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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Announcing Machine Learning Certification Course for Beginners!

A
Analytics Vidhya Last Updated : 25 Aug, 2021
3 min read

Time to Start your Machine Learning Journey for FREE!

From face unlock to giving biometric attendance, machine learning seeped into human lives at almost every stage. Machine learning is so extensive that you probably use it numerous times a day involuntarily and unknowingly

Currently, Machine Learning is like that golden dream, which everyone wants but does not know how to get. And that is precisely where Analytics Vidhya comes in with its magic wand. Our courses have set the benchmark for quality content and we come with another course just for beginners and that too is free of cost.

So, if you are someone who asks themselves, “From where should I start my Machine Learning journey?”, the answer is Machine Learning Certification Course for Beginners!

free machine learning certified course for beginners  

 

What is the Machine Learning Certification Course for Beginners

The Machine Learning Certification Course for Beginners is a FREE step-by-step online starter program to learn the basics of Machine Learning, hear from industry experts and data science professionals, and apply your learning in machine learning hackathons!

We will be covering Python for Data Science, the importance of statistics and EDA, the underlying intuition behind several machine learning algorithms, and then solve case studies/projects using Machine Learning concepts in Python.

So, if you invest 8 – 10 hours a week for this course, you can complete the entire course within 6 – 8 weeks.

The aim of the Machine Learning Certification Course for Beginners is:

  • Help you understand how this field is transforming and disrupting industries
  • Acquaint you with the core machine learning algorithms
  • Enhance and complement your learning through competition and hackathon exposure

This is the perfect starting point to ignite your fledging machine learning career and take a HUGE step towards your dream data scientist role. We believe in a holistic learning approach and that’s how we’ve curated the Machine Learning Certification Course for Beginners!  

 

What will I learn from this course?

  • Python libraries like Numpy, Pandas, etc. to analyze your data efficiently.
  • Importance of Statistics and Exploratory Data Analysis (EDA) in the data science field.
  • Linear Regression, Logistic Regression, and Decision Trees for building machine learning models.
  • Understand how to solve Classification and Regression problems using machine learning
  • How to evaluate your machine learning models using the right evaluation metrics?
  • Improve and enhance your machine learning model’s accuracy through feature engineering

Jackpot: Upon successful completion of the course, you will be provided a blockchain-enabled certificate by Analytics Vidhya with lifetime validity.

 

Tools Covered in this Course:

  • Python
  • Pandas
  • Numpy
  • Matplotlib
  • Scikit learn
  • Seaborn

Projects covered in this course

  1. Customer Churn Prediction

    This project requires you to predict the propensity to churn for each customer.
    Projects covered in this course

     

  2. NYC Taxi Trip Duration Prediction

    This project will cover techniques to extract important features and accurately predict trip duration for taxi trips in New York using data from the TLC commission New York.

 

Who is the Machine Learning Certification Course for?

The Machine Learning Certification Course is for anyone who:

  • Is a beginner in machine learning
  • Wants to kick start their machine learning journey
  • Wants to learn about core machine learning algorithms
  • Is interested in a practical learning environment
  • Wants to practice and enhance their existing machine learning knowledge

But wait, what are the perquisites?

This course requires no prior knowledge about Data Science or any tool.

 

So, what are you waiting for? Enroll in the Machine Learning Certification Course for Beginners! for FREE and begin your learning journey today!

 

A
Analytics Vidhya

Analytics Vidhya Content team

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