Objective

This Short-Term e-learning program in Data Science Foundation & Machine Learning empowers learners to analyze data with industry-standard tools and techniques. The mentors take an efficient approach for teaching analytics, enabling learners to immediately apply their knowledge to solve business problems, internships, and live projects. Throughout the program, learners will strengthen their knowledge with practice exercises, assignments, and live projects.

Highlights

Flip classroom (Combination of recordings & Live sessions)
Pre-recorded videos explaining the concepts
Live Mentored sessions for hands-on practice with python notebooks
Domain centric problem-solving exercises
Focus on the 5 W’s (Why, Who, What, Where, When) while applying the concepts on the case studies.
Industry Sessions
Use of Git and GitHub for project repository
Module-wise Tests and Projects

5 Months & 88 Hours

Knowledge required of Data Science and Introductory Machine Learning concepts

68 hours of mentored 48 hours of self-paced

More flexible and manageable learning schedule

Cost - 35,000 plus GST

Personalized Mentoring with small groups of 8-10 learners.

Avail 20% Discount

Bring a friend and get 20% discount on your programme fees.

OR

Show your student ID and get 20% discount on your Programme fees.

Course Content

Participants will take approximately 5 months to complete this program. Following are the modules covered under this program

Introduction to Python

2 Weeks, Self-Paced
  • Python Basics
  • Python Functions and Packages
  • Working with Data Structures,      Arrays, Vectors & Data Frames
  • Jupyter Notebook – Installation & function
  • Pandas, NumPy, Matplotlib, Seaborn

EDA and Data Processing

2 weeks, Self-Paced
  • Data Types
  • Dispersion & Skewness
  • Uni & multi Variate Analysis
  • Data imputation
  • Identifying and normalizing
  • Outliers

Statistical Base for Data Science

2 weeks, Self-Paced
  • Descriptive Statistics
  • Probability & Conditional Probability
  • Hypothesis Testing
  • Inferential Statistics
  • Probability Distributions

Advanced Statistics

2 weeks, Live Mentored Sessions 
  • Naïve Bayes 
  • Central Limit Theorem 
  • Hypothesis Testing
  • Estimations & Confidence intervals 
  • ANNOVA

Predictive Modelling - Supervised Learning

3 weeks, Self-Paced & Live Mentored Sessions 
  • Linear Regression
  • Multiple Variable Linear Regression
  • Logistic Regression
  • Encoding (One Hot/Label Encoding)
  • Feature engineering & Feature elimination techniques (RFE, VIF etc) 
  • Naive Bayes Classifiers
  • k-NN Classification
  • Support Vector Machines

Ensemble Techniques

2 weeks, Self-Paced & Live Mentored Sessions 
  • Decision Trees (Basic Overview)
  • Bagging
  • Random Forests
  • Gradient Descent
  • Boosting/XG Boost
  • Other Ensemble techniques

Unsupervised learning

2 weeks, Self-Paced & Live Mentored Sessions 
  • K-means Clustering
  • Hierarchical Clustering
  • Dimension Reduction-PCA
  • Sampling (Thomson Sampling)
  • Market Basket Analysis

Featurisation, Model Selection & Tunning

1 week, Self-Paced & Live Mentored Sessions 
  • Feature engineering
  • Model selection and tuning
  • Model performance measures
  • Regularizing Linear models
  • Bootstrap sampling
  • Grid search CV
  • Randomized search C
  • K fold cross-validation 

Time-series Forecasting

1.5 weeks, Self-Paced & Live Mentored sessions
  • Introduction to forecasting data
  • Properties of Time Series data
  • Examples and features of Time Series data
  • Naive, Average and Moving Average Forecasting
  • Exponential Smoothing
  • ARIMA Approach

Introduction to Neural Networks and Deep Learning

1.5 weeks, Live Mentored Sessions
  • Introduction to Perceptron & Neural Networks
  • Activation and Loss functions
  • Gradient Descent
  • Batch Normalization
  • TensorFlow & Keras for Neural Networks
  • Hyper Parameter Tuning

NLP (Natural Language Processing)

3 weeks, Live Mentored sessions
  • Introduction to NLP
  • Stop Words
  • Tokenization
  • Stemming and lemmatization
  • Bag of Words Model
  • Word Vectorizer
  • TF-IDF
  • POS Tagging
  • Text Classification
  • Named Entity Recognition
  • Introduction to Sequential data
  • RNNs and its mechanisms
  • Vanishing & Exploding gradients in RNNs
  • LSTMs – Long short-term memory & Application 
  • GRUs – Gated recurrent unit
  • LSTMs Applications
  • Time series analysis
  • LSTMs with attention mechanism
  • Neural Machine Translation
  • Advanced Language Models: Transformers, BERT, XLNet

Data Visualizations

1.5 weeks, Live Mentored Sessions
  • Introduction to Perceptron & Neural Networks
  • Activation and Loss functions
  • Gradient Descent
  • Batch Normalization
  • TensorFlow & Keras for Neural Networks
  • Hyper Parameter Tuning

SQL

2 weeks, Self-Paced
  • Coming Soon

Tools & Techniques

FbProphet
Python
Anaconda
Scikit Learn
Matplot Lib
Numpy
Pandas
Tableau
StreamLit
Seaborn
Stats Model
Knime

Contact Us

9892358595
nextlearningstep@gmail.com
Give us a call or drop by anytime, we endeavour to answer all enquiries within 24 hours on business days.