 15 students
 82 lessons
 0 quizzes
 10 week duration
Machine Learning is rapidly developing field. Over 50,000 jobs are vacant in India and the count is only going to increase with time. These jobs are not getting filled because of lack of talented engineers who can apply machine learning to various data sets. It must be appreciated that Data Science is a demanding subject; it is in fact culmination of Statistics, Linear Algebra, Calculus and Computer Programming. This doesn’t mean that cracking data science is impossible! It only means that we need a quick introduction to data science where Statistics, Linear Algebra, Calculus and Computer programming is taught simultaneously as a handson session and a follow up with regular classes with focus on real case studies. All these concepts are subjects which require the student to develop an intuition towards observing real world data and correlate it with models and algorithms of machine learning. Since it is an intense and demanding task to master this subject, the initial foundation must be strong. Our course is created to create continuous discussion and meditation on the concepts to help students to find a foothold on this rapidly advancing field.
1.1 Python is the language we use to tell our computers to compute a set of algorithms: why Python?
Imagine a real python (snake); it’s big, wavy and long. We want something similar to handle large number of related/unrelated data sets. A python (snake) is continuous, flexible and similarly Python (language) provides us with that flexibility to handle tabulated/listed arrays of data. Since machine learning at advanced level is all about dealing with software implementation of linear algebra and multinomial expression(not complicated!) , it is important to choose a language that suits easy handling of grouped and structured data.
1.2 Machine Learning:
Mathematics related to computational science provides us with many techniques to discover hidden patterns in data. And because of advanced programming languages (advanced in sense of handling abstract concepts and data structures) like python , Scala etc we have the resource to rapidly code algorithms or to implement it using readily available libraries . Every problem is different because every set of data have different quality. This field and has evolved over 50 years and derives its concreteness from Statistics.
1.3 Statistics:
This is all about numbers. It is a field of math which centers on techniques to discover how much truth is hidden in given data set. Data may show something but the reality is usually hidden deep in the distributions. Once this data is plotted on to a graph we begin to see a pattern. This pattern gives us the insight to go ahead and take the next necessary steps. A statistician is the one who is trained to see DataSet as if it was a physical entity. That’s why he/she uses terms like Gradient, Descent, skew, etc. Statistics is all about making error free interpretations about the phenomenon represented by the data. Many tools have been developed over years. We will learn many (important ones) in the workshop and code it in python simultaneously.
But one cannot assume that mere understanding of algorithms can help us solve any prediction problem!
If that was true, then we could have used “Machine learning algorithms” to teach itself “Machine Learning” and then we would have Evolving Algorithms which are found in science fiction movies. However, the reality is different. Imagine a carpenter and his toolbox. He has a hammer, a set of pliers, nails, files, cutters, hacksaw and many others handy tools. We all know how each one of them functions. Even then, it still does not make us Carpenters! It is only a carpenter who knows which tool to use in a certain situation.
“Data Engineering” is the ability to implement machine learning algorithms, and
“Data Science” is knowing which algorithm to use, when and where.
Data science is an Art. This art can be developed when one develops an intuition for data. Diploma in Machine Learning and AI is tailored such that the student develops this intuition.

Introduction To Machine Learning
 What is Machine learning and real world examples
 Machine, its language and evolution into AI
 A short trip on evolution of mathematics and science.
 Supervised learning
 Unsupervised learning
 Model representation
 Cost function & intuition
 Gradient decent & Intuition
 Gradient decent for linear regression intuition
 Classification and Representation
 Revisiting cost function
 Regularizing linear regression
 Evaluation a learning algorithm

LIBRARIES AND API'S IN PYTHON

STATISTICS & PROBABILITY USING PYTHON
 Types of data
 Mean, Median , Mode using python
 Standard deviation using python
 Probability Density function and Probability mass function
 Common data distribution
 Hands On Activity Covariance and Correlation
 Probability refresher
 Problem on Conditional Probability
 Solution Conditional Probability
 Baye’s Theorem

MACHINE LEARNING AND PYTHON
 Supervised vs. Unsupervised Learning, and Train/Test
 Linear Regression
 Polynomial Regression
 Logistic Regression
 Multivariate Analysis: Predict housing Prices
 Preventing Overfitting
 Bayesian Methods
 Implementing a Spam Classifier with Naive Bayes
 KMeans Clustering
 Hands On Activity Clustering people based on income and Education
 Measuring the chaos in data: Entropy
 Decision Trees
 Project: Apply Decision Trees concepts
 Ensemble Learning
 Meditations on reference systems (Cartesian, spherical etc.) and linear algebra
 Support Vector Machines (SVM)
 Project: Apply SVM concepts using Scikit
 Principal Component Analysis (PCA) and Dimensionality Reduction
 PCA example with the iris data set
 Percentiles and Moments.
 Data Warehousing: ETL and ELT concepts
 Project using matplotlib

RECOMMENDER SYSTEM: HOW NETFLIX WORKS

DEALING WITH REAL WORLD DATA

APACHE SPARK, ML AND BIG DATA

DESIGNING AN DATA SCIENCE EXPERIMENT

ADVANCED TECHNIQUES: NEURAL NETWORKS AND DEEP LEARNING
 Neural Network Game
 Deep Learning concepts
 Biological Motivation of Neural Networks and Historical
 Deep Learning using Tensoflow on google tensor flow playground
 Tensor Flow
 Keras
 Convolution Neural Networks(CNN’s)
 CNN for pattern recognition in hand writing recognition
 Recurrent Neural Networks(RNN)
 Sentiment Analysis.
4.56 average based on 16 ratings
Reviews

This course was really awesome with latest course materials and syllabus . I would recommend it for anyone interested in Machine Learning and AI

Really enjoyed the course .The teaching was made very interesting by the tutor and very clearly articulated

Lots of useful exercises in the course to give practical knowledge . Must for anyone interested in Machine Learning and AI

Highly recommend to people who like programming. All lectures are explained beutifully

Thank you for the course. Learned many new things . The exercises and challenges after each lecture made my basics strong.
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