaching at AP2V Noida.

 

What language to use for machine learning?

Most machine learning experts will tell you that Python is the best language for machine learning. R and MATLAB are also used by some engineers but overall, Python is the most preferred language as it is easy to learn, data-friendly and concise. It is the ideal language for data analysis and is widely used by data scientists and analysts around the world.

 

What is the difference between artificial intelligence and machine learning?

Artificial intelligence is a concept that machines can carry out tasks that humans would consider smart, while machine learning is an application of AI that is based on the concept that we should give machines access to data and let them learn on the basis of the given information. There’s a lot of buzz in the market related to machine learning, artificial intelligence and deep learning and sometimes, they are used interchangeably. However, all three have different concepts and ideas and do different things.

 

 

The Advantages of AP2V Noida’s Machine Learning Course

  • In just a few weeks’ time, you can learn machine learning tutorial Python and become an expert from a beginner

  • All our trainers have been certified in machine learning and teach you using real-life case studies, practical assignments and lots of e-books, Andrew Ng machine learning books and course material and video tutorials

  • Our trainers will also help you after completion of the course if you have any doubts and will offer placement assistance too

  • We also give lifetime access to the recorded training videos so your learning always stays with you

  • Our classrooms and labs are equipped with advanced technologies and modern hardware and software

  • You have access to Wi-Fi 24x7

  • We offer in-depth education and lots of practical sessions of supervised and unsupervised learning

  • We also give you access to machine learning tutorial Java, machine learning tutorial Point, machine learning tutorial R and machine learning tutorial C# to brush up on your programming language skills

  • After completing the course, you can take a giant leap towards the future of data analysis

It’s no surprise that machine learning is the future of technology. It will change the way we work and live. Thus, for professionals who want to be a part of this changing technology and contribute towards making lives simpler and smarter, the best machine learning course is here! If you’re looking for machine learning help online or want to get a machine learning certification, come to AP2V Noida and let us train you for the best career in the industry. Call us today to book your classes!

Lessons

1

Introduction to Machine Learning

Lesson : 1 | Duration 1.5 hours

  • Introduction
  • What is Artificial Intelligence?
  • What is Machine Learning?
  • Why Python for Machine Learning?
2

Introduction to Script

Lesson : 2 | Duration 1.5 hours

  • Course Overview
  • What is Script, program?
  • Types of Scripts
  • Difference between Script and Programming Languages
3

Environment for ML

Lesson : 3 | Duration 1.5 hours

  • Install Python IDE | IDE - Sublime Text
  • Python Download and Installation on Windows, Linux and Mac
  • Execute the Script
  • Interactive and Script Mode
  • Python File Extensions
  • SETTING PATH IN Windows
  • Python Comments
  • Quit the Python Shell
4

Python

Lesson : 4 | Duration 1.5 hours

  • What is Python?
  • Why Python?
  • Who Uses Python?
  • Interpreted languages
  • Advantages and disadvantages
  • Downloading and installing
  • Running standalone scripts under Linux
  • Date Types
  • String
  • Numbers
  • Tuple
  • Lists
  • Dictionaries
  • The if, else, and elif statements
  • for and while loops
  • Syntax of function definition
  • Modules
  • What is a module?
  • The import statement
  • Packages
  • Virtual environment
  • Exercise(s)
5

NumPy

Lesson : 5 | Duration 1.5 hours

  • Introduction
  • Ndarray Object
  • Data types
  • Array attributes
  • Array Creation Routines
  • Array from Existing Data
  • Indexing & Slicing
  • Sort, Search, & Counting Functions
  • Exercise(s)
6

Pandas

Lesson : 6 | Duration 1.5 hours

  • Introduction
  • Data structures
  • Series
  • DataFrame
  • Panel
  • Basic Functionally
  • Reindexing
  • Iteration
  • Sorting
  • Working with Text Data
  • Exercise(s)
7

Matplotlib: Python Plotting

Lesson : 7 | Duration 1.5 hours

  • Introduction
  • Matplotlib vs pyplot vs and pylab
  • Data For Matplotlib Plots
  • Create Your Plot
  • Subplot
  • add_axes() and add_subplot()
  • Work with Size of Figures
  • Plotting Routines
  • Customizing PyPlot
  • Showing, Saving And Closing Your Plot
  • Exercise(s)
8

Seaborn

Lesson : 8 | Duration 1.5 hours

  • Introduction
  • Datasets and Libraries
  • Figure Aesthetic
  • Color Palette
  • Histogram
  • Kernel Density Estimates
  • Visualizing Pairwise Relationship
  • Plotting Categorical Data
  • Exercise(s)
9

Sklearn

Lesson : 9 | Duration 1.5 hours

  • Introduction
  • Loading an example dataset
  • Shape of the data arrays
  • Learning and predicting
  • Model persistence
  • Conventions
  • Refitting and updating parameters
  • Exercise(s)
10

Supervised Learning

Lesson : 10 | Duration 1.5 hours

  • Introduction
  • Classification
  • Methods in Classification
  • Selecting Classification Methods
  • Implementing KNN in Scikit-Learn on IRIS dataset
  • K-Nearest Neighbors in scikit-learn
  • Regression
  • Regression Models
  • Linear Regression
  • Logistic Regression
  • Polynomial Regression
  • Implementation of Linear Regression
  • Exercise(s)
11

Unsupervised Learning

Lesson : 11 | Duration 1.5 hours

  • Introduction
  • Supervised Vs Unsupervised Learning
  • Terminology
  • Preparing data
  • Clustering
  • K-Means Clustering
  • Hierarchical Clustering
  • K Means vs Hierarchical clustering
  • t-SNE Clustering
  • DBSCAN Clustering
  • Exercise(s)
12

Artificial Neural Network

Lesson : 12 | Duration 1.5 hours

  • Introduction
  • Biological Neuron
  • ANN versus BNN
  • Model of Artificial Neural Network
  • Network Topology
  • Training the Neural Network
  • Feedforward
  • Loss Function
  • Backpropagation
  • Exercise(s)
13

Natural Language Processing

Lesson : 13 | Duration 1.5 hours

  • Introduction
  • Libraries
  • Installing
  • Tokenize
  • Working with Stop Words
  • Get Antonyms
  • Word Stemming
  • Lemmatizing
  • Stemming vs Lemmatization
  • Sentiment analysis with Reviews
  • Exercise(s)
14

TensorFlow

Lesson : 14 | Duration 1.5 hours

  • Introduction
  • Graphs and Tensors
  • Sessions
  • Example of TensorFlow with Python
  • Exercise(s)
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