Data Science
AI - Machine Learning
Step into the Future- Be an Industry ready Data Science Professional
100% Placement Assistance | 1000+ Hiring Partners
4.5/5
80256 Learners Enrolled
Next Cohort Starts : 5 Sep, 2023
Limited number of seats available
Program Duration : 6 Months
At 12 - 15 hrs/week
Classroom Based
Learning format
About Program
This Data Science with Machine Learning certification course led by Rooman Technologies aims at helping you master all the l skills that are crucial in the field of Data Science with Machine Learning In this course, you will learn how to utilize python and python based functionalities for data analytical purposes. As well as work with real-world Kickstarter data to assess what makes a successful project via hands-on practice.
Training Option
- Live-Online Instructor Led Training
- 100+ lab assignments & Quizzes
- 24/7 Lab access on Rooman Cloud Lab
- Labs Designed & Mentorship support by Industry Experts
- 5 capstone projects
- Live-Online sessions with Industry Experts & Subject Matter Expert from Rooman
- Access to Recorded Session of Live-Online Classes available 24/7
- Industry Recognized Course Completion Certificate
- Interview Preparation & Placement Support
- In-Person Classroom based Training conducted by Subject Matter Expert
- Flexibility to attend classes at any of our 50+ Centers PAN India
- Hands-on experience at our state-of-the-art Lab
- 100+ lab assignments & Quizzes
- 24/7 Lab access on Rooman Cloud Lab
- Labs Designed & Mentorship support by Industry Experts
- 5 Capstone & 1 real-world project
- Exclusive sessions with Industry & Subject Matter Expert
- Access to Recorded Session of Live-Online Classes available 24/7
- Industry Recognized Course Completion Certificate
- Interview Preparation & Placement Support
- Access to Campus Placement drives
- 1 year access to our LMS
Need Guidance?
Talk to our experts on
080 4044 5566
Tools & Softwares
Course Curriculum
Artificial Intelligence & Reinforcement Learning
- Environment
- Agent, State, Action
- Reward and Punishment
- Model based vs Model free
- On Policy vs Off Policy
- Policy based vs Value based
- Greedy policy
- Bellman Equation
- Markov Property/ Markov Chains
- Transition Probability
- Markov Reward Process
- Markov Decision Process
- Optimal Policy/ Value
- Policy Evaluation /Iteration
- Prediction of Actions
- Importance Sampling
- Discounting
- Pre-rewards
- Advantages of TD
- Q Learning
- Sarsa
- DQN (Deep Q Learning)
- Core APIs
- Spaces
- Wrappers
- Atari
- Custom Environment
- Vectorized Environments
Advance AI
- Encoder vs Decoder
- Difference with PCA
- KL Divergence
- Variable Auto Encoders
- Generators
- Discriminator
- DCGAN
- Generative Adversarial Text to Image Synthesis
- Style GAN
- Soft and Hard Attention
- Local and Global attention
- Monotonic Alignment and Predictive Alignment
- Multi headed Attention
- Transformers
Deep Learning
- Perceptron and relate it with Logistic Regression
- Multiple layer Neural network
- Similarities and Differences with Basic ML
- Forward Propagation
- Back Propagation Algorithm
- Vanishing Gradient and Exploding Gradient
- Non Linearity
- Sigmoid / Tanh Function
- Relu /Leaky Relu /Gelu
- Softmax Function
- Gradient Descent
- Stochastic Gradient Descent
- Momentum
- AdaGrad
- RMSProp
- Adam/ Nadam
- Tensors
- Session, Placeholders and Variables
- Hands on with Tensorflow
- Graphs using Tensorboard
- Why Keras
- Sequential vs Functional
- Model Creation
- Difference between Tensorflow and Pytorch
- Autograd
- Graphs
- Control Flow and Weight Sharing
- Feed Forward Networks
- Fully Connected Networks
- Recurrent Neural Networks
- Convolutional Networks
- OpenCV
- Convolution/ Filters/ Pooling
- Back Propogation in CNN
- Types Of CNN
- FastRCNN, YOLO
- ImageNet
- Need of Transfer Learning
- Freezing of Layers
- Reusing of Structure
- LeNet/ Alexnet
- VGG 16/ 19
- ResNet 34/101
- Inception and Googlenet
- Classical RNN
- Vanishing Gradient
- Exploding Gradient
- LSTM/GRU
- Bidirectional RNN
- Lemmatization/ Stemming
- Embedding
- Vectorization
- Glove
- Nltk/Spacy
ML/AIOps and Big Data
- Installation
- All Necessary Commands
- Permissions and Ownership
- Tools and Packages
- Docker Configuration
- Docker Files
- Continer Linking
- Kubernetes Architecture
- Kubectl Commands
- Url Mapping
- Function / Class based Views
- ORM
- Admin
- AWS Server
- S3 Buckets
- Sagemaker
- Build Train Deploy
- Life Cycle
- Local Repository
- Add / Commit / Push / Pull
- Merge
- Stash
Advance Machine Learning
- Problems with Large Features
- Why penalty is inducted
- Difference between L1 and L2
- Cost Function
- Logistic regression vs Linear Regression
- Log Odds / Logit / Sigmoid Function
- Optimization and Log Loss
- Maximum Likelihood Estimation
- Support Vectors and Hyperplanes
- Hard margin vs Soft margin
- Kernel Trick in non-Linear SVM
- SVM Parameter Tuning
- Optimization
- Hinge Loss and Combined Loss
- Lag Values
- AutoRegression/ AutoCorrelation
- Stationarity
- ACF vs PACF Plots
- Smoothing Time Series
- Dicky Fuller Test
- AR/ MA/ ARIMA/ SARIMA
- Holdout Validation
- K-fold cross Validation
- Stratified Kfold
- Cross_val_score
- GridSearchCV
- RandomizedSearchCV
Regression Based evaluation
- MSE/MAE
- R2/Adjusted R2
Classification Based evaluation
- Confusion Matrix
- Precision/ Sensitivity/ Specificity/ F1 Score
- AUC/ ROC
- AIC and BIC
- Random Forest
- AdaBoost
- Gradient Boosting
- XGBoost
- Blending/ Stacking
- Clustering
- Anomaly Detection
- Association Rules
- Dimensional Reduction
- KMeans
- Heirarchical
- DBScan
- Spectral Clustering
- GMM
- Eigen Values and Eigen Vectors
- Principal Component
- PCA
- Advance Dimension Reduction Techniques
Machine Learning
- Difference Between AI, ML and DL
- Applications of Machine Learning
- Categorization of Machine Learning
- Supervised / Unsupervised / Semi Supervised
- Parametric vs Non Parametric
- Geometric/ Rule Based/ Gaussian
- Flow Operation (Pipelining)
- Sklearn Usage
- Null Values Imputation
- Outlier Detection
- Encoding
- Label Encoder
- Ordinal Encoding
- One Hot Encoding
- Scaling
- Binarizer
- MinMaxScaling
- Normalizer (L1 and L2)
- StandardScaler
- Imbalance Dataset
- Univariate/ Bivariate/ Multivariate Analysis
- Filter Methods
- Wrapper Methods
- Embedding
- Dimensional Reduction
- Data Cleaning
- Train/ Test Split
- Basic Modeling
- Bias and Variance
- Evaluation Metrics
- Cross validation
- Criteria to Select Models
- Ensembling
- Assumptions
- Introduction to Linear Regression
- Predictions using Dot product
- Multiple Linear Regression
- Cost Function (Sum of Square Error)
- Gradient Descent based approach
- Introduction to Polynomial Regression
- When to use Polynomial regression
- Evaluation based on RMSE/ R2
- Introduction to Decision tree
- Decision Tree Classification / Regression
- Types of Decision Tree techniques (ID3 / CART)
- Optimization and Pruning
- Introduction to KNN algorithm
- KNN Classifier vs Regressor
- How to select the best K
- Conditional Probability and Bayes Theorem
- Naive Bayes
- Burnoulli / Multinomial
- Gaussian Implementation
Business Analytics and Intelligence
- Measures of Central Tendency
- Distributions
- PDF/ CDF
- Central Limit Theorem
- Parametric and non parametric Tests
- Z test/ T test
- Chi2 test
- p-Value
- F test
- One way ANOVA, Two Way ANOVA
- MANOVA
- Visualization
- Dashboard
- DAX
- Measures
- M Language
- Power Query
- R Studio
- Basic Data Structures in R
- Vectors and DataFrames
- Distributions
- Regression Analysis
- Classification
Python and Libraries
- Strings and Basic Data Structures
- Strings
- List and Tuples
- Sets and Dictionaries
- Control
- Statements
- Conditions
- While and For Loop
- Adv Loops
- Functions
- Basic Functions
- Nested Functions
- Recursive Functions
- Object Oriented Programming
- Basics of OOPS
- Inheritance
- Polymorphism
- Composition
- Linked Lists
- Trees
- Stacks and Queues
- Graphs
- Searching
- Sorting
- Basic Numpy
- Indexing/ Slicing
- Broadcasting
- Appending/ Inserting on Axis
- Mathematical and Statistical operations
- Advance Numpy
- Sort/ Conditions
- Transpose operations
- Joining/ Splitting
- Linear Algebra
- Introduction
- Data Extraction
- Series/ DataFrame Creations
- Indexing and Slicing
- Grouping and Merging
- Conditions/ Grouping/ Imputation
- Append/ Concat/ Merge/ Join
- DateTime Functionalities and Resampling
- Window Functions
- Excel and Sql Functions
- Pivot table/ Crosstab/ Melt
- Sql with Pandas
- Scatterplots/ Barplots/ Histograms/ Density Plots
- 3D plots
- Boxplotting and Outlier Detection
- Visualizing Linear Relationships
- Customization of Matplotlib
- Subplotting
- Seaborn and Plotly
What Students Say
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Salary Scale
Maximum
10 LPA
Average
5 LPA
Minimum
3.5 LPA
Job Role
- Data Scientist
- ML Engineer
- Data Analyst
- Business Analyst
- Applied Scientist
- Research Analyst
- ML Ops
- Domain Specific Analyst
Salary Scale
Maximum
10 LPA
Average
5 LPA
Minimum
3.5 LPA
Job Role
- Data Scientist
- ML Engineer
- Data Analyst
- Business Analyst
- Applied Scientist
- Research Analyst
- ML Ops
- Domain Specific Analyst
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