Ever since the dawn of data and computational power machine learning has been proven to be a very good tool to predict, and analyze, a segment in every domain. From high-end physics research to the supermarket, data is available everywhere and we should be able to utilize the data. That is why the skill set is very vital and comes in handy in every aspect of our lives.

The main objective is to introduce people to the world of machine learning and familiarize them with the tools and techniques of the machine learning world. The course also plans to make students familiar with the Machine Learning pipeline followed by the industry thus making students ready to be involved in an end-to-end machine learning project in the real world.

It will make students ready to tackle the real-world problems with the skill set utilizing data and then thus able to profit from it.

Objective

At the end of the course, students will be able to construct a whole machine-learning pipeline following the life cycle of any machine-learning project. He/she will be able to construct models, evaluate them using different metrics relative to the problem, perform data analysis to get powerful insights into the data, and be able to tell a story to the client.

In addition to that students will be able to perform supervised and unsupervised learning, dimension reduction, hypothesis testing, and compare the model's performance in a real-world simulation.

Also, many many more technical and geeky aspects of this new field are covered which is included in the detailed syllabus of the course.

Who can join machine learning with Python?

If you have some knowledge of programming (i.e. very basic stuff) and surficial knowledge of matrices and calculus, you are good to go. Stating that if you have the will to learn it will be very easy to be able to catch up with the concepts. Also, our course has been designed to have a very soft learning curve so everyone interested is welcome.

Duration : 2.5 Months

Syllabus Expand All

Fundamentals of Python

Installation of Python and environment for the dependencies
Data types (integers, floats, strings, lists, tuples, dictionary, multi-dimensional lists)
Data types operations (pop, push, append, insert, del)
Loops (for, while)
Conditional statements (and, or, if, equals to, not equals to)
Functions and return types
Inbuilt functions useful in ML (len, columns, is null, value counts, )
Introduction to Pandas and numpy
Reading files from csv, database.
Data operations with pandas like merge, sort, concat, drop, copy.
Subsetting and data extraction from pandas

Data Exploration

Correlations and causality
Introduction to matplotlib and sns
Extracting meaningful insights from data by visualization
Practice exercises in data exploration using popular datasets like titanic, etc.

Bayesian Methods

Bayes Rule
Multinomial and Gaussian Naive Bayes
Implementation in spam email detection
Practice exercises

Evaluation Metrics

Accuracy for Classification
Confusion Metrics, Sensitivity/Recall, Specificity, F1 Score, ROC, AUC
Evaluation for Imbalanced Datasets
Evaluation for Regression Problem, L1 loss, L2 loss
Practice exercises

Probability Distributions and Hypothesis Testing

Kernel Distribution Estimation plots
Setting up hypothesis and checking
Importance of p-value and critical threshold
Practice exercises

Unsupervised Learning, Clustering, and Model Ensembling

K-nn algorithm and its implementation
K-means algorithm and its implementation
DBSCAN algorithm and its implementation
Bagging Boosting (XGBoost, AdaGrad)
Practice exercises

Bias/variance, recall/precision trade-offs and regularization

Overfitting and underfitting a model
Regularization Techniques to prevent overfitting
Balance and requirements for Recall and Precision
L1 and L2 regularization
Practice exercises

Hyper-parameters Tuning

What are hyperparameters
Grid Search vs Random Search
K-fold validation
Practice exercises

Feature Engineering

Extracting feature importance
Feature Selection using inbuilt python methods
Creation of new features and dropping irrelevant features
Practice exercises

Data Preprocessing

Description of Data Frames
Missing Value imputation
Converting to categorical data
Normalization and scaling of data
Processing time-series data
Practice exercises

Introduction to Machine Learning

Linear Regression from scratch using Ordinary Least Square method
Introduction to Gradient Descent
Linear Regression from scratch using Gradient Descent Optimization
Introduction to Sklearn and use of it to implement Linear Regression
The logic behind Logistic Regression, Support Vector Machines, Decision Trees
Practice exercises using well-known datasets to use for prediction

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Machine Learning with Python