A brand synonymous with excellence, AP2V Gurgaon is a popular Machine Learning training institute in Gurgaon for driven and ambitious young professionals. We go the extra mile for our students and customize our sessions to accommodate busy schedules.

Known for offering unrivalled Machine Learning training in Gurgaon, our commitment to students endures past the end of the course. With placement assistance programs, we ensure you get the best start to a blossoming career.

Technological Trends in Machine Learning

The constantly changing tides of technology are sweeping the universe. Renowned scientists the world over admit that there never was a better time to study the field of Artificial Intelligence and robotics. That being said, on any occasion that you mention Big Data or even analytics, the conversation is incomplete without discussing machine learning trends. Aptly credited as the science that powers artificial intelligence as well as deep learning; machine learning has intrigued scores of individuals.

It compels them to study machine learning examples and even take a Google machine learning course. If the marvels of artificial intelligence applications and algorithms have compelled you to reconsider all that you thought you knew about the world, then it may be time to obtain a deeper insight into Andrew Ng Machine Learning.

Course Summary

Armed with a complete array of machine learning tips coming from talented instructors, AP2V Gurgaon offers you the finest training. Our industry standard classroom training in machine learning is coveted by computer science students across the country. Our machine learning classes present you with all the tools you need in addition to practical modules and e-books to master machine learning. It is precisely why so many individuals decide to practice machine learning with AP2V Gurgaon.

Course Prerequisites

Should you be interested in taking up a ML (Machine Learning) Course you must be aware of the course prerequisites. It can be helpful to having a working familiarity with Python and even practice Multivariate Calculus. Since you will have to work on algorithms, you must be proficient in Linear Algebra so as to know more about machine learning. While many choose to opt for a qualification in probability theory as well as statistical inference; these are not mandatory, but merely recommended.

FAQ’s

How to learn machine learning from scratch?

In case you’re concerned about how to start machine learning, you should know that it is a rich and diverse field. It would be prudent to study the theory as well as practice the mathematical algorithms. But most importantly, you must be guided by the right professionals who have years of experience in machine learning coaching, which is why many prefer to study introduction to machine learning with AP2V Gurgaon.

What language to use for machine learning?  

One of the main languages used in machine learning training is Python. The complex scientific is integral to machine learning algorithms and facilitates data analysis as well as matrix handling. A critical communication tool, Python is the key to the future and a major part of any Google machine learning crash course.

What is machine learning with Python?

Students of computer science regard Machine learning a futuristic branch of their field that analyses and researches the composition of algorithms that can self-learn. Archetypal tasks include concept learning in addition to function learning as well as ‘predictive modelling’. Furthermore, there is a study of clustering and recognizing extrapolative patterns.

What is the difference between artificial intelligence and machine learning?    

A number of times, Artificial Intelligence (AI) is erroneously used instead of Machine Learning (ML). However, these concepts are as complex as they are diverse. While machine learning is the basics that Artificial Intelligence is built upon, AI is the notion that believes machines are capable of executing tasks in a manner similar to or better than humans could. On the other hand, machine learning involves a present application of artificial intelligence that functions on the principles that suggest, researchers must purely offer data access to machines and allow them study and learn independently.

How to start learning machine learning?

Gaining an operational familiarity with useful programming languages is a great way to begin. You could study Python, and even Linear Algebra. However, if you seek a deeper familiarity with machine learning Concepts, it would be wise to enrol for a course in a reputed Machine Learning Institute like AP2V Gurgaon.

Why machine learning is important?

The concept of iteration is held in high regard when it comes to ML (Machine Learning). This is primarily due to the fact that introducing new information and data is what aids the models in evolving autonomously. They discover ways to improve results based on the last computation. It is how machine learning encourages adaptability to generate consistent and repeatable results each time. This field has seen great mathematical advancement in the last few years leading to promising progress.

3 Reasons to Undertake Python Machine Learning Certification with AP2V Gurgaon

With a myriad of job opportunities awaiting students of computer science, machine learning is a sought after qualification today. Here are a few reasons that compel aspirants to take a machine learning course with AP2V Gurgaon-

  1. As the most distinguished Machine Learning Institute in the country, we have a panel of enthusiastic staff combined with skilled trainers.
  2. Our placement assistance helps you land a job in leading organizations undertaking ground-breaking studies in the field of Artificial Intelligence.
  3. We focus on fully explicating the concepts of linear algebra for machine learning so that our students completely master machine learning basics.

At AP2V Gurgaon, we take great pride in our expertly designed machine learning tutorial. If you wish to hone a burgeoning career in Artificial Intelligence then simply enrol for our machine learning course in Gurgaon. You can ring us up at +91 124 4364210 or take a trip to our state-of-the-art training center based in Gurgaon. We’re happy to provide you with all the information on machine learning online course fees and more!

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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