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PG Program in Machine Learning and Deep Learning

Become an Industry-ready Certified Machine Learning and Deep Learning specialist by immersing from scratch in Machine Learning & Deep Learning Concepts with this Job Assistance Program

Become an Industry-ready Certified Machine Learning and Deep Learning specialist by immersing from scratch in Machine Learning & Deep Learning Concepts with this Job Assistance Program

22 Mar, 2024

Next Batch
starts on

8 Months

Recommended
18-20 hrs/week

Online

Learning
Format

12,000

Career
Transformed

950+

Hiring
Partners

Program Overview

India's and the world's best Machine Learning and Deep Leaning online program. Learn how to use the most in-demand Machine Learning & Deep Learning tools, techniques, and technologies.

Key Highlights

  • Sessions with industry mentors on a one-on-one basisSessions with industry mentors on a one-on-one basis
  • 100% Placement Assistance100% Placement Assistance
  • Instant Doubt ClearingInstant Doubt Clearing
  • Ideal for both Professionals and College GraduatesIdeal for both Professionals and College Graduates
  • 40+ Case Studies and Projects40+ Case Studies and Projects
  • Interactive Learning with Flexible TimingInteractive Learning with Flexible Timing

PG Program in Machine Learning and Deep Learning

  1. $ 2,500
    • Best online Data science course
    • Best online Data science course
    • Best online Data science course
    • Best online Data science course
    12000+ learners
Features
  • 400+ hours of learning
  • Practice Test Included
  • Instant doubt clearing
  • Certificate on completion

Languages and Tools covered

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8 Months Post Graduate Program in Machine Learning and Deep Learning

Being certified for 2 world-class Certifications gives your Resume an additional boost.

  • Certification on Course completion from DataTrained Education
  • Certification on Internship & Project completion from DataTrained Education

What’s the Objective of this course?

In this course we will cover everything from the scratch to the advanced levels of Machine Learning & Deep Learning.

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Learn Anything, Anytime, Anywhere

Take lessons from world-class professors and industry leaders in our HD online videos.

Dedicated Career Assistance- data science program institute

Dedicated Career Support

Get one-on-one career guidance and practise mock interviews with hiring managers. Boost your career working with 950+ recruiting partners.

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Student Support

Chat helpdesk for Quick Doubt Resolution is available from 6 a.m. to 11 p.m. IST. Program managers are available on call, chat, and ticket during business hours.

Instructors

Learn essential skill from the industry's top leaders by enrolling in DataTrained certified curriculum.

Dr. Deepika Sharma - Training Head, DataTrained

Dr. Deepika Sharma

Training Head, DataTrained

Dr. Deepika Sharma has been associated with academics /corporate education for more than 14 years. She has a deep passion in the field of Artificial Intelligence, Data Science, and Machine Learning.

Shankargouda Tegginmani - Data Scientist, Accenture

Shankargouda Tegginmani

Data Scientist, Accenture

Shankar is a Data Scientist with 14 Years of Experience. His current employment is with Accenture and has experience in telecom, healthcare, finance and banking products.

Sanket Maheshwari - Data Scientist, Faasos

Sanket Maheshwari

Data Scientist, Faasos

Experienced Data Scientist with a demonstrated history of working in the information technology and services industry.

ML and DL Course Syllabus

Best-in-class content in the form of pre-recorded HD videos, live sessions, case studies, projects, assignments, and industry webinars from leading faculty and industry experts.

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Detailed Syllabus of ML and DL Course

  • 400+ Hours of Content - data science program institute
  • 400+

    Hours of Content

  • 200+ Live Sessions - data science online training
  • 200+

    Live Sessions

  • 15 Tools and Software - best tools for data science
  • 15

    Tools and Software

Comprehensive Curriculum

The curriculum has been designed by faculty from IITs, and Expert Industry Professionals.

100+ Hours of Content - data science program institute
400+

Hours of Content

100+ Live Sessions - data science online training
200+

Live Sessions

15 Tools and Software - best tools for data science
15

Tools and Software

Module 1 Foundations

The Foundations bundle comprises 2 courses where you will learn to tackle Statistics and Coding head-on. These 2 courses create a strong base for us to go through the rest of the tour with ease.

This course will introduce you to the world of Python programming language that is widely used in Artificial Intelligence and Machine Learning. We will start with basic ideas before going on to the language's important vocabulary as search phrases, syntax, or sentence building. This course will take you from the basic principles of AI and ML to the crucial ideas with Python, among the most widely used and effective programming languages in the present market. In simple terms, Python is like the English language.

Python Basics

Python is a popular high-level programming language with a simple, easy-to-understand syntax that focuses on readability. This module will guide you through the whole foundations of Python programming, culminating in the execution of your 1st Python program.

Anaconda Installation - Jupyter notebook operation

Using Jupyter Notebook, you will learn how to use Python for Artificial Intelligence and Machine Learning. We can create and share documents with narrative prose, visualizations, mathematics, and live code using this open-source online tool.

Python functions, packages and other modules

For code reusability and software modularity, functions & packages are used. In this module, you will learn how you can comprehend and use Python functions and packages for AI.

NumPy, Pandas, Visualization tools

In this module, you will learn how to use Pandas, Matplotlib, NumPy, and Seaborn to explore data sets. These are the most frequently used Python libraries. You'll also find out how to present tons of your data in simple graphs with Python libraries as Seaborn and Matplotlib.

Working with various data structures in Python, Pandas, Numpy

Understanding Data Structures is among the core components in Data Science. Additionally, data structure assists AI and ML in voice & image processing. In this module, you will learn about data structures such as Data Frames, Tuples, Lists, and arrays, & precisely how to implement them in Python.

In this module, you will learn about the words and ideas that are important to Exploratory Data Analysis and Machine Learning. You will study a specific set of tools required to assess and extract meaningful insights from data, from a simple average to the advanced process of finding statistical evidence to support or even reject wild guesses & hypotheses.

Descriptive Statistics

Descriptive Statistics is the study of data analysis that involves describing and summarising different data sets. It can be any sample of a world's production or the salaries of employees. This module will teach you how to use Python to learn Descriptive Statistics for Machine Learning.

Inferential Statistics

In this module, you will use Python to study the core ideas of using data for estimating and evaluating hypotheses. You will also learn how you can get the insight of a large population or employees of any company which can't be achieved manually.

Probability & Conditional Probability

Probability is a quantitative tool for examining unpredictability, as the possibility of an event occurring in a random occurrence. The probability of an event occurring because of the occurrence of several other occurrences is recognized as conditional probability. You will learn Probability and Conditional Probability in Python for Machine Learning in this module.

Hypothesis Testing

With this module, you will learn how to use Python for Hypothesis Testing in Machine Learning. In Applied Statistics, hypothesis testing is among the crucial steps for conducting experiments based on the observed data.

Module 2 Machine Learning

Machine Learning is a part of artificial intelligence that allows software programs to boost their prediction accuracy without simply being expressly designed to do so. You will learn all the Machine Learning methods from fundamental to advanced, and the most frequently used Classical ML algorithms that fall into all of the categories.

With this module, you will learn supervised machine learning algorithms, the way they operate, and what applications they can be used for - Classification and Regression.

Linear Regression - Simple, Multiple regression

Linear Regression is one of the most popular Machine Learning algorithms for predictive studies, leading to the very best benefits. It is an algorithm that assumes the dependent and independent variables have a linear connection.

Logistic regression

Logistic Regression is one of the most popular machine learning algorithms. It is a fundamental classification technique that uses independent variables to predict binary data like 0 or 1, positive or negative , true or false, etc. In this module, you will learn all of the Logistic Regression concepts that are used in Machine Learning.

K-NN classification

k-Nearest Neighbours (Knn) is another widely used Classification algorithm, it is a basic machine learning algorithm for addressing regression and classification problems. With this module, you will learn how to use this algorithm. You will also understand the reason why it is known as the Lazy algorithm. Interesting Right?

Support vector machines

Support Vector Machine (SVM) is another important machine learning technique for regression and classification problems. In this module, you will learn how to apply the algorithm into practice and understand several ways of classifying the data.

We explore beyond the limits of supervised standalone models in this Machine Learning online course and then discover a number of ways to address them, for example Ensemble approaches.

Decision Trees

The Decision Tree algorithm is an important part of the supervised learning algorithms family. The decision tree approach can be used to resolve regression and classification problems unlike others. By learning simple decision rules inferred from previous data, the goal of using a Decision Tree is constructing a training type that will be used to predict the class or value of the target varying.

Random Forests

Random Forest is a common supervised learning technique. It consists of multiple decision trees on the different subsets of the initial dataset. The average is then calculated to enhance the dataset's prediction accuracy.

Bagging and Boosting

When the aim is to decrease the variance of a decision tree classifier, bagging is implemented. The average of all predictions from several trees is used, that is a lot more dependable than a single decision tree classifier.

Boosting is a technique for generating a set of predictions. Learners are taught gradually in this technique, with early learners fitting basic models to the data and consequently analyzing the data for errors.

In this module, you will study what Unsupervised Learning algorithms are, how they operate, and what applications they can be used for - Clustering and Dimensionality Reduction, and so on.

K-means clustering

In Machine Learning or even Data Science, K-means clustering is a common unsupervised learning method for managing clustering problems. In this module, you will learn how the algorithm works and how you can use it.

Hierarchical clustering

Hierarchical Clustering is a machine learning algorithm for creating a bunch hierarchy or tree-like structure. It is used to group a set of unlabeled datasets into a bunch in a hierarchical framework. This module will help you to use this technique.

Principal Component Analysis

PCA is a Dimensional Reduction technique for reducing a model's complexity, like reducing the number of input variables in a predictive model to avoid overfitting. Dimension Reduction PCA is also a well-known ML approach in Python, and this module will cover all that you need to know about this.

DBSCAN

Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used to identify arbitrary-shaped clusters and clusters with sound. You will learn how this algorithm will help us to identify odd ones out from the group.

Module 3 Advanced Techniques
EDA - Part1

Exploratory Data Analysis (EDA) is a procedure of analyzing the data using different tools and techniques. You will learn data standardization and represent the data through different graphs to assess and make decisions for several business use cases. You will also learn all the essential encoding techniques.

EDA - Part2

You will also get a opportunity to use null values, dealing with various data and outliers preprocessing techniques to create a machine learning model.

Feature Engineering

Feature Engineering is the process of extracting features from an organization's raw data by using domain expertise. A feature is a property shared by independent units that can be used for prediction or analysis. With this module, you will learn how this works.

Feature Selection

Feature selection is also called attribute selection, variable selection, or variable subset selection. It is the process of selecting a subset of relevant features for use in model development. You can learn many techniques to do the feature selection.

Model building techniques

Here you will learn different model-building techniques using different tools

Model Tuning techniques

In this module, you can learn how to enhance model performance using advanced techniques as GridSearch CV, Randomized Search CV, cross-validation strategies, etc.

Building Pipeline

What is Modeling Pipeline and how does it work? Well, it is a set of data preparation steps, modeling functions, and prediction transform routines organized in a logical order. It allows you to specify, evaluate, and use a series of measures as an atomic unit.

Module 4 Time Series Analysis
Introduction

A time series is a set of data points that appear in a specific order over a specific time. A time series in investing records the movement of selected data points, like the cost of security, with a set period of time, with data points collected at regular intervals.

Time Series Components

In this module, you will learn about different components that are necessary to analyze and forecast future outcomes.

Stationarity

You will learn what is stationarity and the importance of learning stationarity.

Time Series Models

In this module, you will learn common Time series models as AR, MA, ARIMA, etc.

Model Evaluation

When you build models, you will use different evaluation methods to gauge the product performance or even accuracy. Yes, In this module, you will learn model evaluation methods.

Use Case and Assignment

You will also get a chance to work on assignments and feel at ease while working on the use case scenarios.

Projects

Also, we are providing a few more extra projects for practice, you can assemble and compare your solutions with the ones we provide.

Module 5 Recommendation Engine
Introduction

In the introduction module, you will learn why recommendation systems are used, their requirement, and their applications.

Understanding the relationship

In this module, you will learn on what basis recommendation engine works and their association rules.

Types of Data in RS

In this module, you will learn all the types of data used in the Recommendation Engine.

Ratings in RS

In this module, you will learn just how the ratings are drawn in the Recommendation Engine.

Similarity and Its Measures

Recommendation systems work on the basis of similarity between the product and the consumers who view it. There are many ways for determining how similar 2 products are. This similarity matrix is used by recommendation systems to recommend the next most comparable product to the customer.

Types of Recommendation Engine

In this module, you will learn different types of Recommendation Engines.

Evaluation Metrics in Recommendation

Once you build the models, you require metrics to evaluate how effective is your model. You will learn various evaluation tools in RE.

Use cases

You will also get an opportunity to focus on additional use cases. Later, you can compare your solution with the SME-provided solution.

Module 6 Introduction to Deep Learning

In this introduction module, we look at the different components of a neural network, starting with adopting the phrases of Neural Networking. Install and familiarize yourself using the TensorFlow library, enjoy Keras' simplicity, then use Keras to create a strong neural network model for a classification problem. Also, you will learn how to fine-tune a Deep Neural Network.

Practical case of MLP

A multi-layer perceptron is a mathematical model of a biological neuron or an artificial neuron. A neural network is a computing system based on the human brain's organic neural networking. In this module, you will learn about all of the neural network's uses and perception.

Practical case of MLP

A multi-layer perceptron is a mathematical model of a biological neuron or an artificial neuron. A neural network is a computing system based on the human brain's organic neural networking. In this module, you will learn about all of the neural network's uses and perception.

Tensor Flow & Keras for Neural Networks and Deep Learning

TensorFlow is an open-source library for numerical computing and machine learning that was introduced by Google. Keras is a robust open-source API for building & evaluating deep learning models. In this module, you will learn how to set up Keras and TensorFlow from the starting. In Python, these libraries are often used for AI & ML.

Activation and Loss functions

In this module, you will learn how the Activation Function is used in defining a neural network's paper from many inputs. The Loss Function is a technique for predicting neural community error.

Convolution neural networks

A Convolutional Neural Community (CNN) is a kind of artificial neural network. In this module, you will learn about image recognition and processing that is specially developed to process pixel data.

Practical Cases of CNN in image classification

You will get an opportunity to work on use cases of image classification and learn how CNN will work behind the scenes.

Transfer Learning

Transfer learning is a deep learning research technique that focuses on storing and transferring knowledge received while training one model to another.

Implementing Object Detection

In this module, you will learn about how object detection models are built.

Segmentation using CNNs

Each pixel in an image is labeled with a unique class in image segmentation. Dense prediction is another name for this pixel labeling problem. In this module, you will learn how image segmentation is performend.

AutoEncoders

A neural network model called autoencoder is designed to master a compressed representation of the input. A neural network that has been taught to replicate the input to its output is called as an autoencoder.

Sequence Based Model

The sequence based model accepts a sequence of objects (words, time series, characters, etc.) and develops another sequence. Model Seq2Seq. The input is a sequence of words, and the output is the translated series of words in the Neural Machine Translation.

Projects

In this module, you will also get an opportunity to work on multiple models.

Industry Projects

Real-world industry projects sponsored by leading companies in a number of fields provide opportunities to learn.

  • Data science program Engage in collaborative projects and learn from peers
  • Data science programMentoring by industry experts to learn and apply better
  • Data science programPersonalized subjective feedback on your submissions to facilitate improvement
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Smartphone and Smartwatch Activity

The crude accelerometer and whirligig sensor information is gathered from the cell phone and smartwatch at a pace of 20Hz.

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Recommendation System

In the connected world, it is imperative that the organizations are using to Recommend their Products & Services to the People.

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Air Quality Study

Based on The Data Collected from the Meteorological Department, Predicting The Air Quality Of Different Parts of The country

Why DataTrained for Machine Learning & Deep Learning Program in India?

We have over 950+ hiring partners and ensures that all of the students are placed. The programme is designed for a career within 8 months with the help of an industry-prepared syllabus.

Resume pepration by DataTrained

Partnered with IIMJobs wherein you get access to their paid resume preparation kit and personal feedback from the industry HR experts. An individual career profile is prepared by our experts so that it suits his/her experience and makes it relevant to a Data Scientist role.

Interview Preparation DataTrained

Mentors conduct regular mock HR and technical interviews, providing personal guidance and mentoring.The industry mentor assists learners to complete projects and upgrade the status bar so that their resume appears competitive to employers.

100% Placement Assistance

Every individual's Ability Score is generated, and it is forwarded to over 950+ recruitment partner businesses. To place our students, we organize campus placements in Noida, Gurgaon, Ahmedabad, Bangalore, and Chennai.

Career Impact

DataTrained presents the best online Machine Learning & Deep Learning Program in India. With 10,000+ careers transformed.

DataTrained has helped me with the vital knowledge and skills that are needed for a data scientist role. The trainer starts with an example to make us comprehend the concept and then help us build the Algorithms with the real industry datasets.DataTrained brings the power of online learning along with dedicated Mentorship, Counselling, Live Sessions and 6 months Internship.

Aaruni Khare - Data Scientist
Aruni Khare Data Scientist, RBS

I saw an ad from DataTrained on facebook and I contacted them straight away and enquired about their Data Science online course. Their counselor took me through the complete journey of what they offer and what is data science all about. After continuous conversation for a few weeks, I was pretty sure about the course and now I knew where I need to invest my money and hard work.

Rakshit Jain - Data Scientist, Optum
Rakshit Jain Data Scientist, Optum

The program is a well-balanced mix of pre-recorded classes, live sessions on weekends and printed reading materials they sent to my address. My mentor was Amit Kaushik and he helped me in getting that confidence and completing my assignments on time.I have almost completed the course and have been able to crack Glenmark interview.Thank you so much DataTrained.

Rupam Kumar Chaurasia - Head Sales, Glenmark
Rupam Kumar Chaurasia Head Sales, Glenmark

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I can certainly say the content they are offering is really good. Assignments are relatable. Completing the assignments helps in a better understanding of the module. In a nutshell, I would recommend this course to anyone interested in Data Science.

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Admission Process

There are 3 simple steps in the Admission Process that are detailed below

Step 1: Fill in a Query Form

Fill up the Query Form and one of our counselors will call you & understand your eligibility.

Step 2: Get Shortlisted & Receive a Call

Our Admissions Committee will review your profile. Upon qualifying, an Email will be sent to you confirming your admission to the Program.

Step 3: Block your Seat & Begin the Prep Course

Block your seat with a payment of INR 10,000 to enroll in the program. Begin with your Prep course and start your Machine Learning & Deep Learning journey!

ML and DL Course Fee

$ 2,500

No Cost EMI options are also available. *

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What's included in the price

Access to real-life projects

Access to domain specific mentorship

Access to career assist by IIMJobs

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