Project Description

Machine Learning with Python

Total Course Duration: 100 Hours
Per Class Duration: 3 Hours

Course Overview

Machine Learning হচ্ছে এমন একটা পদ্ধতি যেখানে সে পূর্বের সকল ডাটাকে তার অভিজ্ঞতা হিসেবে বিবেচনা করে জ্ঞানার্জনের মাধ্যমে বর্তমান বিভিন্ন সমস্যার সমাধান করতে সক্ষম হয়। মানুষের যেমন অভিজ্ঞতা বাড়ার সাথে সাথে তার কাজের দক্ষতা বৃদ্ধি পায়; তেমনি মেশিন কে যত বেশি ডাটা দিয়ে শেখানো হবে, তার অভিজ্ঞতা/দক্ষতাও তত বেশি বৃদ্ধি পাবে।

আবার, একটু অন্যভাবে বললে বিষয়টা এমন- মেশিন লার্নিং হলো Artificial Intelligence (AI) এর একটি অ্যাপ্লিকেশন, যা Explicitly প্রোগ্রাম না করেই স্বয়ংক্রিয়ভাবে Experience থেকে শেখার এবং Improve করার ক্ষমতা সরবরাহ করে। মেশিন লার্নিং মূলত কম্পিউটার প্রোগ্রাম Development কে ফোকাস করে, যা Data এক্সেস করতে পারে, এটি নিজের জন্য শিখতে (মেশিন নিজের জন্য শিখবে) এবং নিজের দক্ষতা বৃদ্ধিতে ব্যবহার করতে পারে।

Skills You Will Gain from This Course

  1. Python Libraries
  2. Machine Learning Tool Sets.
  3. Develop Projects in Jupyter Notebook, Spyder and Various IDE
  4. Difference Between Machine Learning Methods: Supervised and Unsupervised
  5. Supervised learning algorithms, including classification and regression
  6. Unsupervised learning algorithms, including Clustering and Dimensionality Reduction
  7. How statistical modeling relates to machine learning and how to compare them
  8. Communicate visually and effectively with Matplotlib and Seaborn
  9. Train/Test cross validation to select correct model and predict model perform with unseen data
  10. Support Vector Machine (SVM) for handwriting recognition and classification
  11. Use decision trees to predict The Scenario
  12. Real-life examples of the different ways machine learning affect society

Basic Requirements

  • Basic Python programming knowledge is necessary
  • Good understanding of linear algebra & statistics

Who this course is for:

  1. Anyone willing and interested to learn machine learning with Python
  2. Anyone who wants to apply machine learning to their domain
  3. Any intermediate to advanced users who is unable to work with large datasets
  4. Anyone who has a deep interest in the practical application of machine learning to real world problems
  5. Anyone wishes to move beyond the basics and develop an understanding of the whole range of machine learning algorithms
  1. What is Machine Learning?
  2. Applications of Machine Learning
  3. Machine learning methods (Supervised, Unsupervised, Reinforcement)
  4. Machine learning algorithms (Regression, Classification, Clustering, Association)
  5. Python libraries suitable for Machine Learning
  6. Basic ideas about AI, Machine learning for AI, other approaches towards AI
  1. Installing Necessary Applications and Creating Environment
  2. Hello World
  3. Others Settings as Required
  1. Types of Machine Learning Algorithms
  2. Labelled Dataset, Unlabeled Dataset
  3. Model Fitting (Overfitting, Good Fitting, Underfitting)
  4. Training and Testing Data
  5. Important module and library (Numpy, Pandas, Matplotlib, Sklearn, ……….)
  6. Load the Dataset
  7. Separate Training and Testing Data
  8. Train Using Training Data (Through the Algorithm)
  9. Split Dataset for Train and Test
  10. Test or Predict Using Test Data
  11. Accuracy of The Model
  12. Predict the output manually

Data Visualization, Visualize the Data in graph

  1. Difference between data and information
  1. Data types, Data pre-processing, Dealing with missing data
  2. Standardizing data, why and when to standardize, Scaling
  1. Data Exploration and Data Preparation, Data Wrangling
  2. Analyzing data to fetch the information
  1. Getting data for training, scraping
  1. Correlation

Categorical Data

  1. Introduction to regression and also explain the applications of regression
  2. Linear Regression
  3. Non-Linear Regression
  1. Bayesian Linear Regression
  1. Polynomial regression
  2. Gradient Descent
  3. Cost function
  4. Regularization
  5. Ridge and lasso Regression
  6. Linear Regression, Linear Regression with Scikit-Learn
  1. Evaluate Regression Model Performance
  1. Learning Curve
  2. Correlation Analysis and Feature Selection
  3. Understanding MNIST Dataset
  4. Flatten a Matrix
  1. Introduction to Classification & also explain the applications of classification
  2. Logistic Regression, Sigmoid function
  3. Decision tree Classifier Algorithm
    1. Introduction to Decision Tree
    2. Training and Visualizing a Decision Tree
    3. Visualizing Boundary
    4. Tree Regression, Regularization and Over Fitting
  4. K-Nearest Neighbors (K-NN) Classifier Algorithm
    1. KNN Introduction
    2. Addition Materials
  1. Random Forests Classifier Algorithm
  1. SVM (Support Vector Machine)
    1. Support Vector Machine (SVM) Concepts
    2. Linear SVM Classification
    3. Polynomial Kernel
    4. Radial Basis Function
    5. Support Vector Regression
  1. Stochastic Gradient Descent (SGD) Classifier Algorithm

Limitations of linear classifier and evaluate abilities of non-linear classifiers using a data set

  1. Application of Unsupervised learning, examples, and applications
  2. Clustering
    1. Introduction to Clustering
    2. Introduction to k-Means Clustering
    3. Introduction to Hierarchical Clustering
    4. Density Based Clustering
  1. Measuring the distance between two clusters
  2. k-means algorithm

Limitations of K-means clustering

  1. Introduction to dimensionality reduction and also explain the applications of it’s
  2. Feature selection & extraction
  3. Dimensionality reduction via Principal component analysis
  4. Eigenvalue and Eigenvectors
  5. Hands on PCA on MNSIT data
  1. LDA vs PCA
  1. What is reinforcement learning
  2. Applications of reinforcement learning
  3. Components of RL
  4. Approaches to RL
  5. RL algorithms
  6. Deep reinforcement learning
  1. What is NLP? Why NLP
  2. Applications of NLP
  3. Components of NLP
  4. NLP techniques
  1. Why deep learning?
  2. Introduction to Neural Networks
  3. Neural networks Architecture
  4. Applications of neural networks
  5. Biological Neuron vs Artificial Neuron
  6. Artificial Neural networks, layers
  7. Classification basics using Artificial neural network
  1. Dealing with Image in ANN
  1. Over and Under Fitting

Machine Learning Workflow

  1. Introduction to CNN, idea and math, why and when CNN
  2. Using CNN for Image classification
  3. Multiclass classification with CNN
  4. Neural Network Revision
  5. Visualizing CNN

Training Your CNN

  1. Intro to Recommender Systems
  2. Content-based Recommender Systems
  3. Collaborative Filtering

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