Important Discussion on:
● What is Data Science?
● What is Machine Learning?
● Data Science Venn Diagram.
● Differences between Data Science, Machine Learning and Deep Learning.
● Why Python for Data Science.
● Python vs R.
● Future of Data Science.
● Why Machine Learning so popular?
● Types of Learning in ML.
● Supervised Learning.
● Unsupervised Learning.
● Supervised vs Unsupervised.
● All about ML Algorithms.
● Data Science Job Market.
Software Installation:
● Python
● Anaconda
● Jupyter Notebook
Basic Python:
● Input / Output Functions
● Variables
Variables Data Structures: -
● Python Data Structures
● Lists
● Tuples
● Functions
Data Structures: -
● Python Arrays
● Sets
● Dictionaries
● Data Frame Loop & Condition:
• Loops (for, while)
• Python Conditions (if,elif,else)
Discussion on Important Libraries: -
● NumPy
● Pandas
● Vaex
● Matplotlib
● Seaborn
● Scikit Learn
● Keras
● TensorFlow
● Pytorch
All About Single Variable Linear Regression:
▪ What is Linear Regression?
▪ Uses of Linear Regression in Real Life.
▪ Straight Line
▪ Curve Line
▪ Slope
▪ Intercept
▪ Math: In Depth Intuition of Linear Regression
▪ Cost Function
▪ Lose Function
▪ Mean Absolute Error (MAE)
▪ Mean Squared Error (MSE)
▪ Minimizing the Cost: Gradient Decent Algorithm
▪ Create Data Set in CSV Format
▪ Analysis Data with Matplotlib
▪ Implement Single Variable Linear Regression with Python and Real Dataset
▪ Future Value Prediction
▪ Assignment (Real Data Set)
Feature Engineering:
▪ Different Types of Variables
▪ Work with Categorical Variables
▪ Measure of Central Tendency-
▪ Mean
▪ Median
▪ Mode
▪ Theory of One Hot Encoding
▪ One Hot Encoding with Python
▪ Theory of Label Encoding
▪ Label Encoding with Python
▪ Theory of Label Encoding
▪ Label Encoding with Python
▪ Theory of Ordinal Encoding
▪ Ordinal Encoding with Python
▪ Mean or Target Encoding
▪ Mean or Target Encoding with Python
▪ Assignment (Real Data Set)
Feature Engineering:
● What is Feature Scaling?
● Techniques of Feature Scaling in Machine Learning
● Theory of Normalization
● Normalization with Python
● Standardization
● Standardization with Python
● Theory of Robust Scaler
● Robust Scaler with Python
● Theory of Logarithmic Transformation
● Logarithmic Transformation with Python
● Theory of Reciprocal Transformation
● Reciprocal Transformation with Python
● Assignment (Real Data Set)
All About Multiple Variable Linear Regression:
● All about Gradient Decent in ML
● Linear Regression with Gradient Decent
● Math Behind Multiple Variable Linear Regression
● Handle Missing Values with Python (Mean & Median)
● Implement Multiple Variable Linear Regression with Python and Real Dataset
● R Squared Value
● Implement R Square with Python
● Simple ML Project: Future Profit Prediction Based on Previous Data
● Introduction to Kaggle.com & How to Download and Use Data Set
from Kaggle.com
● Assignment (Real Data Set)
Introduction to Classification Algorithms: All about Decision Tree
• Basic Logarithmic Operations.
• All about Tree.
• What is Decision Tree Algorithm?
• What is Entropy in Decision Tree?
• What is Information Gain?
• What is Gini Index?
• In Depth Mathematics Behind Decision Tree.
• Implementation of Decision Tree with Python.
• Visualize and Download Tree.
• Assignment (Real Data Set)
Result Analysis:
● Theory of Confusion Matrix.
● Confusion Matrix with Python.
● Accuracy.
● Precision.
● Recall.
● F1-Measure.
● Specificity.
● AUC Curve.
● ROC Curve.
● Assignment (Real Data Set).
● Project on: Cardiovascular Diseases Prediction using ML
All about Ensemble Algorithms:
● What are Ensemble Techniques in Machine Learning?
● Types of Ensemble Techniques.
● Theory of Random Forest.
● In Depth Mathematics Behind Random Forest.
● Random Forest with Python.
● Decision Tree Vs Random Forest
Hyper Parameter Tuning in Machine Learning:
● Random Search for Classification
● Grid Search for Classification
● Genetic Algorithm
Logistic Regression:
● What is Logistic Regression?
● What is Sigmoid Function?
● In Depth Mathematics Behind Logistics Regression Algorithm.
● Logistic Regression with Python
● Linear Regression Vs Logistic Regression
● Simple ML Project: Heart Attack Prediction with Python & ML
● Assignment (Real Data Set)
Feature Engineering:
● What is Feature Selection in Machine Learning?
● Theory of Principle Component Analysis.
● Principle Component Analysis with Python.
● Different Types of Feature Selection Methods.
● Chi Square Test with Python.
● Select KBest.
● Select kBest with Python.
● Correlation Matrix.
● Correlation Matrix with Heatmap.
● Imbalance Dataset
● Feature Sampling using SMOTETomek
● Under Sampling using NearMiss
● Over Sampling using RandomOverSampler
● Assignment (Real Data Set).
All about K-Nearest Neighbors:
● What is KNN Algorithm?
● Euclidean Distance Formula.
● KNN for Classification.
● KNN for Regression.
● In Depth Mathematics Behind K-Nearest Neighbors (KNN)
Algorithm.
● KNN Regressor vs KNN-Classifier.
● Tuning: KNN Regress and KNN Classifier
● Implementing KNN with Python
● Assignment (Real Data Set
Important Statistical Analysis:
● Hypothesis Testing (Type 1 & Type 2 Error.
● What is Analysis of Variance (ANOVA)?
● Example of ANOVA Test.
● What is T-Test?
● Example of T Test.
● ANOVA Vs T-Test.
● P Value, T-test, ANOVA When to Use What, Implementation with
Python.
● Z Score Statistics.
● All About Correlation Analysis.
● Normal Distribution
● Removing Outliers with Python
All about Cross Validation:
● What is Cross Validation in Machine Learning?
● Cross Validation Techniques.
● Theory of K Fold Cross Validation.
● Hold Out Cross Validation
● K-Fold Cross Validation
● Leave One-Out Cross Validation (LOOCV)
● Stratified K Fold Cross Validation
● Train Test Split Vs K Fold CV.
● Assignment (Real Data Set).
All about Support Vector Machine:
● Theory of Support Vector Machine (SVM) in Machine Learning.
● Hyperplanes and Support Vectors.
● Math Behind SVM.
● SVM Kernels
● Assignment (Real Data Set)
● SVM for Linear Data
● SVM for Non-Linear Data
● SVM Implementation with Python.
Feature Engineering:
● What is Feature Extraction Techniques?
● Bag of Words Model in NLP.
● What is Count Vectorizer?
● Count Vectorizer with Python.
● What is Tfidf Vectorizer?
● Tfidf Vectorizer with Python.
● What is Hashing Vectorizer?
● Hashing Vectorizer with Python.
● What is Word2vec?
● Word2vec with Python.
● Countvectorizer vs Tfidfvectorizer vs Hashing
● Uses of Vectorizer in NLP.
● Use of Natural Language Toolkit in NLP (NLTK)
● Lemmatisation in NLP
● WordNetLemmatizer in NLP
● Stemming in NLP
● PorterStemmer in NLP
● Assignment (Real Data Set)
All about Naïve Bayes:
● What is Bayes Theorem?
● Statistics & Probability
● Statistics & Probability with Python
● Naïve Bayes Algorithm
● Naïve Bayes Algorithm with Python
● Naïve Bayes for Text Classification
● Gaussian NB, Bernoulli NB, MultiNomial NB
● Simple ML Project: Spam Comments Classification with Python
● Assignment (Real Data Set)
All about Xgboost & Adaboost:
● Why Ensemble Learning?
● What is Bagging?
● Why Boosting?
● Math Behind Xgboost Classifier and Regressor?
● Xgboost with Python
● All about Adaboost
● Math Behind Adaboost
● Adaboost with Python
● Assignment on Xgboost and Adaboost
Cluster Algorithms:
● What is Unsupervised Learning?
● Types of Clusters.
● Theory of K-Means Cluster Algorithm.
● Single & Multiple Variable Cluster.
● K-Means Cluster with Python.
● Hierarchical Clustering.
● Optimal Number of Cluster Selection.
● Elbow Method.
● Elbow Method with Python.
● Simple ML Project: Market Basket Analysis.
● Assignment (Real Data Set)
Neural Network:
● All about Neural Network
Class 21
● Tensorflow vs Pytorch
● What is Deep Learning?
● Types of Neural Network
● What is Neuron?
● Human Brain Vs Artificial Neuron
● All about Artificial Neural Network (ANN)
● All about Convolutional Neural Network (CNN)
● Kernels, Relu, Convolution
● Data Augmentations
Guidelines: ● Scope of Higher Studies in Data Science. ● Guide to be a Good Programmer. ● Sharing Experience for Data Science Journey. ● Machine Learning for Future Research. ● R for Data Science. ● Kaggle Competitions. ● ML Jobs, Resume & Salary. ● ML Interview Questions