Now to bridge the gap between industry and IT students, Skill Shikshya is launching a 4 months Data Science Machine Learning Course.

Why Data Science Machine Learning Course?

This course provides a solid foundation in the field of data science and machine learning, equipping you with the skills to make data-driven decisions and create intelligent systems. You will learn how data science drives business innovation and strategy and understand the role of machine learning in predictive analytics and automation. With this course, you will know the core principles and algorithms of machine learning, and develop skills in model evaluation and deployment.

Benefits Of Taking Data Science Machine Learning Course

✔ High-Demand Skills: Learn skills that are in high demand across industries.

✔ Career Advancement: Boost your career with expertise in data-driven decision-making.

✔ Versatility: Apply your knowledge in various domains, from healthcare to finance.

✔ Practical Application of Concepts: Have a deep understanding of how data science and machine learning concepts are implemented in various industries, enhancing your problem-solving and critical-thinking skills.

Why Choose Skill Shikshya?

✔ Expert Instructors: Our instructors are industry experts with extensive experience in Data Science and Machine Learning. Benefit from their practical insights and real-world expertise.

✔ Learn By Doing: Gain practical experience through practical projects, case studies, and interactive assignments. Apply your knowledge to real-world scenarios.

✔ Comprehensive Curriculum: Our curriculum is designed to cover the latest trends and technologies in data science and machine learning. Stay ahead of the curve with relevant and up-to-date content.

✔ Career Support: Access our career support services, including job placement assistance. We are committed to helping you succeed in your career.

✔ Flexibility: Our flexible learning options allow you to balance your professional and personal commitments. Learn at your own pace and from anywhere in the world.

Course Contents

Programming Fundamentals {Python Basics}

• Core Data Structures of Python
• Number
• String
• List
• Tuples
• Dictionary
• Set
• Advance Operation on Core
Data-Structures
• Decision and Branching
• If, Else if, Else, Break, Continue
• Looping
• Functions
• Lambda Functions
• Map, Reduce, Filter [**]
• Function Recursion
• Decorators [**]

Python Core

• List and Dictionary Comprehension
• Exceptions and Exception Handling
• File Handling
• Object Oriented Programming (OOP)
• Introduction to Classes
• Inheritance, Encapsulation, Polymorphism, Abstraction
• Method Overloading
• Building Custom Packages and Modules

Basics to Data Science

• Introduction to Data Science
• Introduction to NumPy and Matplotlib
• Matrix Operations with NumPy
• Random Variable and Probability Distributions
• Probability
• Properties of Probability Distributions
• Mean, Median, Mode
• Variance, Skewness, Kurtosis
• Multivariate Normal Distribution
• Co-Variance, Correlation
• Introduction to Scikit-Learn
• Data Pre-Processing Techniques using Scikit-Learn
• Dimensionality Reduction as Data Pre-Processing
• Principal Component Analysis (PCA)
• Linear Discriminant Analysis (LDA)

Machine learning – I

• Introduction to Reinforcement Learning
• Q-Learning with Python
• Introduction to Clustering
• K-Means Clustering
• Agglomerative Clustering
• Introduction to Supervised Learning
• Naive Bayes Classification

Machine learning – II

• Linear and Polynomial Regression
• K-Nearest Neighbors
• Decision Tree
• Balancing Bias vs Variance of ML Model
• Ensemble Learning
• Random Forest and Adaptive Boost
• Identifying Important Features of Data
• Time Series Analysis

Deep learning – I

• Introduction to Logistic Regression
• Computation Graph and Gradient Descent
• Introduction to Artificial Neuron (Perceptron)
• Multi-Layer Perceptron
• Introduction to Artificial Neural Networks
• Designing Artificial Neural Networks with Keras
• Gradient Decent Variants
• Classification and Regression using Neural Networks

Deep learning – II

• Introduction to Convolutional Neural Network (CNN)
• Object Classification with CNN
• Standard CNN Architectures
• Introduction to Object Detection
• The YOLO Algorithm
• Transfer Learning
• Deep Reinforcement Learning

Natural language processing + web interface

• Introduction to NLTK
• Text Pre-Processing
• POS Tagging and Named-Entity Recognition
• Latent Semantic Analysis
• Introduction to Recurrent Neural Network
• Word2Vec Algorithm for Text Vectorization
• Natural Language Processing with LST
• Giving Web Interface to ML Application using Flask/Django / Streamlit.

Assignment / Labs

• Each student will have a project to complete in order to demonstrate their understanding both during and after the course.
• Lab assignments will focus on the practice and mastery of contents covered in the lectures; and introduce critical and fundamental problem-solving techniques to the students.

Advanced Python and OOP

• Introduction to Map, Reduce, Filter
• List and Dictionary Comprehensions
• Exceptions and Exception Handling
• File Handling in Python
• OOP in Python

Machine Learning, Deep Learning, and NLP

• Dimensionality Reduction as Data Pre-Processing
• Principal Component Analysis (PCA)
• Linear and Polynomial Regression
• K-Nearest Neighbors
• Decision Tree
• Balancing Bias vs Variance of ML Model
• Ensemble Learning: Random Forest and Adaptive Boost
• Identifying Important Features of Data
• Introduction to Deep Learning: Logistic Regression, Perceptron, MLP
• Convolutional Neural Network (CNN)