Fast-Track courses on Machine Learning and Artificial Intelligence

The dynamic business world is continually evolving. To excel in your career, upgrade your knowledge and skills with the course on Machine Learning, and Artificial Intelligence.

Covid-19 pandemic has rewritten the way we work and live. Traveling and staying outside your home is getting restricted, and online work has become the new normal. So, if you want to be on top of your game and outsmart your competition then grab the opportunity of mastering these essential skills at a never before price. 
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Learn the latest module in Machine Learning and Artificial Intelligence.

What's your takeaway?

• You will be trained by people who have got more than 15+ years of industry experience
• This will be a live training program so you attend it without having to go through all the traveling fuss, especially at such unprecedented times.
• The course is being offered at a never before price.
• The money you donate for this course goes to a Non-Profit Educational Organization  (https://www.resileo-labs.com/kalvi)
• 4.5 months from now, you will be able to use ML models
• You'll be certified in Machine Learning, and Artificial Intelligence
• The top 10 candidates in each batch will be offered E-Internship 
• Course days are Monday to Friday, So you'd be able to enjoy your weekend.

Timings: 7 pm- 8 pm

Medium- English

Course: Machine Learning & Artificial Intelligence

4.5 months – 120 hours

 

Pre-requisites: One RDBMS, Python, Tableau/Metabase/PowerBI, Data loading

 

Introduction to Machine Learning (5 hours)

• What cannot be done by manual analysis and need for ML
• Concepts of Supervised learning, unsupervised learning
• Training data, test data – industry datasets
• Introduction to ANN (artificial neural networks)

Mathematics for Machine Learning (10 hours)

• Linear Algebra, equations
• Probability Theory, Eigen values and Eigen vectors
• PCA - Principal Component Analysis
• Line of best fit, curve of best fit

Advanced Statistics using Python (25 hours)

• Intro to statistics
• What are dependent data and independent data?
• Running a basic linear regression code
• Population parameter estimation method
• Confidence interval estimation method
• Hypothesis testing
• Ttest and ztest

Regression & Classification for Business Applications (50 hours)

Time series forecasting

• Exploratory Data Analysis - Pandas
• Seaborn
• Exponential Smoothening, Holt Winters
• ARIMA, Auto ARIMA

Regression

• Linear Regression – K-best, K-fold, train-test, cross validation, normalization
• Subset Regression
• Regularization - bias, variances, lasso, ridge and elastic net

 

Classification

• Logistic Regression
• SVM
• Naïve Bayes classification
• Random Forest
• KNN

Clustering

• K Means Clustering
• Fuzzy K Means Clustering

AutoML

• H20
• DataRobot
• Nyckel

Advanced Topics (TensorFlow) (30 Hours)

• Set up TensorFlow virtual environment for python
• Keras - Create Neural Network
• Keras Models Construction
• Layers in Keras Models
• Build Convolutional NN with Keras
• Introduction to deep learning
• Regression and prediction using ANN
• Image Classification using CNN

Project work (30 hours – outside class hours)

• Load large sets of data in postgres or ClickHouse
• Carry out EDA
• Build dashboards using Tableau or Metabase or PowerBI
• Create prediction models using Python
• Compare and present results using multiple models

*** Apart from the above hours, students are supposed to put an additional effort of 30-40 hours on self-learning.