Course on
End to End Data Science
This course includes the concepts and insight practical approach of Data Science and the learners will get to work in the areas of Machine learning, Deep Learning. A full-fledged course for working professionals and mentoring till they become data scientist.
Key Features
Gain experience with handson exercises
Instructor led training
Flexibility of learning in class or online
Industry exposure – Use Cases
Learn by doing – Assignments, Tasks
Industry-relevant curriculum
Innovative Approach – Discussions, Quiz, webinar
Project Based Learning Approach
Capstone Projects
Solutions for your skill gaps
Upskilling and Level Setting
Dedicating mentoring sessions from industry experts
Career Support
Industry valued Certificate
Talk to an Expert
Schedule a meet with AI consultant to know more about the course, and the discussion will help to take action over learning.
Schedule – Online (Live) Instructor Led Training
| Batch | Schedule | Course Fee | |
|---|---|---|---|
| Open [One – One] | Flexible | INR 125000 USD 1675 EURO 1500 |
** For payment and installment options, please contact +91 9841557655 **
Prerequisite : None
Curriculum
- Introduction, Installing Python, Anaconda environment
- Variables , Input Functions
- Operators, Control Flow
- String Handling
- Data Structures-Lists, Tuples, Sets, Dictionary
- Functions, Modules, Packages,
- File Handling
- Exception Handling
- Object Oriented Paradigms
- Numpy: Introduction, Numerical operations on Numpy
- Pandas: Getting started with pandas
- Data Frame Basics, Key Operations on Data Frames
- Sci-py: working with Scipy
- Scatter Plot, Line Chart, Histogram
- Bar Chart, Box plot, Heat Map
- Pair plot, scatter Matrix using Matplotlib
- Pandas Vizualization
- Seaborn
- Working with Different File formats – CSV,JSON, PDF,binary format, HDF5
- Interacting with data in Sql-Pysql with MysqlDB
- Interacting with data in Nosql -pymongo with MongoDB
- HDFS installation
- HDFS Overview & Data storage
- Get the data into Hadoop from local machine(Data Loading ) – vice versa
- Map Reduce model
- Hive and Pig model
- Spark installation
- Spark streaming
- Pydoop and Pyspark implementation
- Descriptive Statistics: Measuring central tendancy, Variance
- Inferential Statistics : Hypothesis Test- P test, t test, z-score, Chi square test, ANOVA
- Probability Distributions: Gaussian, Poisson, Bernouli, Binomial, Uniform, Exponential
- Linear Algebra
- Analytic Geometry
- Matrix Decompositions
- Vector Calculus
- Continuous Optimization
- Time Series, Down sampling,
- Resampling, Upsampling
- Time deltas, Time series forecasting with ARIMA
- Multivariate Time Series
- Getting Started with Data Mining
- Data Cleaning / Wrangling
- Exploratory Data Analysis
- Affinity analysis,
- Prediction Analysis
- Scikit-Learn, Prepossessing
- Fundamentals of Machine learning
- Supervised Learning ,Unsupervised Learning
- Regression,Classification and Clustering Problems
- Various algorithms involved in machine learning problems
- Regularization Techniques, Cross Validation, Evaluation Metrics
- Dimensionality Reduction techniques
- Market Basket Analysis, Recommender Systems
- Getting started with TensorFlow
- Loading and exploring the data
- Data transformation & Data segmentation
- Tensor and matrix operations
- Computational graphs
- Data pipelines
- Dataset framework & manipulations
- Loading data for classifiers
- Extracting data, loss function
- Optimization, accuracy
- Model training using TensorFlow
- Neural Networks Foundations
- Fundamentals of deep learning
- Activation Function
- Hidden layers, hidden units
- Illustrate & Training a Perceptron
- Important Parameters of Perceptron
- Various neural networks involved in deep learning
- Creating a sequence of layers and adding layers in Tensorflow and Keras
- Deep Learning with MLP
- CNN, RNN, LSTM
- Regression networks
- Auto encoders
- Generative Adversarial Networks
- Deep reinforcement learning, Q learning
- Compile model by specifying functions & optimizers
- Execution of the defined model using Tensor flow & Keras
- Parameters vs Hyper parameter
- Hyper parameter tuning
- Regularization
- Optimization
- Policy Gradient Methods
- Creating performant apps for ML models StreamLit &
- Deploy Machine Learning Models’ dashboard using Joblib, Pickle & StreamLit
- Deploying Streamlit Application in Heroku cloud- PaaS
- Capstone Project – 1
- Capstone Project – 2
- Capstone Project – 3
Tools Covered
