Will explain machine learning algorithms and uses and how to implement them in Python with industry-ready use cases, basic statistics, and mathematics will be added on. This will include both supervised and unsupervised machine learning algorithms, some examples include regression, clustering, segmentation

Data Handling with NumPy

• NumPy Arrays, CRUD Operations, etc.
• Linear Algebra – Matrix multiplication, CRUD operations, Inverse, Transpose, Rank, Determinant of a matrix, Scalars, Vectors, Matrices.

Data Manipulation using Pandas

• Loading the data, data frames, series, CRUD operations, splitting the data, etc.

Data Preprocessing

• Exploratory Data Analysis, Feature engineering, Feature scaling, Normalization, standardization, etc.
• Null Value Imputations, Outliers Analysis and Handling, VIF, Bias-variance trade-off, cross-validation techniques, train-test split, etc.

Data Visualization

• Bar charts, scatter plots, count plots, line plots, pie charts, donut charts, etc. with python matplotlib.
• Regression plots, categorical plots, area plots, etc, with python seaborn.

Text Mining, Cleaning, and Pre-processing

• Various Tokenizers, Tokenization, Frequency Distribution, Stemming, POS Tagging, Lemmatization, Bigrams, Trigrams & Ngrams, Lemmatization, Entity Recognition.

Text Classification, NLTK, and Sentiment Analysis  

• Overview of Machine Learning, Words, Term Frequency, Countvectorizer, Inverse Document Frequency, Text Conversion, Confusion Matrix, Naive Bayes Classifier.

Sentence Structure, Sequence Tagging, Sequence Tasks, and Language Modeling

• Language Modeling, Sequence Tagging, Sequence Tasks, Predicting Sequence of Tags, Syntax Trees, Context-Free Grammars, Chunking, Automatic Paraphrasing of Texts, Chinking.

AI Chatbots and Recommendations Engine 

• Using the NLP concepts, build a recommendation engine and an AI chatbot assistant using AI.