AI / ML Engineer full Overview
Hi, This is Vijay. And welcome to The World of a Data Scientist.
Now in this competitive world, Machine Learning, Artificial Intelligence has become Buzz words.
So if you are a fresher to AI/ML, you need to explore this first. Then you can read this article of the Overview and Curriculum of an AI/ML Engineer comfortably. In this article ML Overview is explained, Continuation will be updated soon in this blog itself.
Explore ML Overview, Happy reading!👇
Fundamentals of ML:
In this course, you will learn the widely used terminology in AI and ML. You'll also understand the essential algorithms behind some Supervised learning applications. Most importantly, you'll build solutions for some real-world problems.
Introduction to AI and ML:
- What is AI, ML?
- Applications of ML
- ML Components
- Supervised Learning
- Classification,Regression
- Python for ML
Linear Regression:
- Hypothesis function
- Cost function
- Gradient Descent
- Normal Equation
- Vectorization
- Polynomial Regression
K-Nearest Neighbours:
- k-NN for classification
- Choice of k
- KD Trees
- LSH and Inverted Indices
- Instance based learning
Logistic Regression:
- Sigmoid Function
- Cost Function
- Maximum Likelihood Estimate
- GD for Logistic Regression
- Multiclass Classification
Data Preprocessing:
- Handling missing values
- One-hot Encoding
- Feature Scaling
- Feature Selection
Regularization:
- Overfitting
- Underfitting
- Regularized Linear Regression
- Regularized Logistic Regression
Supervised Machine Learning Algorithms:
In this course, you'll learn advanced supervised learning algorithms like SVM, decision trees, naive Bayes etc. You will also learn ensembling techniques that will boost the performance of ML models. During the course, you will get to apply the algorithms learnt to solve real-world problems.
SVM:
- Maximum Margin Classifier
- Support Vectors
- Handling Outliers
- Kernels
- SVM as large margin Classifier
- Constrained Optimization
Decision Trees:
- Choosing the best attribute
- Entropy
- Information Gain
- Gini Index
- ID3,C4.5
- Handling missing values
- Pruning
- Reduced-error Pruning
- Rule post-Pruning
- Limitations of Decision Trees
Naive Bayes:
- Conditional Probability and Bayes Rule
- Naive Bayes Classifier
- Naive Bayes Algorithm
- Laplace Smoothing
- NB for text classification
- Handling Real-world Problems
Algorithms for Regression:
- Regression Trees
- Choice of Threshold & Attribute
- Overfitting
- Cost Complexity Pruning
- k-NN for Regression
- Locally Weighted Regression
Learning Theory:
- Evaluation of hypothesis function
- Tuning HyperParameters
- Bias, Variance and Noise Decomposition
- Error Analysis
- Performance Optimization
Bagging:
- Bootstrapping
- Out-of-bag Error
- Bagged Decision Trees
- Random Forests
Boosting:
- Gradient Boosting
- AdaBoost
- Stochastic Gradient Boosting
- ECOC
- Stacking
Competitive Machine Learning:
Does Competitive Programming excite you? This course will take that excitement to the next level. You'll get the right toolbox to take on your competition by showing your expertise in Data-preprocessing, feature engineering, modelling and maximizing the performance of the model.
Introduction:
- Introduction to Competitive ML
- Exploratory Data Analysis
- Introduction to Pandas
- Visualizations
- Dataset Cleaning
- Introduction to scikit -learn
Advanced Pre-Processing & Feature Engineering
- Missing values Imputation
- Label Encoding
- Target Encoding
- Mean Target Encoding
- Feature Interactions
Validation
- K-fold Cross-Validation
- Validation Strategies
- Data-Splitting Strategies
- Pros and Cons of Validation
Modelling
- Hyperparameter Tuning
- Grid Search
- Advanced Model Ensembling
- Metrics Optimization
This is Machine Learning to its deep. Hope you have got better clarity of ML Overview.
Comment how is your experience by reading this article.
Thank you. Hope you gained knowledge from this article.
Thank you. Hope you gained knowledge from this article.
