Machine Learning & Data Analytics with Python
(In Association with iHUB Divyasampark IIT Roorkee)
About the Course:
Machine Learning is the most used technology these days due to its ability to automate tasks, detect patterns and learn from Data. Python is the language that is extensively used in Machine Learning & Data Analytics applications. Therefore, this course is designed in such a way that the participants can learn Python Basics, Data Analytics & Machine Learning together in a specified timeframe. The course begins with Python, covering its fundamentals to certain advanced concepts, followed by an in-depth discussion on using various Python libraries for Data Analytics applications. Next, the course explores Machine Learning in detail, covering both Supervised and Unsupervised Machine Learning algorithms. The hands-on approach adopted here will enable participants to gain proficiency in using Python libraries and Machine Learning models for real-world applications. The skills and certification acquired through this course will enhance employability and open doors to exciting career prospects.
Course Objectives:
- To enable participants to understand the fundamentals of Python programming and its wide range of applications.
- To make the participants efficient in using Python Libraries in different Data Analytics applications.
- To familiarize participants with a range of Machine Learning Algorithms/Models along with their strengths and weaknesses.
- To help participants become comfortable with using predictive models through Projects and Assignments.
- To equip participants with the ability to select and apply suitable algorithms and techniques to solve real-world problems using Machine Learning.
- To lay the foundation for learning advanced concepts like Reinforcement Learning, Neural Networks, and Deep Learning.
Batch Details:
Class Timings: 7:00 pm – 9:00 pm (Tuesday-Thursday-Saturday)
Start Date: 10th Dec 2024 End Date: 08th March 2025
Duration: 78 Hours | Mode: Online
Certification: From iHUB Divyasampark IIT Roorkee
Last Date to Register: 09th Dec 2024
Course Fee:
- Students/PhD Scholars/RA/JRF/SRF/Postdoc fellows: Rs. 12,000/-
- Faculty/Working Professionals: Rs. 14,000/-
Course Overview:
Module 1
- Basics of Python Language
- Python objects with details of strings/numbers/lists/dictionaries/tuples etc.
- Statements & Loops; Comparison operators
- Range, List Comprehension, Functions, Lambda expressions etc.
- Hands-on Project
Module 2
- Introduction to NumPy
- Random functions, Reshape, Arithmetic Operations
- 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
Module 3
- Working with Matplotlib Library; Working with different plots
- Working with text
- Concatenating Series & DataFrames
- Working with Seaborn Library
- Seaborn categorical Plots
- Capstone 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 Machine Learning
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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