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Machine Learning with Python

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
  • 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
  • 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.
  • Bayes Rule
  • Multinomial and Gaussian Naive Bayes
  • Implementation in spam email detection
  • Practice exercises
  • 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
  • Kernel Distribution Estimation plots
  • Setting up hypothesis and checking
  • Importance of p-value and critical threshold
  • Practice exercises
  • K-nn algorithm and its implementation
  • K-means algorithm and its implementation
  • DBSCAN algorithm and its implementation
  • Bagging Boosting (XGBoost, AdaGrad)
  • Practice exercises
  • Overfitting and underfitting a model
  • Regularization Techniques to prevent overfitting
  • Balance and requirements for Recall and Precision
  • L1 and L2 regularization
  • Practice exercises
  • What are hyperparameters
  • Grid Search vs Random Search
  • K-fold validation
  • Practice exercises
  • Extracting feature importance
  • Feature Selection using inbuilt python methods
  • Creation of new features and dropping irrelevant features
  • Practice exercises
  • Description of Data Frames
  • Missing Value imputation
  • Converting to categorical data
  • Normalization and scaling of data
  • Processing time-series data
  • Practice exercises
  • 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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