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.
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In this course we will cover everything from the scratch to the advanced levels of Machine Learning & Deep Learning.
Take lessons from world-class professors and industry leaders in our HD online videos.
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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.
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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.
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.
Experienced Data Scientist with a demonstrated history of working in the information technology and services industry.
Polong Lin is a Data Scientist at IBM in Canada. Under the Emerging Technologies division, Polong is responsible for educating the next generation of data scientists through BDU.
Currently, he is driving several productivity programs - using data analytics to drive insights from business operations and implementing optimizations such as streamlining workflows, improving service levels, and ultimately reducing cost.
Mahdi Noorian is a Postdoctoral Fellow at the Laboratory for Systems, Software and Semantics (LS3) of the Ryerson University. He holds a Ph.D degree in Computer Science from University of New Brunswick.
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.
Hours of Content
Live Sessions
Tools and Software
The curriculum has been designed by faculty from IITs, and Expert Industry Professionals.
Hours of Content
Live Sessions
Tools and Software
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 BasicsPython 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 operationUsing 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 modulesFor 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 toolsIn 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, NumpyUnderstanding 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 StatisticsDescriptive 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 StatisticsIn 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 ProbabilityProbability 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 TestingWith 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.
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 regressionLinear 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 regressionLogistic 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 classificationk-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 machinesSupport 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 TreesThe 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 ForestsRandom 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 BoostingWhen 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 clusteringIn 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 clusteringHierarchical 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 AnalysisPCA 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.
DBSCANDensity-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.
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 - Part2You will also get a opportunity to use null values, dealing with various data and outliers preprocessing techniques to create a machine learning model.
Feature EngineeringFeature 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 SelectionFeature 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 techniquesHere you will learn different model-building techniques using different tools
Model Tuning techniquesIn this module, you can learn how to enhance model performance using advanced techniques as GridSearch CV, Randomized Search CV, cross-validation strategies, etc.
Building PipelineWhat 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.
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 ComponentsIn this module, you will learn about different components that are necessary to analyze and forecast future outcomes.
StationarityYou will learn what is stationarity and the importance of learning stationarity.
Time Series ModelsIn this module, you will learn common Time series models as AR, MA, ARIMA, etc.
Model EvaluationWhen 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 AssignmentYou will also get a chance to work on assignments and feel at ease while working on the use case scenarios.
ProjectsAlso, we are providing a few more extra projects for practice, you can assemble and compare your solutions with the ones we provide.
In the introduction module, you will learn why recommendation systems are used, their requirement, and their applications.
Understanding the relationshipIn this module, you will learn on what basis recommendation engine works and their association rules.
Types of Data in RSIn this module, you will learn all the types of data used in the Recommendation Engine.
Ratings in RSIn this module, you will learn just how the ratings are drawn in the Recommendation Engine.
Similarity and Its MeasuresRecommendation 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 EngineIn this module, you will learn different types of Recommendation Engines.
Evaluation Metrics in RecommendationOnce you build the models, you require metrics to evaluate how effective is your model. You will learn various evaluation tools in RE.
Use casesYou will also get an opportunity to focus on additional use cases. Later, you can compare your solution with the SME-provided solution.
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.
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 MLPA 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 LearningTensorFlow 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 functionsIn 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 networksA 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 classificationYou will get an opportunity to work on use cases of image classification and learn how CNN will work behind the scenes.
Transfer LearningTransfer learning is a deep learning research technique that focuses on storing and transferring knowledge received while training one model to another.
Implementing Object DetectionIn this module, you will learn about how object detection models are built.
Segmentation using CNNsEach 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.
AutoEncodersA 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 ModelThe 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.
ProjectsIn this module, you will also get an opportunity to work on multiple models.
Real-world industry projects sponsored by leading companies in a number of fields provide opportunities to learn.
The crude accelerometer and whirligig sensor information is gathered from the cell phone and smartwatch at a pace of 20Hz.
In the connected world, it is imperative that the organizations are using to Recommend their Products & Services to the People.
Based on The Data Collected from the Meteorological Department, Predicting The Air Quality Of Different Parts of The country
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.
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.
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.
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.
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.
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.
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.
Honeywell
Internshala
Firstsource Solutions
Indium Software
After my graduation, I didn't want to pursue MBA since everyone is doing it I wanted to do something different but I was confused. I opted for the PG Program in Data Science by Data Trained Education and I had an amazing journey with them, the trainers were top-notch, the course content was perfect.
Maganti IT services
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.
Analytics Vidya
HCL
MSMEx
Deqode Solutions
Quantiphi
There are 3 simple steps in the Admission Process that are detailed below
Fill up the Query Form and one of our counselors will call you & understand your eligibility.
Our Admissions Committee will review your profile. Upon qualifying, an Email will be sent to you confirming your admission to the Program.
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!
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