Data Science With Machine Learning , Deep Learning & AI

Data Science With Machine Learning , Deep Learning & AI course is offered by Pythonsoft. Pythonsoft is a leading software training institute in India to provide quality training to students. As a training institute, we try to provide strong programming skills to our students. We provide both conceptual and practical-oriented training from very basic to advanced levels.

Content:

• Chapter 1
• Introduction
• What is data science
• What is machine learning?
• What is Deep learning?
• Difference between DS and ML and AI and DL
• Chapter -2
• Basics of python
• Installing python
• Different IDES
• Variables
• Data types
• Loop
• function
• module and package
• object oriented programming
• python packages numpy,sklearn,matplotlib,pandas
• Working with ANACONDA
• Chapter -3
• Numpy
• Introduction to Numpy
• Creating numpy array
• Attributes of numpy array
• Advantage of Numpy array over List
• Mathematical operation on numpy array
• Different ways to create numpy array
• Reshaping numpy array
• Concatenation and splitting operation
• Trigonometric functions
• Random sample generation
• Chapter -4
• Data analysis with Pandas
• Pandas series
• Pandas data frame
• Reading CSV files
• Parameters of read_csv()
• Read excel files
• Handling missing values
• categorical data
• Data cleaning and pre processing
• Chapter -5
• Data Visualization
• Matplotlib
• Seaborn
• Chapter-6
• Regression
• Linear Regression
• Multiple linear regression
• Polynomial Regression
• Logistic regression
• Chapter-7
• Logistic Regression
• Introduction
• Logistic function or sigmoid function
• Types of logistic regression
• Implementation
• Chapter-8
• K-Nearest Neighbors Algorithm
• How KNN works
• KNN classifier
• Confusion Matrix
• KNN Regressor
• How to choose k value
• Chapter -9
• Naïve Bayes Algorithm
• Bayes Theorem
• Types of naïve bayes classifier
• Bernoulli naïve bayes
• Gaussian Naïve
• Multinomial NB
• Text Processing
• Chapter -10
• Decision Tree Algorithm
• Why to use decision trees?
• Decision Tree Terminologies
• How a decision tree works
• Advantages and disadvantages
• Chapter -11
• Random Forest Algorithm
• What is random forest
• How random forest works
• Ensemble learning
• Bagging and boosting
• Advantages and disadvantages
• Chapter -12
• Support vector machine
• What is support vector machine?
• Types of SVM
• Hyper plane and support vectors
• How support vector works?
• Chapter-13
• Unsupervised Learning
• What is unsupervised learning
• Types of unsupervised learning
• Applications of unsupervised learning
• K-means clustering
• Chapter 14
• Feature engineering and Dimensional Reduction
• feature extraction
• feature selection
• dummy variable and one hot encoding
• Label encoding and ordinal encoding
• Feature scaling
• Hyper parameter tuning
• dimension reduction (feature reduction)
• Chapter-15
• Model selection
• What is Model Selection?
• The need for Model Selection
• Cross-Validation
• What is Boosting?
• How Boosting Algorithms work?
• Types of Boosting Algorithms
• Chapter -16
• Time series prediction
• What is Time Series Analysis?
• Importance of TSA
• Components of TSA
• White Noise
• AR model
• MA model
• ARMA model
• ARIMA model
• Chapter -17
• Neural Network (Deep learning)
• What is Neural Network
• What is neuron and how it works
• How neural network works
• Perceptron and multi-layer neural network
• Types of neural networks
• Implementing ANN using keras
• CNN(convolution Neural Network )
• Chapter -18
• NLP (natural Language processing)
• Why Natural Language Processing (NLP)
• Applications of Text Mining
• Reading, Writing to text and word files
• Setting the NLTK Environment
• Accessing the NLTK Corpora
• Tokenization
• Frequency Distribution
• Different Types of Tokenizers
• Bigrams, Trigrams & Ngrams
• Stemming
• Lemmatization
• Stopwords
• Chapter-19
• Project work