Data Science & Ai Masters 2025 - From Python To Gen Ai

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Data Science & Ai Masters 2025 - From Python To Gen Ai
Published 1/2025
Created by Satyajit Pattnaik,Zep Tech Solutions
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 428 Lectures ( 79h 16m ) | Size: 39 GB
Master Data Science and AI: Learn Python, EDA, Stats, SQL, Machine Learning, NLP, Deep Learning and Gen AI​

What you'll learn
Build a solid foundation in Python programming to effectively implement AI concepts and applications.
Learn how Machine Learning & Deep Learning works
Learn how transformer models revolutionize NLP tasks, and how to leverage them for various applications.
Gain hands-on experience with Retrieval-Augmented Generation (RAG) and Langchain for building advanced AI applications.
Learn how to utilize vector databases for efficient storage and retrieval of embeddings in AI projects.
Understand the complete pipeline of Natural Language Processing, from data preprocessing to model deployment.
Explore the essentials of Large Language Models (LLMs) and their applications in generative tasks.
Develop skills in crafting effective prompts to optimize model performance and achieve desired outputs.
Requirements
No programming experience is needed.
A laptop with atleast 8gb memory is recommended (4gb memory is also fine, can use Google Colab as a backup)
No prior knowledge on SQL, or Machine Learning is needed
Description
Welcome to Data Science & AI Masters 2025 - From Python To Gen AI! This comprehensive course is designed for aspiring data scientists and AI enthusiasts who want to master the essential skills needed to thrive in the rapidly evolving field of data science and artificial intelligence. Whether you're a beginner or looking to enhance your existing knowledge, this bootcamp will guide you through every step of your learning journey.What You Will LearnIn this bootcamp, you will gain a solid foundation in key concepts and techniques, includingython Programming: Start with the basics of Python, the most popular programming language in data science, and learn how to write efficient code.Exploratory Data Analysis (EDA): Discover how to analyze and visualize data to uncover insights and patterns.Statistics: Understand the statistical methods that underpin data analysis and machine learning.SQL: Learn how to manage and query databases effectively using SQL.Machine Learning: Dive into the world of machine learning, covering algorithms, model evaluation, and practical applications.Time Series Analysis & Forecasting: Explore techniques for analyzing time-dependent data and making predictions.Deep Learning: Get hands-on experience with neural networks and deep learning frameworks.Natural Language Processing (NLP): Learn how to process and analyze textual data using NLP techniques.Transformers and Generative AI: Understand the latest advancements in AI, including transformer models and generative AI applications.Real-World Projects: Apply your skills through engaging projects that simulate real-world data challenges.Course StructureThe bootcamp is structured into modules that build upon each other, ensuring a smooth learning experience. Each module includes video lectures, hands-on exercises, and quizzes to reinforce your understanding. By the end of the course, you will have a robust portfolio of projects showcasing your skills and knowledge.ConclusionJoin us in The Complete DS/AI Bootcamp and take the first step towards a rewarding career in data science and artificial intelligence. With the demand for data professionals on the rise, this course will equip you with the skills needed to excel in this exciting field. Enroll now and start your journey to becoming a proficient data scientist and AI expert!
Who this course is for
Complete beginners willing to jump into the field of Data Science, AI & Gen AI
Experienced Professionals willing to switch to Data Science, AI & Gen AI
Homepage

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38.98 GB | 34min 13s | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English​

Files Included :

Spoiler

1 -Welcome Page.mp4 (51.49 MB)
1 -Transformer Types.mp4 (127.41 MB)
10 -BERT Pre Tuning (Masked LM).mp4 (50.1 MB)
11 -BERT Input Embeddings.mp4 (62.12 MB)
12 -ARLM vs AELM.mp4 (43.4 MB)
13 -RoBERTa.mp4 (60.41 MB)
14 -DistilBERT.mp4 (92.38 MB)
15 -AlBERT.mp4 (112.4 MB)
16 -Introduction to GPT (Decoder Only).mp4 (30.54 MB)
17 -GPT Architecture.mp4 (27.73 MB)
18 -GPT Masked Multi Head Attention.mp4 (85.96 MB)
19 -GPT Blocks.mp4 (48.92 MB)
2 -Introduction to Transformers.mp4 (145.8 MB)
20 -GPT Training.mp4 (54.63 MB)
21 -LLM Basics Context Window.mp4 (56.2 MB)
22 -LLM Basics Prompt.mp4 (63.94 MB)
23 -LLM Basics Prompt Engineering.mp4 (119.88 MB)
24 -LLM Basics Prompt Tuning.mp4 (57.09 MB)
25 -LLM Basics Prompt Structures.mp4 (106.9 MB)
26 -RAGs Introduction to RAG.mp4 (5.72 MB)
27 -RAGs What and Why.mp4 (119.24 MB)
28 -RAGs Use Cases.mp4 (138.77 MB)
29 -RAGs Paper Explanation.mp4 (53.32 MB)
3 -Self Attention.mp4 (125.4 MB)
30 -RAGs Architecture Explanation.mp4 (106.51 MB)
31 -RAGs Detailed Architecture Walkthrough.mp4 (74.79 MB)
32 -RAGs Practical Use Cases.mp4 (256.53 MB)
33 -LangChain.mp4 (83.3 MB)
34 -Introduction to Prompt Engineering.mp4 (80.96 MB)
35 -Types of Prompting.mp4 (90.41 MB)
36 -Few Shot Limitations.mp4 (50.66 MB)
37 -Chain of Thoughts Prompting.mp4 (45.02 MB)
38 -Vector Databases.mp4 (86.44 MB)
39 -Vector Database vs Vector Index.mp4 (60.82 MB)
4 -Encoder Architecture.mp4 (47.99 MB)
40 -How Vector Databases works.mp4 (72.07 MB)
41 -Vector Database (Practicals).mp4 (260.83 MB)
42 -LSH.mp4 (85.36 MB)
5 -Contextual Embeddings.mp4 (30.2 MB)
6 -Decoder Architecture.mp4 (31.39 MB)
7 -Introduction to BERT.mp4 (72.09 MB)
8 -Configurations of BERT.mp4 (25.37 MB)
9 -BERT Fine Tuning.mp4 (21.34 MB)
1 -Deployment Basics.mp4 (27.39 MB)
2 -Introduction to Flask.mp4 (72.2 MB)
3 -Flask Basic App.mp4 (88.42 MB)
4 -Model Building (Breast Cancer Prediction).mp4 (135.84 MB)
5 -Flask App (Breast Cancer Prediction).mp4 (187.83 MB)
6 -AWS.mp4 (58.89 MB)
7 -AWS Deployment (Breast Cancer Prediction).mp4 (238.25 MB)
1 -Introduction to Data Engineering.mp4 (3.25 MB)
10 -Data lake vs Data Warehouse.mp4 (28.52 MB)
11 -Elements of Datalake.mp4 (14.58 MB)
2 -What is ETL.mp4 (39.27 MB)
3 -ETL Tools.mp4 (25.14 MB)
4 -What is Data Warehouse.mp4 (26.63 MB)
5 -Benefits of Data Warehouse.mp4 (18.9 MB)
6 -Data Warehouse Structure.mp4 (19.13 MB)
7 -Why do we need Staging.mp4 (30.38 MB)
8 -What are Data Marts.mp4 (11.84 MB)
9 -Data Lake.mp4 (22.44 MB)
1 -ChatScholar (EdTech Project).mp4 (399.7 MB)
2 -Research RAG Chatbot.mp4 (295.13 MB)
3 -Automated AI Claims Processing using Gen AI.mp4 (403.93 MB)
4 -Multi PDF RAG Chatbot built on Web Scraped Data.mp4 (309.7 MB)
5 -AI Career Coach Part 1.mp4 (93.98 MB)
6 -AI Career Coach Part 2.mp4 (112.49 MB)
7 -AI Career Coach Part 3.mp4 (223.18 MB)
1 -Introduction to Python.mp4 (94.25 MB)
10 -Map Reduce Filter.mp4 (514.75 MB)
11 -File Handling.mp4 (327.29 MB)
12 -Control Structures.mp4 (171.9 MB)
13 -OOPs.mp4 (335.18 MB)
14 -NumPy.mp4 (485.43 MB)
15 -Pandas.mp4 (567.4 MB)
16 -Data Visualization.mp4 (113.44 MB)
17 -Matplotlib.mp4 (449.69 MB)
18 -Seaborn.mp4 (325.23 MB)
2 -Variables & Keywords.mp4 (352.33 MB)
3 -Datatypes Operators.mp4 (364.22 MB)
4 -Lists.mp4 (465.6 MB)
5 -Tuples.mp4 (424.24 MB)
6 -Sets.mp4 (220.6 MB)
7 -Dictionary.mp4 (297.26 MB)
8 -Loops & Iterations.mp4 (336.17 MB)
9 -Functions.mp4 (393.85 MB)
1 -Introduction.mp4 (42.33 MB)
10 -Types of Sampling.mp4 (20.74 MB)
11 -Cluster Random Sampling.mp4 (30.59 MB)
12 -Probability Sampling.mp4 (40.69 MB)
13 -Non probability sampling.mp4 (31.45 MB)
14 -Population Sampling.mp4 (56.37 MB)
15 -Why n-1 and not n.mp4 (32.83 MB)
16 -Descriptive Analytics (Agenda).mp4 (4.97 MB)
17 -Measures of Central Tendency.mp4 (9.09 MB)
18 -Mean.mp4 (26.98 MB)
19 -Median.mp4 (36.45 MB)
2 -Types of Data (Agenda).mp4 (3.18 MB)
20 -Mode.mp4 (28.12 MB)
21 -Measures of Dispersion.mp4 (21.57 MB)
22 -Range.mp4 (7.26 MB)
23 -IQR.mp4 (19.47 MB)
24 -Variance Standard Deviation.mp4 (56.38 MB)
25 -Mean Deviation.mp4 (18.94 MB)
26 -Probability (Agenda).mp4 (4.67 MB)
27 -Probability.mp4 (41.99 MB)
28 -Addition Rule.mp4 (45.38 MB)
29 -Independent Events.mp4 (25.97 MB)
3 -Descriptive Stats.mp4 (79.79 MB)
30 -Cumulative Probability.mp4 (29.42 MB)
31 -Conditional Probability.mp4 (57.91 MB)
32 -Bayes Theorem 1.mp4 (9.45 MB)
33 -Bayes Theorem 2.mp4 (24.97 MB)
34 -Probability Distrubution (Agenda).mp4 (10.38 MB)
35 -Uniform Distribution.mp4 (44.12 MB)
36 -Binomial Distribution.mp4 (70.84 MB)
37 -Poisson Distribution.mp4 (18.84 MB)
38 -Normal Distribution Part 1.mp4 (77.48 MB)
39 -Normal Distribution Part 2.mp4 (34.26 MB)
4 -Inferential Stats.mp4 (13.38 MB)
40 -Skewness.mp4 (25.21 MB)
41 -Kurtosis.mp4 (14.06 MB)
42 -Calculating Probability with Z-score for Normal Distribution Part 1.mp4 (49.64 MB)
43 -Calculating Probability with Z-score for Normal Distribution Part 2.mp4 (46.98 MB)
44 -Calculating Probability with Z-score for Normal Distribution Part 3.mp4 (27 MB)
45 -Covariance & Correlation (Agenda).mp4 (2.68 MB)
46 -Covariance.mp4 (54.35 MB)
47 -Correlation.mp4 (86.55 MB)
48 -Covariance VS Correlation.mp4 (26.64 MB)
49 -Hypothesis Testing.mp4 (52.89 MB)
5 -Qualitative Data.mp4 (50.03 MB)
50 -Tailed Tests.mp4 (16.83 MB)
51 -p-value.mp4 (32 MB)
52 -Types of Test.mp4 (26.87 MB)
53 -T Test.mp4 (51.26 MB)
54 -Z Test.mp4 (67.39 MB)
55 -Chi Square Test.mp4 (67.34 MB)
56 -ANOVA.mp4 (68.94 MB)
57 -Correlation Test (Practicals).mp4 (44.6 MB)
6 -Quantitative Data.mp4 (20.51 MB)
7 -Sampling Techniques (Agenda).mp4 (7.62 MB)
8 -Population vs Sample.mp4 (17.9 MB)
9 -Why Sampling is important.mp4 (17 MB)
1 -Agenda.mp4 (15.24 MB)
10 -Standardization Example.mp4 (22.66 MB)
11 -Normalization Example.mp4 (13.95 MB)
12 -Feature Scaling (Practicals).mp4 (111.56 MB)
13 -Outlier Treatment (Theory).mp4 (59.81 MB)
14 -Outlier Treatment (Practicals).mp4 (41.94 MB)
15 -Invalid Data.mp4 (42.51 MB)
16 -Types of Data.mp4 (10.97 MB)
17 -Types of Analysis.mp4 (12.03 MB)
18 -Univariate Analysis.mp4 (54.11 MB)
19 -Bivariate Analysis.mp4 (35.65 MB)
2 -DA,DS Processes.mp4 (24.27 MB)
20 -Multivariate Analysis.mp4 (5.23 MB)
21 -Numerical Analysis.mp4 (19.41 MB)
22 -Analysis Practicals.mp4 (211.54 MB)
23 -Derived Metrics.mp4 (26.53 MB)
24 -Feature Binning (Theory).mp4 (44.16 MB)
25 -Feature Binning (Practicals).mp4 (71.01 MB)
26 -Feature Encoding (Theory).mp4 (83.76 MB)
27 -Feature Encoding (Practicals).mp4 (169.81 MB)
28 -Case Study.mp4 (79.59 MB)
29 -Data Exploration.mp4 (151.44 MB)
3 -What is EDA.mp4 (27.14 MB)
30 -Data Cleaning.mp4 (73.51 MB)
31 -Univariate Analysis.mp4 (97.59 MB)
32 -Bivariate Analysis Part 1.mp4 (129.32 MB)
33 -Bivariate Analysis Part 2.mp4 (54.46 MB)
34 -EDA Report.mp4 (35.83 MB)
4 -Visualization.mp4 (30.52 MB)
5 -Steps involved in EDA (Data Sourcing).mp4 (28.8 MB)
6 -Steps involved in EDA (Data Cleaning).mp4 (30.44 MB)
7 -Handle Missing Values (Theory).mp4 (58.52 MB)
8 -Handle Missing Values (Practicals).mp4 (92.11 MB)
9 -Feature Scaling (Theory).mp4 (74.23 MB)
1 -Installation.mp4 (59.09 MB)
10 -Aggregation Functions.mp4 (211.19 MB)
11 -String functions.mp4 (287.89 MB)
12 -Date Time Functions.mp4 (233.88 MB)
13 -Regular Expressions.mp4 (160.7 MB)
14 -Nested Queries.mp4 (263.49 MB)
15 -Views.mp4 (222.76 MB)
16 -Stored Procedures.mp4 (439.12 MB)
17 -Windows Function.mp4 (365.77 MB)
18 -SQL Python connectivity.mp4 (341.27 MB)
2 -Data Architecture - File server vs client server.mp4 (119.34 MB)
3 -Introduction to SQL.mp4 (164.5 MB)
4 -Constraints in SQL.mp4 (289.4 MB)
5 -Table Basics - DDLs.mp4 (396.54 MB)
6 -Table Basics - DQLs.mp4 (290.03 MB)
7 -Table Basics - DMLs.mp4 (461.59 MB)
8 -Joins.mp4 (448.42 MB)
9 -Data Import Export.mp4 (545.58 MB)
1 -Agenda.mp4 (13.74 MB)
10 -Pre-Requisites Normalization Example.mp4 (13.95 MB)
11 -Pre-Requisites Feature Encoding.mp4 (83.76 MB)
12 -Pre-Requisites Feature Encoding (Practicals).mp4 (78.84 MB)
13 -Regression Introduction to Regression Models.mp4 (38.9 MB)
14 -Regression Regression Metrics.mp4 (151.37 MB)
15 -Regression Regression Metrics (Practicals).mp4 (102.58 MB)
16 -Regression Simple Linear Regression.mp4 (55.94 MB)
17 -Regression Multiple Linear Regression.mp4 (51.48 MB)
18 -Regression Linear Regression (Practicals).mp4 (210.8 MB)
19 -Regression Multiple Linear Regression (Practicals).mp4 (96.24 MB)
2 -Introduction to ML.mp4 (32.17 MB)
20 -Regression Polynomial Regression.mp4 (39.25 MB)
21 -Regression Polynomial Regression (Practicals).mp4 (176.58 MB)
22 -Regression Bias Variance Tradeoff.mp4 (30.95 MB)
23 -Regression Ridge Regression.mp4 (55.15 MB)
24 -Regression Lasso Regression.mp4 (43.86 MB)
25 -Regression Lasso & Ridge Regression (Practicals).mp4 (335.38 MB)
26 -Classification Introduction to Classification.mp4 (41.27 MB)
27 -Classification Types of Classification.mp4 (25.69 MB)
28 -Classification Log Loss.mp4 (63.61 MB)
29 -Classification Confusion Matrix.mp4 (72.79 MB)
3 -Types of ML.mp4 (104.93 MB)
30 -Classification AUC ROC Curve.mp4 (48.57 MB)
31 -Classification Classification Report.mp4 (47.22 MB)
32 -Classification kNN Classifier.mp4 (80.8 MB)
33 -Classification kNN Classifier Example.mp4 (78.01 MB)
34 -Classification Practicals Part 1.mp4 (100.52 MB)
35 -Classification kNN Classifier (Practicals).mp4 (115.41 MB)
36 -Classification Decision Tree.mp4 (72.98 MB)
37 -Classification Decision Tree (Entropy based).mp4 (112.46 MB)
38 -Classification Decision Tree (gini based).mp4 (104.23 MB)
39 -Classification Decision Tree (Practicals).mp4 (66.53 MB)
4 -Use Cases Part 1.mp4 (19.87 MB)
40 -Classification Decision Tree (Visualizing).mp4 (160.32 MB)
41 -Classification Random Forest Classifier.mp4 (40.94 MB)
42 -Classification Random Forest Classifier (Practicals).mp4 (46.69 MB)
43 -Classification Naive Bayes Classifier.mp4 (89.9 MB)
44 -Classification SVM Classifier Part 1.mp4 (71.78 MB)
45 -Classification SVM Classifier Part 2.mp4 (60.17 MB)
46 -Classification Logistic Regression.mp4 (119.15 MB)
47 -Classification Practicals so far.mp4 (218.7 MB)
48 -Classification Issues in Classification (Part 1).mp4 (48.41 MB)
49 -Classification Issues in Classification (Part 2).mp4 (80.24 MB)
5 -Use Cases Part 2.mp4 (8.04 MB)
50 -Classification Project.mp4 (308.88 MB)
51 -Ensemble Introduction to Ensemble Learning.mp4 (117.94 MB)
52 -Ensemble Bagging.mp4 (50.65 MB)
53 -Ensemble Bagging vs Random Forest.mp4 (91.07 MB)
54 -Ensemble Bagging (Practicals #1).mp4 (241.19 MB)
55 -Ensemble Bagging (Practicals #2).mp4 (178.06 MB)
56 -Ensemble Boosting.mp4 (41.91 MB)
57 -Ensemble Ada Boost.mp4 (97.89 MB)
58 -Ensemble Gradient Boost.mp4 (20.82 MB)
59 -Ensemble CF vs LF.mp4 (47.15 MB)
6 -Pre-Requisites Features.mp4 (88.74 MB)
60 -Ensemble Cross Entropy.mp4 (22.06 MB)
61 -Ensemble Xtreme Gradient Boosting (XGB).mp4 (94.38 MB)
62 -Ensemble Project.mp4 (210.58 MB)
63 -Clustering Introduction to Clustering.mp4 (104.01 MB)
64 -Clustering kMeans Clustering.mp4 (121.01 MB)
65 -Clustering kMeans Clustering (Practicals).mp4 (133.4 MB)
66 -Clustering Hierarchical Clustering.mp4 (81.94 MB)
67 -Clustering Hierarchical Clustering (Practicals).mp4 (106.49 MB)
68 -Clustering Mean Shift Clustering.mp4 (73.35 MB)
69 -Feature Engineering Introduction.mp4 (87.4 MB)
7 -Pre-Requisites Train-Test Split.mp4 (115.75 MB)
70 -Feature Engineering RFE and SFS.mp4 (29.05 MB)
71 -Feature Engineering RFE (Practicals).mp4 (190.89 MB)
72 -Feature Engineering Successive Feature Selection.mp4 (180.08 MB)
73 -Feature Engineering Chi-Square.mp4 (31.7 MB)
74 -Feature Engineering Chi-Square (Practicals).mp4 (54.64 MB)
75 -Feature Engineering Principal Component Analysis.mp4 (258.21 MB)
76 -Feature Engineering Principal Component Analysis (Practicals).mp4 (79.7 MB)
77 -Feature Engineering Linear Discriminant Analysis.mp4 (54.25 MB)
78 -Feature Engineering Linear Discriminant Analysis (Practicals).mp4 (84.87 MB)
79 -Feature Engineering kPCA & QDA.mp4 (53.51 MB)
8 -Pre-Requisites Feature Scaling.mp4 (74.24 MB)
80 -Feature Engineering kPCA & QDA (Practicals).mp4 (50.71 MB)
81 -Hyper Parameter Optimization Basics.mp4 (76.02 MB)
82 -Hyper Parameter Optimization Manual HPO.mp4 (31.87 MB)
83 -Hyper Parameter Optimization GridSearch vs RandomizedSearch.mp4 (70.77 MB)
84 -Hyper Parameter Optimization Manual HPO (Practicals).mp4 (164.77 MB)
85 -Hyper Parameter Optimization RandomizedSearchCV (Practicals).mp4 (138.78 MB)
86 -Hyper Parameter Optimization GridSearchCV (Practicals).mp4 (60.41 MB)
9 -Pre-Requisites Standardization Example.mp4 (22.66 MB)
1 -Introduction to TSA.mp4 (23.29 MB)
10 -Testing TS Stationarity.mp4 (43.64 MB)
11 -Transformation.mp4 (21.42 MB)
12 -Introduction to Pre-Processing.mp4 (17.81 MB)
13 -Handle Missing Value.mp4 (58.55 MB)
14 -Handle Missing Value (Practicals).mp4 (92.12 MB)
15 -Outlier Treatment.mp4 (59.77 MB)
16 -3-Sigma Technique.mp4 (102.73 MB)
17 -Feature Scaling.mp4 (74.24 MB)
18 -Feature Scaling Standardization.mp4 (22.67 MB)
19 -Feature Scaling Normalization.mp4 (13.95 MB)
2 -Time Series vs Regression.mp4 (77.14 MB)
20 -Feature Scaling (Practicals).mp4 (111.6 MB)
21 -Feature Encoding.mp4 (83.77 MB)
22 -Feature Encoding (Practicals).mp4 (78.85 MB)
23 -Models - Algorithms.mp4 (5.79 MB)
24 -Models - ARIMA Part 1.mp4 (11.35 MB)
25 -Models - ARIMA Part 2.mp4 (32.11 MB)
26 -Models - AR Theory.mp4 (41.67 MB)
27 -Models - MA Theory.mp4 (46 MB)
28 -Models - ACFPACF Plots.mp4 (45.36 MB)
29 -Models - Find p,d,q in ARIMA.mp4 (11.88 MB)
3 -Time Series Analysis.mp4 (14.58 MB)
30 -Models - ARIMA (Practicals Part 1).mp4 (90.9 MB)
31 -Models - ARIMA (Practicals Part 2).mp4 (85.74 MB)
32 -Models - ARIMA (Final).mp4 (70.73 MB)
33 -Models - Decomposition.mp4 (31.81 MB)
34 -Models - ACFPACF.mp4 (21.56 MB)
35 -Models - Best Transformation.mp4 (72.32 MB)
36 -Models - Grid Search (Part 1).mp4 (90.15 MB)
37 -Models - Grid Search (Part 2).mp4 (16.93 MB)
38 -Models - Final Model Building.mp4 (83.57 MB)
39 -Models - Facebook Prophet (Part 1).mp4 (52 MB)
4 -Anomaly Detection.mp4 (29.89 MB)
40 -Models - Facebook Prophet (Part 2).mp4 (84.67 MB)
41 -Models - Facebook Prophet (Part 3).mp4 (51.95 MB)
42 -Models - Multi Variate Time Series Analysis.mp4 (42.59 MB)
43 -Models - Facebook Prophet (Uni vs Multi).mp4 (118.2 MB)
44 -Introduction to Metrics.mp4 (29.98 MB)
45 -Forecasting Evaluation Metrics.mp4 (6.72 MB)
46 -Mean Squarred Error.mp4 (7.04 MB)
47 -Root Mean Squarred Error.mp4 (7.1 MB)
48 -Mean Absolute Percentage Error.mp4 (16.31 MB)
49 -Project 1 - Energy Forecasting Part 1.mp4 (25.59 MB)
5 -Components of Time Series.mp4 (46.34 MB)
50 -Project 1 - Energy Forecasting Part 2.mp4 (53.23 MB)
51 -Project 1 - Energy Forecasting Part 3.mp4 (77.66 MB)
52 -Project 2 - Stock Market Prediction Part 1.mp4 (30.64 MB)
53 -Project 2 - Stock Market Prediction Part 2.mp4 (37.68 MB)
54 -Project 2 - Stock Market Prediction Part 3.mp4 (152.52 MB)
55 -Project 3 - Demand Forecasting Part 1.mp4 (24.07 MB)
56 -Project 3 - Demand Forecasting Part 2.mp4 (113.42 MB)
57 -Project 3 - Demand Forecasting Part 3.mp4 (94.14 MB)
58 -Project 3 - Demand Forecasting Part 4.mp4 (10.83 MB)
59 -Project 3 - Demand Forecasting Part 5.mp4 (141.06 MB)
6 -Decomposition.mp4 (6.46 MB)
60 -Project 3 - Demand Forecasting Part 6.mp4 (79.8 MB)
7 -Decomposition (Practicals).mp4 (46.48 MB)
8 -AdditiveMultiplicative Decomposition.mp4 (38.94 MB)
9 -Stationarity.mp4 (28.53 MB)
1 -Introduction to Deep Learning.mp4 (10.62 MB)
10 -Backpropagation & Forward Pass.mp4 (212.37 MB)
11 -Gradient Descent.mp4 (107.42 MB)
12 -Artificial Neural Networks Intuition.mp4 (28.24 MB)
13 -Artificial Neural Networks Practicals.mp4 (140.78 MB)
14 -Artificial Neural Networks Hyper Parameter Optimization.mp4 (101.91 MB)
15 -Convolutional Neural Networks What is CNN.mp4 (123.11 MB)
16 -Convolutional Neural Networks Steps in CNN.mp4 (176.23 MB)
17 -Convolutional Neural Networks Architecture Explained.mp4 (253.06 MB)
18 -Convolutional Neural Networks Image Augmentation.mp4 (205.75 MB)
19 -Convolutional Neural Networks Batch size vs iterations vs epochs.mp4 (120.93 MB)
2 -Understanding Deep Learning.mp4 (92.76 MB)
20 -Convolutional Neural Networks Practicals.mp4 (308.33 MB)
21 -Convolutional Neural Networks Model Summary & Parameters.mp4 (113.47 MB)
22 -Convolutional Neural Networks Project (X-Ray detection).mp4 (260.89 MB)
23 -Recurrent Neural Networks Basics.mp4 (35.18 MB)
24 -Recurrent Neural Networks Types of RNN.mp4 (19 MB)
25 -Recurrent Neural Networks Vanishing Gradient & Exploding Gradient Problem.mp4 (94.64 MB)
26 -Recurrent Neural Networks LSTMs.mp4 (36.19 MB)
27 -Recurrent Neural Networks LSTMs (Practicals).mp4 (88.98 MB)
28 -Pre-Trained Models.mp4 (171.97 MB)
29 -Pre-Trained Models (Practicals).mp4 (214.15 MB)
3 -What is a Neuron.mp4 (132.66 MB)
30 -Pre-Trained Models VGG16.mp4 (75.88 MB)
31 -Pre-Trained Models MobileNet.mp4 (46.76 MB)
32 -Transfer Learning.mp4 (39.19 MB)
33 -Project Pneumonia Detection from X-Ray Images.mp4 (124.27 MB)
4 -Activation Functions.mp4 (70.41 MB)
5 -Activation Function Step Function.mp4 (91.35 MB)
6 -Activation Function Linear Function.mp4 (170.98 MB)
7 -Activation Function Sigmoid Function.mp4 (93.51 MB)
8 -Activation Function TanH Function.mp4 (45.91 MB)
9 -Activation Function ReLu Function.mp4 (148.45 MB)
1 -Intro to NLP Introduction.mp4 (59.1 MB)
10 -NLP Basics nGrams.mp4 (52.28 MB)
11 -NLP Basics Vectorization.mp4 (24.37 MB)
12 -NLP Basics Word Embeddings.mp4 (14.51 MB)
13 -NLP Basics Bag of Words.mp4 (50.68 MB)
14 -NLP Basics Bag of Words (Practicals).mp4 (154.6 MB)
15 -NLP Basics TF-IDF.mp4 (68.59 MB)
16 -NLP Basics TF-IDF (Practicals).mp4 (150.4 MB)
17 -NLP Basics Part of Speech Tagging and Named Entity Recognition.mp4 (57.27 MB)
18 -NLP Basics NER (Practicals).mp4 (96.94 MB)
19 -Word Embeddings Word2Vec Introduction.mp4 (23.68 MB)
2 -Intro to NLP Introduction continued.mp4 (46.87 MB)
20 -Word Embeddings Word2Vec Part 2.mp4 (13.59 MB)
21 -Word Embeddings Pre-Trained Word2Vec.mp4 (62.3 MB)
22 -Word Embeddings Word2Vec Intuition.mp4 (37.53 MB)
23 -Word Embeddings Word2Vec - Check X Features.mp4 (65.76 MB)
24 -Word Embeddings Word2Vec CBOW.mp4 (103.25 MB)
25 -Word Embeddings Word2Vec Skip Grams.mp4 (55.89 MB)
26 -Word Embeddings GloVe.mp4 (79.39 MB)
27 -Word Embeddings FastText.mp4 (141.98 MB)
28 -Word Embeddings Cosine Similarity.mp4 (94.96 MB)
29 -Neural Networks LSTMs Part 1.mp4 (73.43 MB)
3 -Intro to NLP Key Challenges.mp4 (67.52 MB)
30 -Neural Networks LSTMs Part 2 (Architecture).mp4 (106.95 MB)
31 -Neural Networks LSTMs Part 3 (Deep Dive).mp4 (28.26 MB)
32 -Neural Networks LSTMs Part 4 (Pointwise Operations).mp4 (34.74 MB)
33 -Neural Networks LSTMs Part 5 (forget gate).mp4 (61.76 MB)
34 -Neural Networks LSTMs Part 6 (inpute gate).mp4 (115.02 MB)
35 -Neural Networks LSTMs Part 7 (output gate).mp4 (49.5 MB)
36 -Neural Networks LSTMs Part 8 (Practicals #1).mp4 (219.76 MB)
37 -Neural Networks LSTMs Part 9 (Practicals #2).mp4 (90.07 MB)
38 -Neural Networks LSTMs Part 10 (Practicals #3).mp4 (112.51 MB)
39 -Neural Networks GRU Part 1.mp4 (19.82 MB)
4 -Intro to NLP Linguistics.mp4 (31.11 MB)
40 -Neural Networks GRU Part 2.mp4 (146.16 MB)
41 -Neural Networks GRU Part 3 (reset gate).mp4 (41.27 MB)
42 -Neural Networks GRU Part 4 (update gate).mp4 (44.6 MB)
43 -Neural Networks GRU Part 5 (Practicals).mp4 (105.53 MB)
44 -Neural Networks Bi-Directional LSTMs.mp4 (116.33 MB)
5 -NLP Basics Case Folding.mp4 (27.96 MB)
6 -NLP Basics SCR.mp4 (89.81 MB)
7 -NLP Basics Handling Contractions.mp4 (64.58 MB)
8 -NLP Basics Tokenization.mp4 (38.86 MB)
9 -NLP Basics Stop Word Removal.mp4 (40.58 MB)

]
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