• Overview
• Creating ndims arrays
• Why do we need arrays?
• Numeric operations using NumPy
• Indexing and slicing
• Some Mathematical functions
• Generate Random array

• Pandas Overview
• Data Structures
• Series
• DataFrame
• Series and DataFrame operations
• Missing Data
• Categorical Data
• Working on DateTime data
• Read data from the different file format
• Merging and Grouping Data
• Many other data operations using Pandas

• Overview
• Scatter plot, line plot, bar plot
• Histogram
• Xlabel, Ylabel, Xticks, Yticks, title
• Marker style,type, size
• Figure and Subplot
• Saving a Figure
• HeatMap,BoxPlot

• What is NLP?
• NLP libraries
• NLP Applications
• Cleaning text data
• Tokenization
• Removal Stop words
• Stemming and Lemmatization
• part-of-speech(POS) tagging

• Regression
• Introduction
• Simple Linear Regression
• Multiple Linear Regression
• Polynomial Regression
• Evaluate Performance of a linear regression model
• Overfitting and underfitting
• Regularization
• Logistic Regression
• Logistic Regression theory
• Implementing Logistic regression with scikit-learn
• Logistic Regression Parameters
• MNIST digit dataset with Logistic Regression
• Predictive modeling on adult income dataset
• Naive Bayes Classification
• Theory Naive Bayes Algorithm
• Features Extraction
• Countvectorizer
• TF-IDF
• Email Spam filtering
• Sentiment analysis
• Decision Tree and Random Forest
• The theory behind the decision tree
• Implementing a decision tree with scikit-learn
• Decision tree parameters
• Combining multiple decision trees via Random forest
• How random forest works..?
• Model Evaluation and Parameter Tuning
• Cross-validation via K-Fold
• Tuning hyperparameters via grid search
• Confusion matrix
• Recall and Precision
• ROC and AUC
• Clustering and Dimension Reduction
• K-means Clustering
• Elbow method
• Principal components analysis(PCA)
• PCA step by step
• Implementing PCA with scikit-learn

• Introduction to Machine learning(ML)
• Overview of major types: supervised and unsupervised
• Major steps in ML, Overview of NumPy library
• Lab: AIML Lab 1

• Pandas, data structure in pandas: Series and DataFrame
• Introduction to Matplotlib
• Lab: AIML Lab 2

• Linear Regression Algorithm, Mean squared error(MSE), R2_score
• Lab: AIML Lab 3

• Non-linear Regression, Overfitting, and Underfitting
• Lab: AIML Lab 4

• Introduction to KNN (K Nearest Neighbor), working of KNN, Decide the value of K, Accuracy score
• Lab: AIML Lab 5

• Evaluation technique for classification, confusion matrix, Recall, Precision
• Hyper-parameter tuning using GridSearchCV
• Lab: AIML Lab 6

• Introduction to Logistic Regression, working of it, Binary classification and Multi-class classification
• Lab: AIML Lab 7

Project: AIML Project - 1

• Feature extraction, Bag of words, Countvectorizer and TfidfVectorizer
• Lab: AIML Lab 8

• Introduction to Naive Bayes algorithm and working of Naive Bayes algorithm
• Lab: AIML Lab 9

• SVM (support vector Machine), linear and Non-linear SVM, decide Hyperparamters C and kernel
• Hyper-parameter tuning of C and kernel
• Lab: AIML Lab 10

• Introduction to Decision tree algorithm, Gini Index, Pruning technique
• Lab: AIML Lab 11

• What is a Random Forest algorithm? Working on it, and how does it differ from the decision tree?
• Lab: AIML Lab 12

• PCA, working of PCA, steps in PCA
• Lab: AIML Lab 13

Project: AIML Project - 2

• What is clustering?, K-means clustering algorithm, Elbow method
• Lab: AIML Lab 14

• Basic Overview of Neural Network, Single-layer Neural network, and Multi-layer neural Network
• Keras API, Activation functions, feed-forward Neural network
• Lab: AIML Lab 15

• Basic Introduction to Convolutional Neural Network(CNN), CNN Architecture, Convolution layer, Pooling layer,
• Dense layer
• Lab: AIML Lab 16

Project: AIML Project - 3

Doubt clearing session