Machine Learning

Machine Learning Training In Bangalore

Machine Learning

Computers are inherently stupid as they can’t think of themselves. But this is not a limitation because they were never designed to take decision. Computers were designed to follow instructions and to do compute intensive mathematics in quick time. Computers are really good at running simulations and it gives enormous power of recognizing patterns in a given set of data.
“Statistics is the study of data, patterns, and trends. Machine learning is statistics on steroids.”

21st-century computers have fast processing power and GPU has revolutionize this whole thing. Post-1991, the internet has generated a huge chunk of data. Invention of smart phones and IoT devices has increased this chunk of data thousand times. This data is useless until this data can be turned to information. Machine Learning is the tool that crunch this large data and generates information from it to make critical decisions. It will not be wrong if we say that year 2019 fully belonged to Machine Learning and below the snapshot of Stack Overflow trends also showing same. Python is most OnDemand skills nowadays and all thanks to Machine Learning.

Machine Learning course, Machine Learning Certification Training Institute In Bangalore

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Machine Learning is the process where Machine learns from experience or machines creates separate patterns from data. Majorly, Machine Learning has two branches.

One is supervised learning where a Machine learns from experience. So labeled data is fed to the machine and the machine generates a relation model between input and output data. This model further can be used for the data where output is unknown. Stock market prediction on the basis of the past performance of stocks is one of the examples of supervised machine learning. This example actually belongs to regression, a normal statistician can’t run lakhs of regression within minutes but a computer can.

Finding cancer from the photograph is also an example of supervised machine learning, where the machine learning model been fed by previous clinical data and the model learns from that year of experience.

Another branch of machine learning is unsupervised learning. The label is not available here. Here is just a chunk of data and no relation is known in that data. This model is to find patterns from the data and then produce the results. Finding a new protein from the research data can be an example of unsupervised learning. Generating Netflix’s recommendation from the viewer’s behavior is also an example of unsupervised machine learning.

Machine Learning is increasing at a remarkable pace and every industry is getting benefitted from it. C++ and Python are the main programming language to make machine learning models but now other languages are also in the race. Swift is the new player in this ML race and Javascript is also a great choice if one wants to run ML model in a web browser.

What Our Students Say

If one wants to start his journey for Data Science or Machine Learning (both files are highly related) then competition platform Kaggle can be a good start. There are many Youtube channels that are teaching Machine Learning and Andrew Ng has one of the best courses on Coursera.

Machine Learning is a hot skill since ML engineers are making some serious money as the salary package of ML engineers ranges from $80 k per annum to $170 k per annum with an average of somewhere $130 k per annum.

Deep learning is the new skill in demand and tools like Tensorflow and Pytorch have made these technologies easy to adopt. ScikitLearn is still one of the best resources to learn the bit and pieces of Machine Learning.

Best Machine Learning Training in Bangalore

DLithe provides technology platform for people who want to learn about future technology. This course is equipped with classroom training and offer hands-on experience during the training.


Students, Professionals who like to pursue career in Machine Learning, Data Science can opt for it.

Course Objectives:

The Machine Learning certification course with DLithe offers hands-on training covering basics, supervised and unsupervised learning, paradigms, data, model and many more. The aspirants can learn and implement machine learning.

This course is designed to help learn how to apply machine learning to business problems. Real-life case studies are used to teach the various algorithms and techniques. The focus will be on applications, rather than on exposition of the various algorithms.

The certificate procured by the DLithe Machine Learning course has lifetime validity. DLithe also helps you with sourcing your profile on Machine Learning, Data Science related openings in product & service based companies.


The course is comprised of Instructor-led 40 hours, with hands-on sessions.

Topic 1: Introduction & Concepts

  • What is Machine Learning: Learning from Data
  • ML Paradigms: Supervised, Unsupervised, Reinforcement
  • Approach for building ML products: the process
  • Intuition for Classification (Paper & Pen)

Topic 2: Model Building: Tree-based

  • Building a Decision Tree
  • Encoding data, Training & Test Split
  • Choosing Error Metrics: Standard vs. Custom
  • Model Evaluation Approach

Topic 3: Model Validation & Selection

  • Overfitting & Underfitting, Generalisation, Learning Curves
  • Regularisation in Trees
  • Cross-Validation: Hold-out, K-fold
  • Hyper-Parameter Tuning: Grid Search

Topic 4: Ensemble Models

  • Ensemble Models for Generalisation
  • Resampling & Bootstrap
  • Data Bagging Approach: Random Forest
  • Boosting Approach: Gradient Boosting

Topic 5: Building Model: Linear

  • Linear Regression
  • Normalisation & Standardistion
  • L1 / L2 Regularisation in Linear Models
  • Logistic Regression (for Classification)

Topic 6: Feature Engineering

  • Feature Creation Approaches
  • Scale Transformations
  • Feature Importance & Selection
  • Domain Knowledge & Art of Feature Engineering

Topic 7: Build & Deploy ML Service

  • Concept of ML Service for Prediction
  • Pipelines and Model Serialisation
  • Rest API and design
  • Deploy your ML Service - localhost API

Topic 8: Interpret ML Models

  • Concept & Why ML Interpretation
  • Types: Feature, Instance & Model level
  • Local Surrogate Models (LIME), Shapely Values
  • Model Visualisation

Topic 9: Dimensionality Reduction

  • The curse of dimensionality
  • Matrix Factorisation approaches
  • Usage for Unsupervised Learning: Similarity
  • Feature creation for Supervised Learning

Topic 10: Clustering

  • Concept & Challenges of Clustering
  • Distance-based approaches e.g. K-Means
  • Measuring clustering performance
  • Alternate approaches - Neighbour, Manifold (theory)

Topic 11: ML Challenges & Automation

  • Handling Time-dependent Data
  • Unbalanced Class, Anomaly Detection
  • Hyper Parameter Tuning Approaches
  • Automated Feature Engg & Model Selection: AutoML

Topic 12: Practice Session & Wrap-up

  • Best practices in building ML service
  • Monitoring Model drift & Tracking Performance
  • Challenges in managing ML in production
  • Where to go from here: Learning Path

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