Machine Learning – Training cum Internship

Begin an enriching journey with our immersive 6-month Machine Learning – Training cum Internship program, thoughtfully designed for students in their final semester. It has the primary objective of augmenting their employability. Notably, students enrolled in this course will undergo rigorous training, culminating in internship opportunities and comprehensive placement assistance. Diving deeper into the curriculum, the program encompasses Competitive programming, integrating advanced data structures and Algorithms to fortify essential concepts. Furthermore, it delves into Python programming and Machine Learning, offering hands-on projects to nurture a robust comprehension of the subjects.

Who Should Enroll:

Prerequisites:

Furthermore, a basic understanding of programming concepts and proficiency in at least one programming language are prerequisites for this course. Prior experience with Python is not required, making this course ideal for beginners and intermediate programmers looking to solidify their Python skills.

Machine Learning – Training cum Internship Program Objectives:

• Master the core concepts of Competitive programming, as well as Python programming skills.
• Hone your practical coding abilities through engaging hands-on exercises and participation in live projects.
• Acquire valuable experience by working on real-world applications, preparing yourself for employment opportunities.
• Explore and adopt best practices in coding, debugging, and software design principles.

Machine Learning – Training cum Internship Syllabus:

Module 1 : Competitive Programming: Advanced Data Structures and Algorithms

Introduction to Competitive Programming

• Overview of competitive programming: Definition, benefits, and career opportunities.
• Moreover, an introduction to online judges and coding platforms (e.g., Codeforces, AtCoder) will be provided
• Setting up the Java development environment for competitive programming.

 

Advanced Data Structures and Techniques  

• Binary Indexed Tree (Fenwick Tree) and its applications.
• Segment Tree with lazy propagation and range queries.
• Disjoint Set Union (DSU) / Union-Find for connected component problems.
• Trie (Prefix Tree) for string manipulation and dictionary searches.
• Wavelet Trees for efficient range queries on sequences.
• Suffix Trees and Arrays for substring search and pattern matching.
• Advanced techniques for Fenwick trees and other data structures.

 

Dynamic Programming  

• Introduction to dynamic programming: Principles and types (top-down, bottom-up).
• Classic dynamic programming problems: Longest Common Subsequence (LCS), Knapsack, etc.
• Dynamic programming optimization techniques: Memorization, tabulation, and space optimization.
• Advanced dynamic programming topics: Bitmask DP, meet-in-the-middle, and subset sum.

 

Java Collection Framework  

• Overview of Java Collection framework: Interfaces, implementations, and algorithms.
• Practical usage of ArrayList, LinkedList, HashSet, TreeSet, HashMap, TreeMap, etc.
• Working with iterators, comparators, and other utility classes in the Collection framework.

 

Algorithmic Techniques  

• Divide and conquer algorithms: Merge sort, quicksort, binary search, etc.
• Greedy algorithms and their applications in problem-solving.
• Backtracking techniques for exhaustive search and optimization problems.
• Graph algorithms: Depth-First Search (DFS), Breadth-First Search (BFS), and their applications.

 

Problem-Solving Strategies

• Problem analysis and understanding requirements.
• Effective coding techniques: Optimization, debugging, and testing.
• Contest strategies: Time management, problem selection, and adaptive approaches.

 

Module 2 :  Practical Machine Learning with Python

 

Introduction to Python

• Overview of Python: History, features, and popularity.
• Additionally, the course will cover installing Python and setting up the development environment (e.g., Anaconda, Jupyter Notebook)
• Running Python code using interactive shells and script files.

 

Python Syntax and Data Types

• Python syntax: Indentation, comments, and statements.
• Data types: Integers, floats, strings, boolean, and None.
• Variables and assignment: Naming conventions, dynamic typing, and variable scope.

 

Control Flow and Loops

• Conditional statements: If, elif, and else.
• Furthermore, the course will around into loops, covering both for loops and while loops
• Loop control statements: break, continue, and pass.

 

Data Structures in Python

• Lists: This includes creating lists, indexing, slicing, and list methods (e.g., append, extend, remove).
• Tuples: It encompasses creating tuples, accessing elements, and tuple methods (e.g., count, index).
• Dictionaries:This section covers creating dictionaries, accessing elements by keys, and dictionary methods (e.g., keys, values, items).
• Sets: Lastly, it explores creating sets, set operations (e.g., union, intersection, difference), and set methods (e.g., add, remove).

Functions and Modules

• Functions: Defining functions, parameters, return values, and function scope.
• Lambda functions: Anonymous functions for simple expressions.
• Modules and packages: Importing modules, using standard library modules, and creating user-defined modules.

 

File Handling

• Reading from and writing to files: Open, read, write, and close files.
• File modes: Read mode (‘r’), write mode (‘w’), append mode (‘a’), and binary mode (‘b’).

 

Exception Handling

• Furthermore, the course will cover handling exceptions, including the use of try, except, else, and finally blocks
• Raising exceptions: raise statement for custom exceptions.

 

Object-Oriented Programming (OOP) Basics

• Classes and objects: Defining classes, creating objects, and accessing attributes.
• Inheritance and polymorphism: Extending classes, method overriding, and method overloading.

Basic NumPy and Pandas

• Introduction to NumPy: Arrays, indexing, slicing, and basic operations.
• Introduction to Pandas: Series, DataFrame, data manipulation, and basic data analysis.

Basic Visualization with Matplotlib

• Introduction to Matplotlib: Plotting graphs, customizing plots, and basic data visualization.

Introduction to Machine Learning

• Overview of machine learning: Definition, types (supervised, unsupervised, reinforcement learning), and applications.
• Introduction to Python libraries for machine learning: NumPy, Pandas, Matplotlib, and Scikit-learn.
• Setting up the development environment: Installing Python and required libraries.

Data Preprocessing

• Data cleaning: Handling missing values, outliers, and inconsistencies.
• Feature engineering: Feature scaling, encoding categorical variables, and creating new features.
• Exploratory Data Analysis (EDA): Data visualization techniques for understanding data distributions, correlations, and patterns.
• Data splitting: Train-test split and cross-validation for model evaluation.

Supervised Learning Algorithms

• Linear Regression: Theory, implementation, evaluation metrics (e.g., MSE, R-squared).
• Logistic Regression: Binary and multiclass classification, evaluation metrics (e.g., accuracy, precision, recall, F1-score).
• Decision Trees and Random Forests: Theory, ensemble learning, feature importance, and hyperparameter tuning.
• Support Vector Machines (SVM): Linear and kernelized SVM, soft margin vs. hard margin, kernel tricks.
• k-Nearest Neighbors (kNN): Distance metrics, curse of dimensionality, and model selection.

Unsupervised Learning Algorithms

• K-Means Clustering: Theory, implementation, choosing the number of clusters (k), and evaluating clustering performance.
• Hierarchical Clustering: Agglomerative and divisive clustering methods, dendrogram visualization.
• Principal Component Analysis (PCA): Dimensionality reduction, eigenvectors, eigenvalues, and variance explained.
• t-Distributed Stochastic Neighbor Embedding (t-SNE): Non-linear dimensionality reduction, visualization of high-dimensional data.

Advanced Topics in Machine Learning

• Ensemble Learning: Gradient Boosting Machines (GBM), AdaBoost, XGBoost, and LightGBM.
• Neural Networks and Deep Learning: Introduction to artificial neural networks (ANN), deep learning frameworks (e.g., TensorFlow, Keras), and deep learning architectures (e.g., CNN, RNN, LSTM).
• Hyperparameter Tuning: Grid search, random search, and Bayesian optimization techniques.
• Model Evaluation and Validation: Cross-validation strategies, bias-variance tradeoff, and model selection techniques.

Machine Learning – Training cum Internship  Certification:

Participants will receive an Internship Certificate on Completion of the course provided they have attended all classes including assignments and project work.

Instructor:

Furthermore, Our instructors are seasoned developers and educators, with years of experience both in the industry and in teaching Python. They are committed to providing personalized feedback and support to ensure participants achieve their learning and career goals.

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