Data science with AI course in Delhi NCR

Topics Covered:

1- Introduction to Python – print(), input(), Comments, Variables, Built-in Data Types.

2- Basics of Python – Strings and its methods, Python Booleans, Operators (Arithmetic, Logical, Comparison, Assignment, Identity, Memebership, Bitwise), Slicing and Indexing.

3- Python Data Structures:
3.1- Lists – Access, Change, Add, Remove, Loop, Sort, Copy, Join, List Methods, List Comprehension.
3.2- Tuples – Access, Update, Unpack, Loop, Join, Tuple Methods
3.3- Sets – Access, Add, Remove, Loop, Join, Set Methods
3.4- Dictionary – Access, Change, Add, Remove, Loop, Copy, Nested Dictionaries, Dictionary Methods.

4- Python Conditional Statements – IF, ELIF, ELSE

5- Loops in Python:
5.1- For Loops – Introduction, break, pass, Nested Loops
5.2- While Loops – Break, Continue, Else, Conditional Statements in While Loops.

6- Python Functions – Creating, Calling, Arguments, Arbitrary Arguments, Keyword Arguments, Arbitrary Keyword Arguments.

7- Python Classes and Objects
7.1- Introduction – Creation, init (), str (), Object Methods, SELF Statement, PassStatement
7.2 Inheritence – Creation, Parent Class, Child Class, init (), super(), Inheritence Methods.

8- Miscellaneous – Datetime, RegEx, String Formatting, TRY/EXCEPT.

Topics Covered:

1- NumPy – Numerical Python
1.1- Introduction – Arrays (Indexing, Dimensions, Slicing, Shape, Reshape, Iteration, Joins, Split, Search, Sort, Filter, Copy Vs View.
1.2- Random – Introduction, Pseudo vs True Random, Shuffle and Permutations, Random Distributions (Poisson, Normal, Binomial, etc.)

2- Data Wrangling using PANDAS
2.1- Introduction to Pandas – Installation (using PIP) and Import
2.2- Pandas Series and DataFrames – Difference, Labels, Key/Value Objects
2.3- Pandas DataFrames Tutorial – View, Info, Description, Location of Rows and Columns, Named Indexes, CSV/EXCEL Files
2.4- Pandas DataFrames Advanced Tutorial – Cleaning of Data, Handling Null Values (Dropna/Fillna), Handling Duplicate Values, Handling Data in Wrong Format
2.5- Plotting of Data Using Pandas.

3- Data Visualization Using Matplotlib and Seaborn
3.1- Introduction to Data Visualization – Installation (using PIP) and import of Matplotlib and Seaborn, Basics of Plotting (Labels, X Axis, Y Axis, Headings)
3.2- Matplotlib and Seaborn – Line Plots, Bar Graphs, Scatter plots, Pie Charts, Heatmaps for Correlation, etc.
3.3- Exploring Outliers and Data Distributions – Boxplots, Histograms, KDE Plot, Violin Plot, Pair Plot, etc
3.4- Advanced Plotting Tutorial – Adding a third Axis on a 2D plot, Fonts, Styles, Subplots, Axis, Grids, Texts on Plots.

4- Web Srcaping using Beautiful Soup
4.1- Introduction to Web Scraping and Beautiful Soup – Installation and Importing
4.2- Scrape HTML Content from a Page, Parse HTML Code with BeautifulSoup, Find Elements by ID, Find Elements by HTML Class Names, Extract Texts, Identify Errors, Extract Attributes from HTML Elements.
4.3- Building a Script using Web Scraping

Topics Covered:

1- Introduction to Statistics – Data Types: Numeric (Continuous, Discrete), Categorical (Binary, Ordinal, Nominal), Rectangular.

2- Descriptive Statistics
2.1- Estimates of Location (Mean, Weighted Mean, Trimmed Mean, Median, Weighted Median, Mode, Outliers)
2.2- Estimates of Variability (Deviations, Variance, Standard Deviation, Mean Absolute Deviation, Median Absolute Deviation,
2.3- Skewness and Kurtosis

3- Sampling Techniques – Bias Sample, Population, Random Sampling, Stratified Sampling, Simple Random Sampling, Bootstrap, Resampling

4- Inferential Statistics – Confidence Intervals, Normal Distribution (Z score, QQ-Plot), T-Distrubtion and T-test, Binomial Distribution, Chi- Square Distribution and Chi-Square Test, F-Distribution, F-test, ANOVA Test, Poisson Distribution, Exponential Distribution, Weibull Distribution

5- A/B Testing (Treatment Group, Control Group), Hypothesis Testing (Type 1 Error, Type 2 Error, Significance Value (Alpha),

6- Correlation Coffecient, Coefficient of Determination, Simple Linear Regression in Statistics.

MID COURSE TEST – 1
CAPSTONE PROJECT – 1

Topics Covered:

1- Introduction to Machine Learning
1.1- What is ML, Why ML, Types of ML, (Training, Validation, and Testing Set)
1.2- Train/Test Split, Preprocessing of Data (LabelEncoder, OneHotEncoder), Standardization of Data
1.3- Hyperparameters, Selection and Fine Tuning of Models, (Main Challenges – Overfitting, Underfitting, Poor Quality Data, Irrelavant Features, etc.)

2- Classification Techniques
2.1- Performance Metrics – Accuracy, Recall, Precision, F1 Score, Confusion Matrix, Classification Report, Precision/Recall Tradeoff, ROC Curve, AOC Curve
2.2- Classification Models – Gradient Descent and Stochastic Gradient Descent, Logistic Regression, K Nearest Neighbors (KNN), Naive Bayes, Support Vector Machines (SVM),
Linear Discriminant Analysis (LDA), Decission Trees
2.3- Ensembling Methods – Bagging (Voting Classifer, Cross Validation, etc.), Boosting (XG Boost, Adaboost, etc.), Random
2.4- Advanced Techniques – Hyperparameter Tuning, GridSearchCV, RandomizedSearchCV, Multilabel Classification, L1 and
2.5- Classification Project – Real World Use Case.

3- Regression Techniques
3.1- Introduction – Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, Cost Function and Gradient
3.2- Performance Metrics – Mean Squared Error, Root Mean Squared Error, Mean Absolute Error, etc.
3.3- Challenges – Heteroskedasticity, Non – Normality of Data, Multicollinearity of Data, etc.
3.4- Regression Models – Decision Tree Regressor, Support Vector Machine (SVM), K Nearest Neighbors (KNN)
3.5- Ensemble Models – Cross Validation, Voting Classifier, Random Forest, Bagging and Boosting Methods
3.6- Advanced Techniques – Hyperparameter Tuning, GridSearchCV, RandomizedSearchCV, L1 and L2 Regularization
3.7 Regression Project – Real World Use Case.

4- Unsupervised Learning
4.1- Introduction to Unsupervised Learning
4.2- Clustering Methods – KMeans, Hierarchical, Model Based Clustering, DBSCAN Clustering, Anamoly Detection using
4.3- Dimensionality Reduction using Principal Component Analysis
4.4- Building and Working of Recommendation Engines.

MID COURSE TEST – 2
CAPSTONE PROJECT – 2

Topics Covered:

1- Introduction to Artificial Neural Networks
1.1- Biological to Artificial Neurons
1.2- The perceptron
1.3- Multi-layer Perceptrons (MLPs)
1.4- Input Layer, Hidden Layers and Output layers
1.5- Weights and Biases
1.6- Regression MLPs
1.7- Classification MLPs
1.8- Activation functions and Optimizers.

2- Implementation using Tensorflow and Keras
2.1- Building a Neural Network using Sequential API
2.2- Building a Neural Network using Functional API
2.3- Building a Neural Network using Subclassing API
2.4- Saving and Restoring a Model
2.5- Callbacks

3- Training Deep Neural Networks
3.1- Vanishing/Exploding Gradients Problem
3.2- Batch Normalization
3.3- Gradient Clipping
3.4 Transfer Learning – Using Pretrained Layers
3.5- Pretraining on Auxiliary Task
3.6- Faster Optimizers – RMSprop, AdaGrad, Adam, Nadam, Nesterov Accelerated Gradient
3.7- Learning Rate Scheduling.

4- Fine Tuning Models
4.1- How to choose number of hidden layers and number of Neurons
4.2- Learning Rate, Optimizer, Batch Size and Activation Functions
4.3- L1 and L2 Regularization
4.4- Dropouts and Batch Normalization
4.5- Max Norm Regularization

Topics Covered:

1- Introduction to Computer Vision
1.1- The Architecture of Visual Cortex
1.2- Convolutional Layers
1.3- Feature Maps
1.4- Pooling
1.5- Padding
1.6- Stacking Multiple Feature Maps

2- Hands on Experience – Building an Image Classifier using CNN

3- Object Detection, Image Segmentation, and Semantic Segmentation.

4- CNN Architectures
4.1- Learning Predefined Architectures – LeNet, AlexNet, GoogleLeNet, ResNet, VGGNet, Xception, SENet
4.2- Transfer Learning – Using Pretrained Models from Keras
4.3- Classification and Localization

Topics Covered:

1- Introduction to Natural Language Processing
1.1- Overview of NLP and its Applications.
1.2- Data Preprocessing for NLP
1.3- Key Components – Tokenization, Stemming and Lemmatization
1.4- Hands on Experience – Generating AI Text
1.5- Sentiment Analysis in NLP using Keras.

2- Neural Machine Translation (NMT)
2.1- Bidirectional Recurrent Neural Networks
2.2- Beam Search
2.3- Sequence to Sequence Model
2.4 Building a basic Encoder Decoder Network for NMT.

3- Attention Mechanism
3.1- Introduction to Attention Mechanisms
3.2- Visual Attention
3.3- The Transformer Architecture
3.4- Fine Tuning NLP Models for NLP Tasks.

4 Hands on Experience – Building a Basic Chatbot

Topics Covered:

1- Processing Sequences using Recurrent Neural Networks
1.1- Introduction to Recurrent Neurons and Layers
1.2- Memory Cells
1.3- Implementation and Training of Recurrent Neural Networks
1.4- Time Series using Recurrent Neural Networks
1.5- Deep RNNs for Time Series
1.6- Forecasting Several Time Steps Ahead
1.7- Handling Long Sequences using LSTM and GRU cells.

2- Autoencoders
2.1- Introduction to Autoencoders
2.2- Encoder Decoder Networks
2.3- Stacked Autoencoders
2.4- Reconstructing Fashion MNIST Data using Autoencoders
2.5- Types of Autoencoders – Convolution, Recurrent, Denoising, Sparse and Variational Autoencoders
2.6- Anamoly Detection using Autoencoders.

3- Generative Adversarial Networks
3.1- What are GANs? Why GANs?
3.2- Generator and Discriminator
3.3- Building a Deep Convolutional GAN on Fashion MNIST Data.

4- Reinforcement Learning
4.1- What is Reinforcement Learning?
4.2- Learning to Optimize Rewards
4.3- Policy Search
4.4- Hands on Experience using Open AI Gym
4.5- The Credit Assignment Problem
4.6- Q Learning and Deep Q Learning
4.7- Implementing Deep Q Learning using keras

PROFESSIONAL SOFT SKILL SESSIONS
FINAL CAPSTONE PROJECT
FINAL EXAM