Data Science with Machine Learning Training in Chennai

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Softlogic Course Duration


4 to 8 Months

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Learning data science will assist you comprehend how to take the raw data, evaluate it, and make some interesting discoveries. When you learn machine learning along with it, you will become an expert in artificial intelligence. Want to become a data scientist and machine learning expert? Then it’s time to enroll in the Top Data Science with Machine Learning Training Institute in Chennai.

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  • Technology Training
  • Aptitude Training
  • Learn to Code (Codeathon)
  • Real Time Projects
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  • Panel Mock Interview
  • Unlimited Interviews
  • Life Long Placement Support

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  • A Personalized Learning Experience with Direct Trainer Engagement!
  • Direct Interaction with the Trainer
  • Clarify doubts then and there
  • Airconditioned Premium Classrooms and Lab with all amenities
  • Codeathon Practices
  • Direct Aptitude Training
  • Live Interview Skills Training
  • Direct Panel Mock Interviews
  • Campus Drives
  • 100% Placement Support

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  • Instructor Led Live Training! Learn at the comfort of your home
  • No Recorded Sessions
  • Live Virtual Interaction with the Trainer
  • Clarify doubts then and there virtually
  • Live Virtual Interview Skills Training
  • Live Virtual Aptitude Training
  • Online Panel Mock Interviews
  • 100% Placement Support

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Corporate Training

  • Blended Delivery model (both Online and Offline as per Clients’ requirement)
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Course Highlights


Data science has evolved as an extension of statistics that is capable of handling the huge sums of data through computer science technologies. Generally, Machine Learning is confused with Data Science which is wrong. Even though Machine Learning is a prominent area of Data Science it is not the only one. Data science encompasses a huge spectrum of data technologies comprising Python, SQL, Spark, Hadoop etc. Through the Data Science with Machine Learning Certification training in Chennai you will learn the subtle differences between the two and what is their importance.

Perhaps the most famous data science methodologies arise from machine learning. The difference between machine learning and other computer guided decision processes is that the former develops prediction applying data. Some of the most famous products that utilize machine learning comprise the handwriting readers deployed by the postal service, movie recommendation systems, and speech recognition and spam detectors. In the Data Science with Machine Learning classes in Chennai you will get knowledge of popular machine learning algorithm and other factors.

To be precise, machine learning is valuable part of data science. In one way, we can tell that machine learning never would take place without big data.

Softlogic’s Machine Learning and Data Science course will assist you ace the data science and analytics applying various machine learning techniques.

  • Data Analyst
  • Data Scientist who is willing to deploy Predictive Modeling
  • Teams beginning Data Science and ML project
  • A background in Java and statistics is an added advantage. It will make the learning curve easy.
  • Knowledge of Python is beneficial

Want to Master your skills in Data Science with ML ?

Data Science with ML Syllabus

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The syllabus is crafted as per industry standards. It is seen that the content of the syllabus is fresh and excellent. Since both data science and machine learning are equally important terms, the syllabus covers all the relevant topics with regard to both of them.

  • Introduction to Machine Learning & Predictive Modeling
  • Types of Business problems
  • Mapping of Techniques
  • Regression vs. classification vs. segmentation vs.Forecasting
  • Major Classes of Learning Algorithms
  • Supervised vs Unsupervised Learning
  • Different Phases of Predictive Modeling (Data Pre-processing, Sampling, ModelBuilding, Validation)
  • Implementation in Python
  • Linear Regression
  • Segmentation - Cluster Analysis (K-Means)
  • Decision Trees (CART/CD 5.0)
  • Support Vector Machines(SVM)
  • Other Techniques (KNN, Naïve Bayes, )
  • Important python modules for Machine Learning (SciKit Learn, scipy, etc)
  • Artificial Neural Networks(ANN)

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Trainer's Profile


Our Mentors are from Top Companies like

  • Our trainers are highly skilled and have 10+ years of extensive experience working with Data Science and Machine Learning Training.
  • They are specialized in demonstrating the usage of the various tools used in data science and machine learning, helping the customers to understand the importance of each tool before moving forward.
  • They have in-depth knowledge in building models using Machine Learning, teaching students the basics of the software and developing hands-on experience related to many engineering fields.
  • They provide accurate guidance for data auditing, data cleaning, data visualization and analysis for machine learning application development.
  • They have conducted numerous workshops introducing the complexities of Data Science and providing a comprehensive overview of the product.
  • They have developed a wide range of technical problem-solving skills suitable for various business environments and are well-known for their ability to provide in-depth analysis and visualization of complex data generated from data science.
  • They rapidly assimilate new technologies, develop innovative solutions to difficult problems and excel in both theory and practical machine learning programming.
  • They have the ability to work with teams of students or individually and have the skills to effectively train groups of any size.
  • They have effective communication and interpersonal skills for efficient coordination with the students.
  • With their experience and capabilities, they are responsible for achieving the training objectives of students and making sure that students are well equipped with the right skill sets to become certified data scientists and machine learning experts.

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Project Practices

Text Generation With Deep Learning
Text Generation with Deep Learning
Generating human-like text using deep learning for automated content creation and chatbot responses.
Demand Forecasting With Ml For Supply Chain Optimization
Demand Forecasting with ML for Supply Chain Optimization
Optimizing supply chain operations through machine learning-based demand forecasting.
Image Style Transfer With Deep Learning
Image Style Transfer with Deep Learning
Transferring image styles using deep learning for artistic and visually appealing transformations.
Product Demand Segmentation With Ml
Product Demand Segmentation with ML
Optimizing inventory and product strategies through machine learning-based segmentation.
Traffic Prediction With Ml
Traffic Prediction with ML
Optimizing traffic management using machine learning to predict congestion and patterns.
Medical Image Analysis
Medical Image Analysis
Diagnosing diseases and planning treatments using machine learning on medical images.
Energy Consumption Forecasting with ML
Predicting energy usage patterns for planning and optimization.
Energy Consumption Forecasting With Ml
Customer Segmentation
Customer Segmentation
Using machine learning algorithms to segment customers based on their behaviors, preferences, or demographics.
Time Series Forecasting with ML
Predicting future values based on historical time series data using machine learning.
Time Series Forecasting With Ml

Learn with Real Time Projects

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Panel Mock Interview

Panel Mock Interview

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Data science is the process of extracting meaningful insights from raw data through the use of tools, models, and algorithms. It encompasses a broad set of techniques including Natural Language Processing (NLP), Predictive Modeling and Machine Learning (ML). Machine learning is a subset of data science and is used to create models and algorithms that can automate tasks and make predictions based on the data.

The most popular programming languages used for machine learning are Python, R and Java. Python is heavily used for Structured Data Analysis (SDA) and Predictive Modeling (PM). R is used for Data Mining (DM) and Visualization (Viz). Java is primarily used for Natural Language Processing (NLP).

Software engineering is the process of designing, developing, deploying, and sustaining software. Machine learning is a subset of software engineering and is used to create models and algorithms that can automate tasks and make predictions based on the data.

Mathematics plays a critical role in machine learning. It enables the development of algorithms and models that can be used to identify patterns and create predictions from the data. Mathematics is also used to create models with greater accuracy and improve the predictive power of machine learning systems.

Supervised machine learning is the process of training a model on a labeled dataset. The labels are used to guide the learning process, allowing the model to accurately predict the target variable. Unsupervised machine learning is the process of training a model on an unlabeled dataset. The model is left to its own devices to identify patterns and make predictions from the data.

Data science can be used to identify customer pain points and opportunities for improvement. By analyzing customer data such as customer feedback, transactional data and online reviews, it is possible to identify trends and patterns that can be used to optimize the customer experience.

The benefits of using machine learning include improved accuracy and efficiency in automating tasks and making predictions from the data. Machine learning models can also handle large amounts of data and enable companies to gain insights from the data that were previously not possible.

Yes, there is a high demand for data scientists with knowledge of machine learning, as machine learning is an increasingly important technology in many industries such as finance, healthcare, and e-commerce.

The Data Science with Machine Learning course will teach you how to use data science tools and techniques to extract meaningful insights from data. You will gain skills in data wrangling, data exploratory analysis, predictive modeling, supervised and unsupervised learning algorithms, and natural language processing.

The Data Science with Machine Learning course prepares you for a career in data science or machine learning. Many graduates of this course have gone on to work as data analysts, data scientists, machine learning engineers, and AI developers in a variety of industries.

Additional Information


The skill requirements for these two professionals are very similar. Let’s see the common skillsets:

  • The foremost requirement is to have sound understanding on a programming language. Python is preferred as it has a simple learning curve and its application are exhaustive than any other language.
  • Though Python is an excellent language it alone cannot help you. You may perhaps have to learn C++, R, Python and Java and also operate with Map Reduce at some point.
  • Data Scientists and Machine Learning Engineers should know statistics. Acquaintance with Matrices, Matrix Multiplication and Vectors is required.
  • Data cleansing is a significant process that can assist organizations save time and raise the efficiency. The ability to tell compelling story with data is important to make sense of your point. If your finding can’t be rapidly identified, then you cannot get it through to others easily. Data visualization can have a great effect as far as the impact of your data is concerned.
  • Machine Learning and predictive modeling are turning out to be the two happening topics. Knowledge of Machine Learning techniques including supervised machine learning, logic regression, decision trees etc., is essential. These skills will assist you to solve various data analytical issues that are dependent on predictions of prominent organizational results.
  • Deep Learning has taken conventional Machine Learning approaches to an entirely different level. It has got inspiration from biological Neurons. The key lies in mimicking the human brain. A huge network of such artificial neurons is used. This is called as Deep Neural Networks.
  • A great amount of data is needed to train Machine Learning/Deep Learning Models. Deep Learning models were not possible earlier due to the lack of data and computational power. At present, a huge amount of data is produced at good speed. Hence we need frameworks including Spark and Hadoop to handle Big Data. At present, several companies are using Big Data analytics to obtain hidden business insights. It is hence an essential skill for a data scientist and Machine Learning Engineers.
  • The most successful projects should address the exact pain points. You should know the functioning of the industry and what will be advantageous for the business. Suppose a Machine Learning Engineer or a Data Scientist does not have business insight and the knowledge of aspects that lead to a successful business model, all those technical skills cannot be used in a productive manner.
  • Computer vision and machine learning are two important branches of computer science that can operate and drive very sophisticated systems. When you join the two, you can accomplish lot more.

Machine Learning Engineer Roles:

  • Understand and transform Data science prototypes
  • Frame Machine Learning Systems
  • Research and execute relevant ML algorithms and tools
  • Develop machine learning applications as per requirements
  • Choose relevant Datasets and Data Representation Methods
  • Initiate Machine Learning Tests and Experiments
  • Carry out Statistical analysis and Fine-Tuning applying Test Results
  • Train Systems
  • Retrain systems when needed
  • Extend existing ML Frameworks and Libraries
  • Be updated with Developments in the Field

Data Scientist Roles:

  • Choosing features, Developing and Optimizing Classifiers applying Machine Learning Techniques
  • Comprehend the customer’s business requirement and lead them to a solution
  • Data mining applying cutting-edge methods
  • Processing, cleansing, and checking the integrity of data used for evaluation
  • Carry out Market Research
  • Gather data and Recognize Strength
  • Apply Deep Learning frameworks like MXNet, Tensorflow, Theano and Keras to develop Deep Learning models
  • Identify Trends, Patterns and Correlations in complex data sets
  • Find out new opportunities for process enhancement
  • Collaborate with Professional Services DevOps consultants to assist customers work with models after they are created

Want to Master your skills in Data Science with ML ?