Mastering Machine Learning: From Fundamentals to Advanced Applications

Explore the essentials and advanced techniques of machine learning in this comprehensive program. Learn through real-world case studies, hands-on projects, and expert guidance to master predictive models, neural networks, and AI-driven solutions. Perfect for aspiring professionals eager to lead in the AI space.


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Course Highlights

Live Classes

Engage in interactive sessions led by experienced mentors.

Placement Assistance

Gain exclusive access to Skolar's job portal for career opportunities.

Beginner to Advance

No prior experience required; suitable for learners at all levels.

Project Based Learning

Learn through hands-on experience through projects

Industry Expertise

Engage in interactive sessions led by experienced mentors.

Quick Course

Join a comprehensive program spanning 12 weeks

Course Highlights

Meet your mentors for this course

Course content

  • Installing necessary tools
  • Other Prerequisites
  • Python Application
  • What is Python?
  • Why Python?
  • Installing Python
  • Python Basic
  • Python Intermediate (List, Tuples, Strings, Slicing, Sets, Dictionary, Scope, Flow Control, SCIENCE, Functions, Python JSON, Python RegEx, File Handling, Python Modules, Python PIP)
  • What is git?
  • How to use GitHub?
  • Version control with git?
  • Why should you use VS Code?
  • Jupyter Notebook Overview
  • What is parallel computing?
  • What is GPU? Why do we need to learn about Nvidia CUDA?
  • Google Colab
  • What is a Virtual Environment?
  • Numpy
  • Pandas
  • Scikit-Learn
  • Matplotlib vs Seaborn
  • Vector addition and subtraction
  • Norms and vector length
  • Dot product and its properties
  • Matrices and matrix operations (addition, multiplication)
  • Matrix-vector multiplication
  • Transpose of a matrix
  • Linear equations and systems
  • Principal Component Analysis (PCA)
  • Basics of limits and continuity
  • Partial derivatives and gradients
  • Definite and indefinite integrals
  • Multiple integrals and applications in probability
  • Integration techniques (substitution, integration by parts)
  • Newton's method for optimization
  • Introduction to probability
  • Random variables and probability distributions
  • Discrete and continuous probability distributions
  • Expected values and variance
  • Central Limit Theorem
  • Sampling and sampling distributions
  • Point estimation and confidence intervals
  • Hypothesis testing
  • Linear regression as a statistical model
  • Maximum Likelihood Estimation (MLE)
  • Bayesian probability and Bayes' theorem
  • Basic Excel Skills
  • Advanced Formulas and Functions
  • Data Analysis and Visualization
  • Data Management and Cleanup
  • Automation with Macros and VBA
  • Introduction to PowerBI
  • Data Import and Transformation
  • Data Visualization
  • Power BI DAX (Data Analysis Expressions)
  • Power BI Administration and Security
  • Power BI Advanced Features
  • Types of Machine Learning: Supervised, unsupervised, and reinforcement learning.
  • Data Preprocessing: Data cleaning, feature scaling, and handling missing values
  • Feature Engineering Techniques
  • Handling Imbalanced Data
  • Handling Categorical Data
  • Applications of Unsupervised Learning
  • Advanced Clustering Techniques
  • Advanced Dimensionality Reduction Techniques
  • Understanding Model Complexity: Overfitting and Underfitting of Training Data
  • Performance metrics
  • Hyperparameter Tuning
  • Grid Search and Random Search
  • Cross-Validation
  • Gradient Descent
  • Model Evaluation: Learning Curves
  • Bias-Variance Trade-off
  • Regularization
  • Ensemble Methods: Bagging and Boosting
  • Linear Regression
  • Classification Algorithms: Logistic Regression, K-Nearest Neighbors, Decision Trees, Random Forests.
  • Support Vector Machines (SVMs)
  • Ensemble Methods
  • Model Evaluation: Metrics (accuracy, precision, recall, F1-score)
  • Clustering: K-Means, Hierarchical clustering, DBSCAN.
  • Advanced Clustering Techniques
  • Introduction to Reinforcement Learning
  • Markov Decision Processes, Q-learning.

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Frequently Asked Questions

Skolar is a skill development and internship training platform that not only allows you to find relevant career opportunities but even comprehensively prepares you for placements
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