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Learn Machine Learning: The complete ML course

Learn A-Z everything about ML
From the basic to advanced

What you'll learn

  • Basic fundamentals of Machine learning.
  • Data exploration, data preprocessing, handling missing values.
  • Feature engineering and exploratory data analysis.
  • Data visualization techniques.
  • Descriptive and inferential statistics .
  • Cross – validation techniques.
  • Model selection, model training, model evaluation and model prediction.
  • Supervised learning, Unsupervised learning and Reinforcement learning.
  • Regression, Classification, Clustering, Association rules.
  • Linear regression, Logistic regression, Support vector machine, Naïve bias algorithm, Decision tree, Random forest, K-nearest neighbors and others.
  • Ensemble learning – bagging and boosting.
  • K-means, DBSCAN, Hierarchical clustering.
  • Content based filtering and Collaborative filtering.
  • Recommendation system and its working process.
  • Adaboost, XGboost, Catboost, Gradient boosting, etc.
  • Deep learning and Neural networks.
  • Perceptron, Artificial neural networks, Feed forward neural network.
  • Back-propagation algorithm.
  • Weights, bias and tradeoff.
  • Overfitting and underfitting.
  • Activation functions, optimizers and loss / cost functions.
  • Epochs, step per epochs, batch size, val epochs, learning rate, etc..
  • Project management, development and deployment.
  • Web scraping techniques.
  • API development using FASTAPI framework.
  • Working with sklearn, TensorFlow, Pandas, Numpy, Matplotlib, Seaborn, Plotly.
  • Hands on experience in real world projects.
  • Machine learning interview questions.
  • Machine learning mock interview preparation.
  • Helping resume creation.

Requirements

  • Carry your own laptop with decent configurations
  • Knowledge about Python programming language.

  • Course overview
  • Course outcome
  • Installing anaconda, jupyter notebook
  • Working with environments

  • Introduction of Machine learning
  • Importance of Machine learning
  • Industrial applications of Machine learning
  • Problem statement analysis
  • Numerical and categorical variables
  • Types of Machine learning
  • Machine learning pipeline

  • Explore various data exploration methods
  • Handling missing values methods
  • Working with Pandas, Numpy and Sklearn libraries
  • One-hot encoder and label encoder
  • Data normalization, data standardization and quantiles
  • Handling outliers
  • Descriptive statistics - mean, mode, median, standard deviation, variance, etc.
  • Data distributions, skewness and kurtosis
  • Inferential statistics - various feature selection techniques, statistical tastings, hypothesis testing
  • Dimensionality reduction techniques – PCA, LDA, etc.
  • Grid search, random search
  • Cross – validation techniques
  • Group by and pivot table
  • Perform exploratory data analysis (EDA)
  • Project 1
  • Project 2

  • Introduction of Data visualization
  • Importance of Data visualization
  • Explanation of various graphs / charts
  • Practical with Matplotlib, Seaborn and Plotly libraries
  • Project 1
  • Project 2

  • Getting started with Supervised learning
  • Types of Supervised learning
  • Explanation of Regression technique
  • Algorithms of Regression technique
  • Model evaluation methods for regression
  • Use cases of Regression technique
  • Explanation of Classification technique
  • Algorithms of Classification technique
  • Model evaluation methods for Classification
  • Use cases of Classification technique
  • Linear Regression
  • Logistic Regressionn
  • Support vector machine
  • Naïve bias algorithm
  • Decision tree
  • Random forest
  • K-nearest neighbors and others
  • Project 1
  • Project 2
  • Project 3

  • Introduction of Ensemble learning
  • Types of Ensemble learning
  • Workflow of Ensemble learning
  • Explanation of Bagging technique
  • Algorithms of Bagging technique
  • Explanation of Boosting technique
  • Types of Boosting technique
  • Algorithms of Boosting technique
  • Adaboost
  • XGboost
  • Catboost
  • Gradient boosting and others
  • Project 1
  • Project 2

  • Getting started with Unsupervised learning
  • Types of Unsupervised learning
  • Explanation of Clustering technique
  • Algorithms of Clustering technique
  • Model evaluation methods for Clustering
  • Use cases of Clustering technique
  • K-means, DBSCAN, Hierarchical clustering
  • Explanation of Association rules technique
  • Algorithms of Association rules technique
  • Model evaluation methods for Association rules
  • Use cases of Association rules technique
  • Content based filtering and Collaborative filtering
  • Recommendation system and its working process
  • Project 1
  • Project 2

  • Introduction of Reinforcement learning
  • Working process of Reinforcement learning
  • Algorithms of Reinforcement learning
  • Applications of Reinforcement learning
  • Practical

  • Introduction of Deep learning
  • Importance of Deep learning
  • Explanation about Neural networks
  • Types of Neural networks
  • Architecture of Neural networks
  • Workflow of Neural networks
  • Feed forword propagation
  • Back propagation
  • Weights and bias
  • Weights and bias initialization techniques
  • Handling overfitting and underfitting
  • Regularizations and dropouts
  • Batch normalization
  • Explanation on activation functions
  • Various types of activation functions
  • Explanation on loss / cost functions
  • Various types of loss / cost functions
  • Explanation on optimizers functions
  • Various types of optimizers functions
  • Learn about hyper parameters – epochs, step per epochs, batch size, val epochs, learning rate, etc.
  • Working with TensorFlow library
  • Building a custom Artificial neural networks
  • Project 1
  • Project 2

  • Introduction of Computer vision
  • Introduction of Natura Language Processing
  • Web scraping techniques
  • FASTAPI development
  • GitHub management
  • Project deployment
  • Level up your Kaggle profile

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