Applied Machine Learning with Python & Analytics

Applied Machine Learning with Python & Analytics
(In Association with iHUB Divyasampark IIT Roorkee)

Why this Course: 

Course Overview:

Module 1

• Basics of Python Language
• Python objects with details of shell/numbers/variables etc. 
• Comparison operators
• Range, List Comprehension, 
• Functions, Lambda expressions etc.
• Introduction to NumPy
• Random functions, Reshape, Arithmetic Operations
• Hands-on project

Module 2 

• Introduction to Pandas
• Selecting a single column, important series methods 
• Indexing & Sorting; loc & iloc with series
• Inspecting dataFrames, filtering with conditional operators
• Adding & removing columns; updating values, working with date & time 
• Hands-on Project

Module 3 

• Working with Matplotlib Library; Working with different plots
• Working with text
• Concatenating Series & DataFrames
• Working with Seaborn Library
• Seaborn categorical Plots
• Hands-on project on Data Analytics

Module 4 

• Machine Learning Basics, introduction to supervised & unsupervised learning
• Linear Regression for One and Multiple Variables, Cost Function & Gradient Function
• Ordinary Least Square, Dummy Variables, One Hot Encoding, Polynomial Regression
• Anscombe’s quartet, Performance Metrics like Mean Absolute Error, Root Mean Squared Error, - Regularization (Ridge & Lasso)

Module 5 

• Logistic Regression, Sigmoid Function, Anscombe’s quartet
• Confusion Matrix, interpreting parameters like F-1 score, Accuracy, Precision, Recall etc. 
• Bias-variance trade off, Overfitting, Underfitting of Models
• K- nearest neighbors (KNN), Elbow Method; Distance Metric in KNN
• Understanding Support Vector machines using Hyperplanes; Maximum Margin Classifier

Module 6 

• Higher Dimension Transformation and Projection, Kernels :: Polynomial, RBF etc.
• Decision Trees, Nodes: Root, Leaf, Parent, Children. Tree Pruning, Gini Impurity
• Random Forests, Ensemble Learners, Information Gain
• Boosted Trees, Weak and Strong Learners, AdaBoost, Gradient Boosting, Stump Classification
• Naive Bayes classifier, Conditional Probability, Bayes Theorem 

Module 7

• Natural Language Processing (NLP), Count Vectorization, Extracting Features From Text Data, Term Frequency - Inverse Document Frequency (TF-IDF), Document Term Matrix (DTM)
• Unsupervised Learning Basics
• K-Means Clustering, Clustering of unlabelled data, Assigning new point to the cluster
• Hierarchical Clustering: Agglomerative and Divisive Approach, Dendrogram, Linkage Matrix, Similarity Metrics, Ward

Module 8

• DBSCAN, epsilon distance, Core, Border and Outlier 
• Principal Component Analysis (PCA), Dimension Reduction
• Introduction to Deep Learning
• Artificial Neural Networks
• Perceptron Model, Activation Functions; Cost Functions and Gradient Descent
• Forward and Backward Propagation; Keras vs TensorFlow
• Hands-on Project on ML

Prerequisites and eligibility:

• No coding experience in any programming language required. We’ll start from scratch.
• This course can be taken up by any undergraduate/postgraduate student of Basic & Applied Sciences, Engineering, Management and Computer Applications and also by Research Scholars/Faculties/Working Professionals who want to upskill themselves.
• Participants need to have a laptop/PC (with a minimum of 4 GB RAM, 100 GB HDD, Intel i3 processor) and proper internet/Wi-Fi connection.

Contact Person: Dr. Subrat Kotoky

Email: [email protected] / [email protected]

Phone: 9085317465 / 8473874389

Expert Profile:  Mr. Shreyas Shukla

Professional Corporate Trainer & Microsoft Azure Certified Data Engineer

 M.Tech-IIT Kharagpur & BE- The Aeronautical Society of India, New Delhi

 (Total Experience in conducting Professional Courses: 4+ Years)

  Certifications:

• DP-203: Microsoft Certified: Azure Data Engineer Associate

• DP-900: Microsoft Certified: Azure Data Fundamentals

• AZ-900: Microsoft Certified: Azure Fundamentals

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